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    {\Huge\bfseries Web-Based Tool for Observing\\[0.3cm]
    and Analysing Online Communities \par}
    
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    % Author
    {\Large Dylan De Faoite \par}
    
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    {\large April 2026 \par}
    
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    {\large
    Bachelor of Science in Computer Science \\[0.2cm]
    University College Cork \\[0.2cm]
    Supervisor: Paolo Palmieri
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\tableofcontents
\newpage 

\section{Introduction}
This project presents the design and implementation of a web-based analytics engine for the exploration and analysis of online discussion data. Built using \textbf{Flask and Pandas}, and supplemented with \textbf{Natural Language Processing} (NLP) techniques, the system provides an API for extracting structural, temporal, linguistic, and emotional insights from social media posts. A web-based frontend delivers interactive visualizations. The backend architecture implements an analytical pipeline for the data, including data parsing, manipulation and analysis.

\vspace{0.5cm}
Beyond its technical objectives, the system is based on the concepts and ideas of \textbf{digital ethnography} and computational social science. Traditional Ethnography is the practice of studying individual or group culture from the point of view of the subject of the study. Digital ethnography seeks to understand how social relations, topics and norms are constructed in online spaces.

\subsection{Motivation}
There are many beneficiaries of a digital ethnography analytic system: social scientists gain a deeper understanding of contemporary culture and online communities; businesses and marketers can better understand consumer behaviour and online engagement; educators and designers can improve digital learning environments and user experiences; and policymakers can make informed decisions regarding digital platforms, online safety, and community regulation.

\newpage
\section{Background}
\subsection{What is Digital Ethnography?}
\textit{Digital Ethnography} is the study of cultures and interactions in various online spaces, such as forums, posts and video comments. The goal is not only to describe the high-level statistics such as number of posts and posts per day, but also analyse people's behaviour at an interactional and cultural level, delving into common phrases, interactions patterns and common topics and entities.

Compared to traditional ethnography, digital ethnography is usually faster and more cost-effective due to the availability of large swathes of data across social media sites such as Reddit, YouTube, and Facebook and lack of need to travel. Traditional ethnography often relied on in-person interviews and in-person observation of communities. \cite{coleman2010ethnographic}

\subsubsection{Traditional Ethnography}
Ethnography originated in the late nineteenth and early twentieth centuries as a method for understanding cultures through long-term fieldwork. The goal was not just to describe behaviour, but to show how people made sense of that world. Over time, ethnography grew beyond anthropology into sociology, media studies, education, and human computer interaction, becoming a broadly used qualitative research approach. Traditional ethnography was closely tied to physical locations: villages, workplaces or towns. However, as communication technologies developed and social life increasingly took place through technological mediums, it was no longer tied to a physical place.

\subsubsection{Transition to Digital Spaces}
The rise of the internet in the late twentieth century massively changed social interaction. Online forums, emails, SMS and social media platforms became central to human communication. All types of groups and identities were constructed. As a result, ethnographic methods were adapted to study these emerging digital environments. Early work in this area was referred to as "virtual ethnography" or "digital ethnography", where online spaces began to mix and intertwine with traditional cultural spaces.

There are new challenges to overcome in comparison to traditional ethnography. Digital ethnography is distributed across platforms, devices and online-offline interactions. For example, a digital ethnographer studying influencer culture might examine Instagram posts, comment sections, private messages, algorithms, and also conduct interviews or observe offline events. In some ways, digital ethnography is easier than traditional ethnography. Cost is reduced, as there is no need to travel or spend long periods in a field; it's less invasive as there is no need to interact with subjects directly, and there is a much larger amount of data available for analysis. \cite{cook2023ethnography}

\subsection{Online Communities}
There are many different types of online communities, often structured in various ways, with many different types of users, norms and power dynamics. These communities can range from large-scale social networking platforms and discussion forums to niche interests groups. Each type of community fosters different forms of interaction, participation, and identity construction.

Participation within these communities is usually not evenly distributed. The majority of users are passive consumers (lurkers) \cite{sun2014lurkers}, a smaller percentage contribute occasionally, and a very small core group produces most of the content. This uneven contribution structure has significant implications for digital ethnography, as visible discourse may disproportionately reflect the perspectives of highly active members rather than the broader community. This is particularly evident in some reputation-based systems such as Reddit, which allows for the opinions of a few to rise above the rest.

\subsection{Digital Ethnography Metrics}
This section describes common keywords and metrics used to measure and quantify online communities using digital ethnography.

\subsubsection{Active vs Passive Participation}
\label{sec:passive_participation}
Not everyone in an online community participates in the same way. Some users post regularly and leave comments while others might simply read content without ever contributing anything themselves. Some might only contribute occasionally.

This distinction between active and passive participation (passive users are often referred to as "lurkers") is important in digital ethnography, because looking only at posts and comments can give a misleading picture of how large or engaged a community actually is.

This uneven distribution of participation is well documented in the literature. The "90-9-1" principle describes a consistent pattern across many online communities, whereby approximately 90\% of users only consume content, 9\% contribute occasionally, and just 1\% are responsible for the vast majority of content creation \cite{sun2014lurkers}.

\subsubsection{Temporal Activity Patterns}
Looking at when a community is active can reveal quite a lot about its nature and membership. A subreddit that peaks at 2am UTC might have a mostly American userbase, while one that is consistently active across all hours could suggest a more globally distributed community. Beyond timezones, temporal patterns can also capture things like how a community responds to external events, like a sudden spike in posting activity often corresponds to something newsworthy happening that is relevant to the community.

\subsubsection{Cultural Markers}
\label{sec:cultural_markers}
Cultural markers are the words, phrases, memes, and behaviours that are specific to a particular community and signal that someone is a member of it. These might include in-jokes, niche slang, recurring references, or even particular ways of formatting posts. In the context of digital ethnography, identifying these markers is useful because they reveal how communities build a shared identity and distinguish themselves from outsiders.

Some patterns, such as usage of words like "we, us, our, ourselves", where posts are referring to themselves as a community might have different sentiment to posts where words like "they, them, their, themselves" are used. These are known as "identity markers" and they can be used to identify how welcoming a community might be to outsiders.

\subsubsection{Stance Markers}
\label{sec:stance_markers}
Stance Markers refer to the usage of different phrasing patterns which can reveal the speakers attitude towards topics. There are different kinds of these phrasings, such as hedge, certainty, deontic and permission patterns.

Hedge and certainty markers are discussed in this article \cite{shen2021stance}.

\textbf{Hedge Patterns} are usually phrases that contain words like "maybe, possibly, probably, i think, i feel" and generally mean that someone is unsure or suspicious about something.

\textbf{Certainty Patterns} contain phrases like "definitely, certainly, clearly, obviously" and as the name suggests, imply certainty or assuredness.

\textbf{Deontic Patterns} contains phrases that imply obligation, such as "must, should, need, have to". In the context of online communities, these patterns are often used to assert authority or to reinforce communal norms and "unwritten rules."

\textbf{Permission Patterns} refer to phrases where someone is asking permission, like "can, allowed, ok, permitted". These patterns could serve as an indicator of a user's status within an online community.

\subsection{Natural Language Processing}
\textbf{Natural Language Processing} is a branch of artificial intelligence that allows machines to interpret, analyse and generate human language. The aim of NLP models is not only to understand individual words, but to understand them in context.

NLP can carry out many different types of tasks, such as classifying sentences or paragraphs, generating text content, extracting answers from text or even speech recognition in audio. However, even with the advances in NLP models, many challenges and limitations remain. These include understanding ambiguity, cultural context, sarcasm, and humour.

\subsubsection{Why Natural Language Processing?}
Digital ethnography traditionally relied on manual reading of texts and interviews. These approaches are valuable for deep interpretive analysis, but they do not scale well to the volume of data generated in online communities. A single subreddit might contain hundreds of thousands of posts and comments, far beyond what any single researcher could read and analyse by hand.

NLP techniques can be used to automatically process and analyse large volumes and applying ethnographic methods at scale. For example, NLP can be used to identify common themes and topics in a subreddit, track how these themes evolve over time, and even detect the emotional tone of discussions. This allows researchers to gain insights into the dynamics of online communities that would be impossible to achieve through manual analysis alone.

\subsubsection{Sentiment Analysis}
\textbf{Sentiment Analysis} involves determining the emotional tone behind a piece of text. It is commonly used to classify text as positive, negative, or neutral. More advanced sentiment analysis models can detect nuanced emotions, such as frustration, satisfaction, or sarcasm, although accurately identifying these emotions remains a challenge \cite{giuffre2026sentiment}. For ethnographic analysis, sentiment analysis can provide insights into the emotional dynamics of a community, such as how users feel about certain topics or how the overall mood of discussions changes over time.

\subsubsection{Named Entity Recognition}
\textbf{Named Entity Recognition (NER)} is the process of identifying and classifying key entities within a text into predefined categories like names of people, organisations, locations, or dates. NER is essential for structuring unstructured text data and is often used in information extraction, search engines, and question-answering systems. Despite its usefulness, NER can struggle with ambiguous entities or context-dependent meanings.

\subsubsection{Topic Modelling}
\textbf{Topic Modelling} is a technique used in NLP to identify the main themes or topics present in a collection of text. Instead of analysing each sentence individually, topic modelling looks for patterns of words that frequently appear together, allowing it to group documents based on similar themes.

This method is often used to organise lots of unstructured data, such as news articles, research papers, or social media posts.

\subsubsection{Stop Words}
\textbf{Stop Words} are common words that are often filtered out in NLP tasks because they carry little meaningful information. Examples of stop words include "the", "is", "in", "and", etc. Removing stop words can help improve the performance of NLP models by reducing noise and focusing on more informative words. However, the choice of stop words can vary depending on the context and the specific task at hand.

For example, in a Cork-specific dataset, words like "ah", or "grand" might be considered stop words, as they are commonly used in everyday speech but do not carry significant meaning for analysis. \cite{mungalpara2022stemming}

\subsection{Limits of NLP}
\label{sec:nlp_limitations}
While computational methods enable large-scale observation and analysis of online communities, there are many limitations that must be acknowledged. Many limitations come from NLP techniques and the practical boundaries of computational resources.

One key limitation is how the models will likely find it difficult to interpret context-dependent language. Online communities will often use sarcasm, irony or culturally specific references, all of which will be challenging for NLP models to correctly interpret. For example, a sarcastic comment might be incorrectly classified as positive, despite conveying negativity.

Emojis and emoticons are a common feature of online communication and can carry significant emotional meaning. However, NLP models may struggle to accurately interpret the sentiment conveyed by emojis, especially when they are used in combination with text or in a sarcastic manner. \cite{ahmad2024sentiment}

In addition, the simplification of complex human interactions and emotions into discrete categories like "happy" or "sad" will more than likely overlook some nuance and ambiguity, even if the model is not inherently "wrong". As a result, the outputs of NLP models should be interpreted as indicative patterns rather than definitive representations of user meaning.

\subsubsection{Computational Constraints}
The performance and speed of the system will be influenced by the computational resources available during development and execution. While the system will attempt to use GPU acceleration during NLP inference, these resource may not always be available or may not be particularly strong should they exist.

\subsection{Cork Dataset}
\label{sec:cork_dataset}
The Cork dataset serves as the foundation for this project, providing a geographically and culturally grounded corpus for analysis. Rather than examining a globally distributed or topic-neutral community, the dataset centres on a single city with Cork, Ireland which allows the system's analytical outputs to be interpreted against a known social and cultural context.

The dataset is drawn from four distinct online platforms, each of which represents a structurally different mode of online community participation:

\begin{itemize}
    \item \textbf{r/Cork} — a dedicated subreddit for Cork residents and diaspora, characterised by threaded discussion, upvote-based content curation, and an established community identity with its own norms and recurring topics.
    \item \textbf{r/Ireland} (Cork-filtered) — the broader Irish national subreddit, filtered by Cork-related keywords, capturing how Cork is discussed within a wider national discourse rather than within its own community space.
    \item \textbf{YouTube} — video comments retrieved via Cork-related search queries, representing a flatter, less threaded interaction model and a potentially more casual or emotionally expressive register than forum-style platforms.
    \item \textbf{Boards.ie Cork section} — an older Irish forum platform with a distinct demographic profile and lower volume compared to Reddit, providing a counterpoint to the Reddit-dominant data and representing a longer-established form of Irish online community.
\end{itemize}

Reddit's hierarchical comment threading enables deep conversational analysis and reply-chain metrics \cite{medvedev2019anatomy}, whereas YouTube comments are largely flat and unthreaded. Boards.ie occupies a middle ground, with linear threads but a more intimate community character. Taken together, the four sources offer variation in interaction structure, community age, demographic composition, and linguistic register, all of which are factors that the system's analytical modules are designed to detect and compare.

Due to data being collected across multiple platforms, they must be normalised into a single data model. Posts, comments, and metadata fields differ in schema and semantics across sources. A core design requirement of the system is the normalisation of these inputs into a unified event-based internal representation, allowing the same analytical pipeline to operate uniformly regardless of the source.

\newpage
\section{Analysis}
\subsection{Goals \& Objectives}
The objective of this project is to provide a tool that can assist social scientists, digital ethnographers, and researchers to observe and interpret online communities and the interactions between them. Rather than replacing the study of digital ethnography or the related fields, this tool aims to aid researchers in analysing communities.

Specifically, the system aims to: 

\begin{itemize}
    \item \textbf{Enable scalable observation}: Provide researchers with the ability to process and explore large volumes of online discussion data that would be impractical to analyse manually.

    \item \textbf{Support mixed-method research}: Bridge quantitative computational analysis and qualitative ethnographic interpretation by presenting statistically derived insights that can inform deeper contextual study.

    \item \textbf{Reveal structural dynamics}: Expose interaction patterns such as reply networks, participation inequality, conversation depth, and influential contributors within a community.

    \item \textbf{Identify thematic structures}: Detect dominant topics, recurring phrases, and emerging themes using Natural Language Processing techniques.

    \item \textbf{Track emotional and cultural signals}: Analyse sentiment and emotion distributions across posts, users, and topics to better understand the affective tone of discussions and how it evolves over time.

    \item \textbf{Promote ethical data practices}: Restrict analysis to publicly available data, provide opt-out mechanisms for computationally intensive processing, and ensure responsible handling of user-generated content.
\end{itemize}

Ultimately, the project seeks to demonstrate how computational systems can aid and augment social scientists and digital ethnographers toolkits.

\subsection{Feasibility Analysis}
\subsubsection{NLP Limitations}
Online communities often use sarcasm, irony or context-specific references, all of which will be challenging for NLP models, especially weaker ones, to correctly interpret. In a Cork-specific dataset, this will be especially apparent due to the use of regional slang or informal grammar. 

Therefore, the outputs of the model for any single event should not be considered as definitive, but rather as an indicative pattern that is more likely to be correct when aggregated across the entire dataset. For example, while a single comment about a specific topic might be misclassified as positive, the overall sentiment of that topic across thousands of comments is more likely to reflect the true emotional tone of the community.

To account for NLP limitations, the system will:
\begin{itemize}
    \item Rely on \textbf{aggregated results} rather than individual classifications.
    \item Provide \textbf{context for outputs}, such as confidence scores where available.
    \item Allow \textbf{access to original text} behind each NLP result.
\end{itemize}

Overall, while NLP provides powerful tools for analysing large datasets, its limitations must be acknowledged and mitigated through careful design and interpretation of results.

\subsubsection{Data Normalisation}
Different social media platforms will produce data in many different formats. For example, Reddit data will have a much different reply structure to a forum-based platform like Boards.ie where there are no nested replies. Therefore, a core design requirement of the system is to normalise all incoming data into a single unified internal data model. This allows the same analytical functions to be applied across all data sources, regardless of their original structure.

Both comments and posts represent user-generated content that contributes to the community discourse. Therefore, the system will normalise all posts and comments into a single "event" data model, which will allow the same analytical functions to be applied uniformly across all content. This also simplifies the data model and reduces the complexity of the analytical pipeline, since there is no need to maintain separate processing paths for posts and comments.

Though separate processing paths are not needed, the system will still retain metadata that indicates whether an event was originally a post or a comment, as well as any relevant structural information (e.g., parent-child relationships in Reddit threads).

\subsubsection{Scalability Constraints}
This system should be scalable enough to handle large datasets, but there are practical limits to how much data can be processed within reasonable timeframes, especially given the computational demands of NLP models.

Some of the data can be precomputed during the data ingestion phases, such as datetime column derivations and NLP outputs, which can speed up and make queries more efficient. However, the initial processing time of large datasets will still be significant, especially if the dataset contains hundreds of thousands of posts and comments.

To mitigate this, the system will:
\begin{itemize}
    \item Utilise GPU acceleration where available for NLP inference.
    \item Pre-compute some analytical results during data ingestion to speed up subsequent queries.
    \item Implement asynchronous processing for long-running tasks.
\end{itemize}

Overall, while the system is designed to be scalable, it is important to set realistic expectations regarding performance and processing times, especially for very large datasets.

\subsection{Ethics}
The system will process only publicly available data, and will not attempt to access private or restricted content.

\subsubsection{Automated Data Collection}
The system will provide an option for users to automatically fetch datasets from social media sites filtered for keywords or categories. Therefore, it's important to ensure that this data collection is done ethically.

The system will:
\begin{itemize}
    \item Respect rate limits by implementing an exponential backoff strategy for API requests.
    \item Only collect data that is publicly available and does not require authentication or violate platform terms of service.
    \item Provide user-agent headers that identify the system and its purposes.
    \item Allow users the option to upload their own datasets instead of automated collection.
    \item For websites without an API, the \texttt{robots.txt} file will be examined to ensure compliance with platform guidelines.
\end{itemize}

Some platforms provide APIs that allow for easy and ethical data collection, such as YouTube and Reddit. These APIs have clear guidelines and rate limits that the system will adhere to. \cite{chugani2025ethicalscraping}

\paragraph{Reddit (API)}
Reddit provides a public API that allows for the retrieval of posts, comments, and metadata from subreddits. The system will use the official Reddit API with proper authentication via OAuth2 and access tokens. 

In November 2025, Reddit introduced a new approval process for API access, which requires developers to apply for access and specify their intended use case. While the public unauthenticated endpoints are still accessible, they have far stricter rate limits (100 requests every 10 minutes) compared to authenticated access (100 requests per minute). Therefore, the system shall allow for authenticated access to the Reddit API to speed up data retrieval.

Unauthenticated access will still be available as a fallback if client credentials are not provided on the backend, but this will massively slow the data retrieval process, and this will still only fetch public posts and comments.

From Reddit, the system will collect posts, comments and all replies to comments, as well as metadata such as the author name and timestamp.

\paragraph{Boards.ie (Web Scraping)}
Boards.ie is an Irish discussion forum with no public API, so the system will use web scraping instead. The platforms \texttt{robots.txt} will be used to ensure compliance with the site's guidelines for automated access. The Boards.ie \texttt{robots.txt} file contains the following information:

\begin{verbatim}
Sitemap: https://www.boards.ie/sitemapindex.xml
User-agent: *
Disallow: /entry/
Disallow: /messages/
Disallow: /profile/comments/
Disallow: /profile/discussions/
Disallow: /search/
Disallow: /sso/
Disallow: /sso
\end{verbatim}

Public discussion threads are allowed to be automatically crawled, while user profiles, private messages, and authentication endpoints are not allowed. The system will respect these boundaries and will not attempt to access any restricted path.

\paragraph{YouTube (Data API v3)}
YouTube is supported via the official YouTube Data API v3, provided by Google. The API exposes structured endpoints for querying videos, comments, channels, and playlists, making it well-suited for collecting public discourse around specific topics or keywords.

Authentication is handled through an API key issued via the Google Cloud Console. The API enforces a quota system rather than a traditional rate limit: each project is allocated 10,000 quota units per day by default, with different operations consuming different amounts.

In addition, comment retrieval can be disabled by the video uploader, so the system will handle this case by skipping videos where comments are not accessible.

\subsubsection{Data Storage \& Retention}
All data fetched from social media sites are stored locally in a PostgreSQL database. The system will not share or expose any of this data to third parties beyond the users of this application. Raw API responses are discarded once the relevant information is extracted. 

All datasets are associated with one and only one user account, and the users themselves are responsible for uploading or fetching the data, analysing the data, and deleting the data when they are done. The system will not retain any data beyond what is necessary for the end-user to carry out their analysis, and users will have the option to delete their datasets at any time.

The system will not store any personally identifiable information except for what is necessary for the analysis, which includes only usernames and timestamps. The system will not attempt to de-anonymise content creators or link data across platforms.

\subsection{Requirements}
The following requirements are derived from the backend architecture, NLP processing pipeline, and the React-based frontend interface.

\subsubsection{Functional Requirements}
\paragraph{Data Ingestion and Preparation}
\begin{itemize}
    \item The system shall accept social media data in \texttt{.jsonl} format containing posts and nested comments.
    \item The system shall validate uploaded files and return structured error responses for invalid formats or malformed data.
    \item The system shall normalise posts and comments into a unified event-based dataset.
    \item The system shall give the user the option to automatically fetch datasets from social media sites filtered for specific keywords or categories.
\end{itemize}

\paragraph{Dataset Management}
\begin{itemize}
    \item The system shall utilise Natural Language Processing models to generate analytical outputs such as sentiment analysis, topic modelling, and named entity recognition.
    \item The system shall return detailed endpoints that return calculated statistics grouped into themes.
\end{itemize}

\paragraph{Filtering and Search}
\begin{itemize}
    \item The system shall support keyword-based, date-based and source-based filtering of the dataset.
    \item The system shall allow multiple filters to be applied simultaneously.
\end{itemize}

\paragraph{Ethnographic Analysis}
\begin{itemize}
    \item The system shall provide endpoints for structural analysis, temporal analysis, linguistic analysis, and emotional analysis.
    \item The system shall allow users to define custom topics for topic modelling and analysis.
    \item The system shall return outputs that are suitable for visualisation in the frontend.
\end{itemize}


\paragraph{Frontend}
\begin{itemize}
    \item The system shall provide a frontend UI to accommodate all of the above functions
    \item The system shall provide a tab for each endpoint in the frontend
    \item The system shall provide a simple user-friendly interface for uploading and viewing analytics and visualisations.
\end{itemize}

\subsubsection{Non-Functional Requirements}

\paragraph{Performance}
\begin{itemize}
    \item The system shall utilise GPU acceleration where available for NLP.
    \item The system shall utilise existing React libraries for visualisations.
\end{itemize}

\paragraph{Scalability}
\begin{itemize}
    \item The system shall utilise cookies and session tracking for multi-user support.
    \item NLP models shall be cached to prevent redundant loading.
\end{itemize}

\newpage
\section{Design}
\subsection{System Architecture}
\begin{figure}[h]
    \centering
    \includegraphics[width=1.0\textwidth]{img/architecture.png}
    \caption{System Architecture Diagram}
    \label{fig:architecture}
\end{figure}

\subsection{Client-Server Architecture}
The system will follow a client-server architecture, with a Flask-based backend API and a React-based frontend interface. The backend will handle data processing, NLP analysis, and database interactions, while the frontend will provide an interactive user interface for data exploration and visualization.

The reasoning behind this architecture is that it allows the analytics to be aggregated and computed on the server side using Pandas, which is much faster than doing it on the client frontend. The frontend will focus on rendering and visualising the data.

\subsubsection{API Design}
The Flask backend will expose a RESTful API with endpoints for dataset management, authentication and user management, and analytical queries. Flask will call on backend components for data parsing, normalisation, NLP processing and database interfacing. 

Flask was chosen for its simplicity and speed of development. It also has many extensions that can be used for authentication (Flask-Bcrypt, Flask-Login).

The API is separated into three separate groups, \textbf{authentication}, \textbf{dataset management} and \textbf{analysis}. 

\subsubsection{React Frontend}
React was chosen for the frontend due to its massive library of pre-built components with efficient rendering capabilities and ability to display many different types of data. The frontend will be structured around a tabbed interface, with each tab corresponding to a different analytical endpoint (e.g., temporal analysis, linguistic analysis, emotional analysis). Each tab will fetch data from the backend API and render it using appropriate visualisation libraries (react-wordcloud for word clouds, react-chartjs-2 for charts, etc.). The frontend will also include controls for filtering the dataset based on keywords, date ranges, and data sources.

\subsection{Data Pipeline}
As this project is focused on the collection and analysis of online community data, the primary component that must be well-designed is the data pipeline, which encompasses the processes of data ingestion, normalisation, enrichment, storage, and retrieval for analysis.

A unified data model is used to represent all incoming data, regardless of its original source or structure. This ensures that the same pipeline works across YouTube, Reddit and Boards.ie data, and can be easily extended to new sources in the future.

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/pipeline.png}
    \caption{Data Pipeline Diagram}
    \label{fig:pipeline}
\end{figure}

\subsubsection{Data Ingestion}
The system will support two methods of data ingestion:
\begin{itemize}
    \item \textbf{File Upload}: Users can upload datasets in a specified \texttt{.jsonl} format, which contains posts and nested comments.
    \item \textbf{Automated Fetching}: Users can trigger the system to automatically fetch data from supported social media platforms using specified keywords or filters.
\end{itemize}

Originally, only file upload was supported, but the goal of the platform is to aid researchers with ethnographic analysis, and many researchers will not have the technical expertise to fetch data from social media APIs or scrape websites. Therefore, the system was designed to support automated fetching of data from social media platforms, which allows users to easily obtain datasets without needing to manually collect and format data themselves.

In addition to social media posts, users can upload a list of topics that they want to track in the dataset. Custom topic lists allow the system to generate custom topic analysis based on user-defined topics, which can be more relevant and insightful for specific research questions. For example, a researcher studying discussions around local politics in Cork might upload a list of political parties, politicians, and policy issues as topics to track.

If a custom topic list is not provided by the user, a pre-defined generalised topic list that is designed to capture common themes across a wide range of online communities is used.

Each method of ingestion will format the raw data into a standardised structure, where each post will be represented as a "Post" object and each comment will be represented as a "Comment" object.

\subsubsection{Data Normalisation}
After a dataset is ingested, all posts and nested comments are normalised into a single unified "event" data model. Therefore, both posts and comments will be represented as the same type of object, with a common set of fields that capture the relevant information for analysis. 

The decision to normalise posts and comments into a single "event" data model allows the same analytical functions to be applied uniformly across all content, regardless of whether it was originally a post or a comment. This simplifies the data model and reduces the complexity of the analytical pipeline, since there is no need to maintain separate processing paths for posts and comments.

As part of this normalisation process, the dataset is also \textbf{flattened}, so rather than comments being nested within their parent posts as they are in the raw source data, all events are stored as a flat sequence of records. The relationships between posts and comments are preserved through the \texttt{parent\_id} and \texttt{reply\_to} fields. This allows for more efficient querying and analysis of the data, as well as simplifying the data model.

Overall, the data normalisation process unifies the structure of the dataset, and flattens the data into a format that makes analysis more efficient and easier.

\subsubsection{Data Enrichment}
After normalisation, the dataset is enriched with additional derived fields and NLP outputs. This includes:
\begin{itemize}
    \item \textbf{Datetime Derivations}: Fields such as day of week, hour of day, and week of year are derived from the raw timestamp and stored alongside the event, so they do not need to be recomputed on every query.
    \item \textbf{NLP Analysis}: The text content of each event is processed through NLP models to generate outputs such as emotion classification, topic classification, and named entity recognition.
\end{itemize}

NLP processing lets us perform much richer analysis of the dataset, as it provides additional layers of information beyond just the raw text content. After enrichment, the dataset is ready to be stored in the database and made available for analysis through the API endpoints.

\subsubsection{Data Storage}
The enriched dataset is stored in a PostgreSQL database, with a schema similar to the unified data model defined in the normalisation section, with additional fields for the derived data, NLP outputs, and user ownership. Each dataset is associated with a specific user account, and the system supports multiple datasets per user.

\begin{figure}[!h]
    \centering
    \includegraphics[width=1.0\textwidth]{img/schema.png}
    \caption{System Schema}
    \label{fig:schema}
\end{figure}

\subsubsection{Data Retrieval}
The stored dataset can then be retrieved through the Flask API endpoints for analysis. The API supports filtering by keywords and date ranges, as well as grouping and aggregation for various analytical outputs.

\subsection{Natural Language Processing}
The system will utilise pre-trained NLP models to perform emotion classification, topic classification, and named entity recognition on the text content of each event. These NLP outputs will be stored in the database alongside the raw content, allowing for efficient retrieval and analysis without needing to re-run the models on every query.

These will be implemented in a separate module that will be called during the data enrichment phase of the pipeline. The specific models used for each task will be selected based on their performance and suitability for the type of text data being analysed. This module will be called by the Celery worker during the data enrichment phase, and the outputs will be stored in the database for later retrieval.

\subsubsection{Emotional Classification}
Emotional Classification will be the bedrock of the ethnographic analysis, as it provides insight into the emotions of a community and how they relate to different topics and users. As mentioned in the feasibility analysis, the outputs of the emotion classification model should be interpreted as indicative patterns rather than definitive representations of user meaning, due to the limitations of NLP models.

Usage of simple VADER-based models is usually too-simplistic for the type of text data being analysed. Classifying posts into positive, negative and neutral categories is not nuanced enough to truly capture the emotional tone of a community. Therefore, the system will use a more complex model that can classify text into a wider range of emotions, which will allow for richer emotional analysis of the community.

\subsubsection{Topic Classification}
The system will support both a generalised topic classification model that can classify posts into a set of pre-defined general topics, as well as a custom topic classification model that can classify posts into user-defined topics based on a list of topics and descriptions provided by the user.

Initially, the system generated common themes and topics from the dataset by extracting common keywords and phrases, and then use these to generate a topic list. However, this approach was noisy and topics were often singular random words that did not have any overlap with each other, making topic classification less insightful. Therefore, custom user-provided or pre-defined topic lists will instead be used.

\subsubsection{Named Entity Recognition}
In combination with emotional classification, with NER we can see the general sentiment around specific places and people in a community, which can be very insightful for ethnographic analysis. For example, in a Cork-specific dataset, we might see that the city centre is often mentioned with negative emotions due to traffic and parking issues, while local parks are mentioned with positive emotions.

\subsection{Ethnographic Analysis}
The main goal of this project is to provide a tool that can assist researchers with ethnographic analysis of online communities. Therefore, ethnographic analysis will be a core component of the system.

Ethnographic analysis can be carried out from many different perspectives, such as the perspective of a single user or the community as a whole. The system is designed to support both of these perspectives, as well as the ability to zoom in and out between them. For example, a researcher might want to look at the overall emotional tone of a community, but then zoom in to see how a specific user contributes to that tone.

The system is designed to support multiple types of analysis, such as:
\begin{itemize}
    \item \textbf{Temporal Analysis}: looking at when a community is active and how that activity changes over time.
    \item \textbf{Linguistic Analysis}: looking at the words and phrases that are commonly used in a community, and how they relate to identity and culture.
    \item \textbf{Emotional Analysis}: looking at the emotional tone of a community, and how it varies across different topics or users.
    \item \textbf{User Analysis}: looking at the behaviour and activity of individual users, and how they contribute to the community.
    \item \textbf{Interaction Analysis}: looking at how users interact with each other, such as who replies to whom and how conversations develop.
    \item \textbf{Cultural Analysis}: looking at the cultural markers and identity signals that are present in a community, such as slang, memes, and recurring references.
\end{itemize}

All types of analysis are available at different API endpoints for any given dataset, and the frontend is designed to allow users to easily switch between them and explore the data from different angles.

Some types of analysis that involve inspecting the content of the posts themselves. The content will be split into tokens and stop words will be stripped from them, as these common words would not provide meaningful insight for the analysis.

\subsubsection{Temporal Analysis}
Temporal analysis allows researchers to understand what a community is talking about over time, and how the emotional tone of the community changes over time. For example, a researcher might want to see how discussions around a specific topic evolve over time, or how the emotional tone of a community changes in response to external events.

However a major limitation of the data captured for this system, whether it's the Cork dataset, or any automatically fetched dataset, it will only stretch at most a few weeks back in time. This is because the system is designed to fetch only the most recent posts and comments from social media platforms, which means that it will not capture historical data beyond a certain point. Therefore, while temporal analysis can still be carried out on the dataset, it will be limited to a relatively short timeframe.

In this system, temporal analysis will be limited to:
\begin{itemize}
    \item Event frequency per day.
    \item Weekday--hour heatmap data representing activity distribution.
\end{itemize}

\textbf{Average reply time per emotion} was considered as a potential temporal analysis metric, but was eventually excluded due to inconsistent and statistically insignificant results that yielded no meaningful analytical insight. 

\subsubsection{Linguistic Analysis}
Linguistic analysis allows researchers to understand the language and words used in a community. For example, a researcher might want to see what words are most commonly used in a community, or how the language used in a community relates to identity and culture.

In this system, linguistic analysis will include:
\begin{itemize}
    \item Word frequency statistics excluding standard and domain-specific stopwords.
    \item Common bi-grams and tri-grams from textual content. \cite{mungalpara2022stemming}
    \item Lexical diversity metrics for the dataset.
\end{itemize}

The word frequencies and n-gram metrics were chosen because they can provide insights into the language and phrases used commonly in an online community, which is important for ethnographic analysis and understanding a community fully. Lexical diversity metrics such as the total number of unique tokens versus the total number of tokens can show if a specific culture often repeats phrases (like memes, slang etc.) or if they often have structured, serious discussion without repeating themselves.

Outlining a list of stopwords is essential for linguistic analysis, as it filters out common words that wouldn't be useful for linguistic analysis. Stop Word lists can be provided by a Python library such as NLTK. In addition to standard stop words, the system also excludes link tokens such as "www", "http", and "https" from the word frequency analysis, as social media users will often include links in their posts and comments, and these tokens can become quite common and skew the word frequency results without adding meaningful insight.

\subsubsection{User Analysis}
User analysis allows researchers to understand the behaviour and activity of individual users within a community. For example, a researcher might want to see who the most active users are in a community, or how different users contribute to the overall emotional tone of the community.

In this system, user analysis will include:
\begin{itemize}
    \item Identification of top users based on activity.
    \item Per-user activity.
\end{itemize}

Initially the user endpoint contained the interactional statistics as well, as a case could be made for the user analysis and interaction analysis being combined, however a distinction can be made between individual user analysis and user analysis on a larger, community-level scale focused on interactions. This allows the user endpoint to stay focused on singular user analysis while still using NLP outputs like emotions and topics.

Identifying top users allows us to see the most active and prolific posters in a community, which might often be site-specific bots that comment on every post or deleted users, which often show up as simply "[Deleted User]" and can aggregate together in statistics . An example might be a User Moderator bot on Reddit, seen below.

\begin{figure}[!h]
    \centering
    \includegraphics[width=0.75\textwidth]{img/reddit_bot.png}
    \caption{An AutoModerator Bot on r/politics}
    \label{fig:bot}
\end{figure}

While it's impossible to filter out all of these bots, deleted users can simply be filtered out using an exclusion list. 

\subsubsection{Interactional Analysis}
Instead of per-user analysis, interactional analysis looks at the interactions between users, such as who replies to whom and who is contributing the most to the conversations. 

In this system, interactional analysis will include:
\begin{itemize}
    \item Top interaction pairs between users.
    \item An interaction graph based on user relationships.
    \item Conversation concentration metrics such as who is contributing the most to the conversations and how much of the conversation is dominated by a small number of users.
\end{itemize}

For simplicity, an interaction is defined as a reply from one user to another, which can be either a comment replying to a post or a comment replying to another comment. The system will not attempt to capture more complex interactions such as mentions or indirect references between users, as these would require more advanced NLP techniques. Unfortunately, \texttt{Boards.ie} does not have a reply structure beyond mentions in linear threads, so interactional analysis is limited for that data structure.

\subsubsection{Emotional Analysis}
Emotional analysis allows researchers to understand the emotional tone of a community, and how it varies across different topics and users.

In this system, emotional analysis will include:
\begin{itemize}
    \item Average emotion by topic.
    \item Overall average emotional distribution across the dataset.
    \item Dominant emotion distributions for each event
    \item Average emotion by data source
\end{itemize}

It is emphasised that emotional analysis is inaccurate on an individual post level as the models cannot fully capture the nuance of human interaction and slang. Warnings will be presented to the user in the frontend that AI outputs can possibly be misleading on an individual scale, and accuracy only increases with more posts. NLP limitations are discussed further in Section \ref{sec:nlp_limitations}

\subsubsection{Cultural Analysis}
Cultural analysis allows researchers to understand the cultural markers and identity signals that are present in a community, such as slang, memes, and recurring references. While some of this is covered in the linguistic analysis, cultural analysis will focus more on the identity and stance-related markers that are present in the language of the community.

In this system, cultural analysis will include:
\begin{itemize}
    \item In-Group vs Out-Group phrasing 
    \item Average emotion for in-group vs out-group phrasing
    \item Stance Markers
    \item Average emotions per stance marker type
    \item Average emotions per entity
\end{itemize}

These metrics were chosen because they can provide insights into the cultural markers and identity signals that are present in an online community, further described in Section \ref{sec:cultural_markers} and \ref{sec:stance_markers}.

\subsection{Frontend Design}
The primary audience for this tool is researchers and social scientists, not software developers. Therefore the frontend needs to feel approachable and easy to use for non-technical users. At the same time it must support multi-dataset workflows and handle long-running background processes.

React was chosen as the UI framework primarily for its large amount of pre-built visualisation components and for its component-based architecture. There are many different types of data being visualised in this system, such as word clouds, bar charts, line charts, heatmaps and network graphs, and React has a large library of pre-built components for all of these types of visualisations.

\subsubsection{Structure}
A persistent layout shell will wrap every page of the frontend, providing a consistent header for navigation and account management. This will also store login state and user information in a global way, such that no component has to manage authentication state on its own. The main content area will be reserved for the dataset management and analysis interface.

The frontend will be structured around a tabbed interface, with each tab corresponding to a different analytical endpoint (e.g., temporal analysis, linguistic analysis, emotional analysis). Each tab will fetch data from the backend API and render it using appropriate visualisation libraries. The frontend will also include controls for filtering the dataset based on keywords, date ranges, and data sources.

\subsubsection{Visual Design}
The visual design of the frontend will be clean and minimalistic, with a focus on usability and clarity. The styling files will be centralised to allow for developers to easily change or modify the colouring and palettes in the future. 

\subsection{Automatic Data Collection}
Originally, the system was designed to only support manual dataset uploads, where users would collect their own data from social media platforms and format it into the required \texttt{.jsonl} format. 

However, this approach is time consuming and since this system is designed to aid researchers rather than burden them, the system includes functionality to automatically fetch data from social media platforms. This allows users to easily obtain datasets without needing to manually collect and format data themselves, which is especially beneficial for researchers who may not have technical expertise in data analytics or programming.

The initial system will contain connectors for:
\begin{itemize}
    \item \textbf{Reddit} — using the official Reddit API to fetch posts and comments from specified subreddits or filtered by keywords.
    \item \textbf{YouTube} — using the YouTube Data API v3 to fetch video comments based on search queries.
    \item \textbf{Boards.ie} — using web scraping techniques to collect posts and comments from the Cork section of the Boards.ie forum.
\end{itemize}

\subsubsection{Connector Abstractions}
While the system is designed around a Cork-based dataset, it is intentionally source-agnostic, meaning that additional data sources for data ingestion could be added in the future without changes to the core analytical pipeline.

\textbf{Data Connectors} are components responsible for fetching and normalising data from specific sources. Each connector implements a standard interface for data retrieval.

Creating a base interface for what a connector should look like allows for the easy addition of new data sources in the future. For example, if a new social media platform becomes popular, a new connector can be implemented to fetch data from that platform without needing to modify the existing data pipeline or analytical modules.

The connector registry is designed so that any new connector implementing \texttt{BaseConnector} is automatically discovered and registered at runtime, without requiring changes to any existing code. This allows for a modular and extensible architecture where new data sources can be integrated with minimal effort.

\subsection{Asynchronous Processing}
In addition to NLP, fetching large datasets from sites like Reddit and YouTube takes a lot of time, due to the sequential nature of data fetching and severe rate limits on even authenticated Reddit accounts. To prevent the Flask API from blocking while these tasks are being processed, an asynchronous processing queue will be implemented using \textbf{Redis} and \textbf{Celery}.

\subsubsection{Dataset Enrichment}
A non-normalised dataset will be passed into Celery along with the dataset id and the user id of the dataset owner. At this point, the program is running separately to the main Flask thread. The program then calls on the \textbf{Normalisation \& Enrichment Module} to:
\begin{itemize}
    \item Flatten the dataset from posts with nested comments to unified event data model.
    \item Add derived timestamp columns to aid with temporal analysis
    \item Add topic, emotional and entity NLP analysis as columns
\end{itemize}

\subsubsection{Data Fetching}
If the user triggers a data auto-fetch from any given social media site, a task will be added to the Redis queue. Celery workers will then pop tasks off the Redis queue and process these tasks in the background, which ensures the API to remain responsive to user requests. The specific data connectors are called and the data fetching begins. Once the data has been fetched from all social media sites, NLP processing begins and we are at the same stage as before.

Asynchronous processing is especially important for automatic data-fetching, as particularly large datasets can take hours to fetch.


\subsection{Design Tradeoffs}
\subsubsection{Database vs On-Disk Storage}
Originally, the system was designed to store \texttt{json} datasets on disk and load them into memory for processing. This was simple and time-efficient for early development and testing. However, as the functionality of the system expanded, it become clear that a more persistent and scalable storage solution was needed.

Storing datasets in a database allows for more efficient querying, filtering, and updating of data without needing to reload entire datasets into memory. However the primary benefit of using a database is support for \textbf{ multiple users and multiple datasets per user}.

An additional benefit of using a database was that it allowed the NLP processing to be done once, with the NLP results stored alongside the original data in the database. This meant that the system could avoid redundant NLP processing on the same data, which was a significant performance improvement.

\texttt{PostgreSQL} was chosen as the database solution due to its robustness, support for complex queries, and compatibility with Python through \texttt{psycopg2}. PostgreSQL's support for JSONB fields allows for storage of unstructured NLP outputs, which alternatives like SQLite does not support.

\subsubsection{Unified Data Model vs Split Data Model}
The choice between a \textbf{Unified Data Model} and a \textbf{Split Data Model} led to many swaps in design for the early API. 

A unified data model means both posts and comments are stored in the same data object, with a field to differentiate between the two. A split data model means posts and comments are stored in separate tables, with a foreign key relationship between them.

\paragraph{The Case for a Unified Data Model}
\begin{itemize}
    \item \textbf{Simpler Schema}: One \texttt{events} table rather than split comments and posts tables.
    \item \textbf{Simpler Pipeline}: The same pipeline works for both posts and comments.
    \item \textbf{Differentiation Possible}: Through the \texttt{type} column, we can still differentiate between a post and a comment, though more awkwardly.
\end{itemize}

A unified data model led to a simplification of some of the content, for example a post title is very different from the comment content. Reply chains must be reconstructed using the \texttt{reply\_to} and \texttt{parent\_id} fields and some fields, like \texttt{reply\_to} will be null depending on the data source. For example, \texttt{Boards.ie} does not support nested replies.

\paragraph{The Case for a Split Data Model}
\begin{itemize}
    \item \textbf{Per-Type Analysis}: A post has different attributes to a comment, extending the analysis for post-specific metrics (like title sentiment, title-to-post length ratio) is easier later down the line.
    \item \textbf{Accurate Reply Relationship}: Reply relationships are naturally represented, comments have a foreign key to posts, no reconstruction needed.
\end{itemize}

The decision to \textbf{stick with a unified data model was made} since the downsides of a Unified Model could be mitigated through reconstruction of reply chains using specific fields, and being able to differentiate between a post and a comment using a type field. Largely, in ethnography, a post and a comment are both just a user saying something at a point in time, and even in cases where they might need to be treated differently (reply-chains, interactions graphs), that distinction can still be made using specific fields.


\newpage
\section{Implementation}
In the previous chapter, the architecture of the web-based ethnography tool was
outlined. In this chapter, the details on how this was implemented will be
discussed.

\subsection{Overview}
In the initial stages, the project was a small Python script that would fetch data from Reddit and aggregate simple statistics such as number of posts and a number of comments. Some early features like search and subreddit specific searches were added through hard-coded variables. The Reddit Connector code was extracted into its own \texttt{RedditConnector} module, though the connector abstraction had not yet been formalised.

As this was going to be a web-based tool, the Flask server was then setup. A rudimentary sentiment analysis endpoint was added as an initial test using the VADER Sentiment Python module. An endpoint to fetch from Reddit was added but temporarily shelved. Eventually more analysis endpoints were added, creating the many different analytical perspectives that are available in the final system, such as linguistic analysis and user analysis. 

At this stage, datasets were simply files stored on the machine and loaded into memory globally, which made early development and testing easier, but as the project progressed, the database was added to allow multiple datasets and users. While this was happening, further infrastructure was added to support multiple users, and to fix long-standing issues such as the blocking nature of NLP and data fetching, which was solved through the addition of Redis and Celery for asynchronous processing. Multiple user support was added through the addition of user accounts, with authentication and dataset ownership endpoints.

A very basic frontend was created with React, which was just a simple interface to call the API endpoints and display some basic summary stats such as number of posts, number of comments, and average sentiment. After the initial analysis endpoints were created and the API was fully functional, the frontend was expanded to include the full tabbed interface with visualisations for each analytical perspective.

\subsection{Project Tooling}
The project was developed using the following tools and libraries:
\begin{itemize}
    \item \textbf{Python 3.13} for the backend API and data processing.
    \item \textbf{Flask} for the web server and API development.
    \item \textbf{BeautifulSoup} and \textbf{Requests} for web scraping and HTTP requests.
    \item \textbf{google-api-python-client} for interacting with the YouTube Data API.
    \item \textbf{PostgreSQL} for the database.
    \item \textbf{Redis} and \textbf{Celery} for asynchronous task processing.
    \item \textbf{React} and \textbf{TypeScript} for the frontend interface.
    \item \textbf{Docker} and \textbf{Docker Compose} for containerisation and deployment.
    \item \textbf{Pandas} for data manipulation and analysis.
    \item \textbf{NLTK} for basic stop word lists and tokenisation.
    \item \textbf{Transformers} for NLP models used in emotion classification, topic classification, and named entity recognition.
    \item \textbf{react-chartjs-2} and \textbf{react-wordcloud} for data visualisation in the frontend.
\end{itemize}

Git was used for version control, with regular commits and branches for new features.

\subsection{Social Media Connectors}
The first connectors implemented were the Reddit and Boards.ie connectors, as these were the original data sources for the Cork dataset. The YouTube connector was added later to improve diversity of data sources.

The connectors fetch the newest posts from their specified source, which allows for the collection of the most recent data from the community, which is more relevant for contemporary analysis of communities. This limits long term temporal analysis but allows for more up-to-date analysis of the community.

\subsubsection{Data Transfer Objects}
Data Transfer Objects are simple classes that represent the data structure of a post or comment as it is retrieved from the source platform. They are used to encapsulate the raw data and provide a consistent interface for the rest of the system to interact with, regardless of the source platform.

These are later replaced by the unified "event" data model during the normalisation process, but they are a useful abstraction for the connectors to work with. Two DTOs are defined: \texttt{PostDTO} and \texttt{CommentDTO}, which represent the structure of a post and a comment respectively as they are retrieved from the source platform. The \texttt{PostDTO} will contain a list of \texttt{CommentDTO} objects.

\subsubsection{Reddit Connector}
The initial implementation of the Reddit connector was a simple class that simply used the \texttt{requests} library to fetch data directly from the Reddit API. The connector follows the Reddit API specification \cite{reddit_api}. It uses the \texttt{reddit.com/r/\{subreddit\}/new} endpoint to fetch the most recent posts from a specified subreddit, and the \texttt{reddit.com/r/\{subreddit\}/{post\_id}/comments} endpoint to fetch comments for each post.

It primary method implemented is of this signature:
\begin{Verbatim}[breaklines=true]
def get_new_posts_by_search(self, search: str, category: str, limit: int) -> list[Post]:
\end{Verbatim}

The endpoint returns a maximum of 100 posts per request, so \textbf{pagination} was implemented to allow fetching of more than 100 posts, which is necessary for Reddit datasets of larger than 100 posts. The connector will keep fetching posts until it reaches the specified number of posts to fetch, or until there are no more posts available.

It became apparent that when unauthenticated, the Reddit API has severe rate limits that make fetching large datasets take hours, therefore the connector was updated to support authentication using Reddit API client credentials, which are provided through environment variables. This was done using the \texttt{requests\_oauthlib} library, which provides a convenient way to handle OAuth2 authentication with the Reddit API. With authentication, the rate limits are increased, allowing for faster data fetching.

\subsubsection{YouTube Connector}
The YouTube connector was the simplest out of the three initial connectors, as YouTube provides an official API that is well-documented compared to the Reddit API. The Python library \texttt{google-api-python-client} was used to interact with the YouTube Data API.

Like the Reddit Connector, it implements the \texttt{get\_new\_posts\_by\_search} method, which searches for videos based on a query and then fetches comments for those videos. A limit of 50 posts per query is imposed by the YouTube API, therefore pagination was implemented to allow fetching more than 50 posts.

\subsubsection{Boards.ie Connector}
The Boards.ie connector was the most complex connector to implement, as Boards.ie does not provide an official API for data retrieval, which meant web scraping techniques were utilised to fetch data from the site. The \texttt{requests} library was used to make HTTP requests to the Boards.ie website, and the \texttt{BeautifulSoup} library was used to parse the HTML content and extract the relevant data.

Browser developer tools were used to inspect the HTML structure and to find the relevant HTML elements that contain the post and comment data. \texttt{BeautifulSoup} was then used to extract the correct data from the \texttt{.Message.userContent} tag and the \texttt{.PageTitle} tag, which contain the content and title of the posts. Each comment lived in an \texttt{ItemComment} class. Each of these were collected and iterated through to create the list of \texttt{PostDTO} and \texttt{CommentDTO} objects that represent the data retrieved from the site.

As not all comments on a thread are on one page, pagination was implemented by looking for the "Next" button on the page and following the link to the next page of comments until there are no more pages left. This allows for fetching of all comments for a given post, even if they span multiple pages.

A \texttt{ThreadPoolExecutor} was used to fetch posts in parallel, which improved the performance of the connector significantly, as fetching posts sequentially was very slow due to the need to fetch comments for each post, which often spanned multiple pages. Though there were diminishing returns after a certain number of threads, possibly due to site blocking or internet connection limits. Initially 20 threads were used, but this was later reduced to 5 threads to avoid potential issues with site blocking and to improve ethical considerations around web scraping.

\subsubsection{Connector Plugin System}
The connector plugin system was implemented to allow for easy addition of new data sources in the future. This requires simply implementing a new connector class and dropping it into the connectors directory, without needing to modify any existing code. This was achieved through the use of Python's \texttt{importlib} library, which allows for dynamic importing of modules at runtime.

To achieve this, the base class \texttt{BaseConnector} was defined, which allows a standard interface for all connectors to implement. Each connector implements the \texttt{get\_new\_posts\_by\_search} method, which takes in a search query, a category (which is the subreddit for Reddit, or the category for Boards.ie), and a limit on the number of posts to fetch. The method returns a list of \texttt{PostDTO} objects that represent the data retrieved from the source platform.

In addition, some metadata is required for each connector, such as the source name, search support and category support, which are defined as class variables in each connector. This is required as some connectors may not support search or categories, for example YouTube does not support categories in the same sense that Reddit might.

\subsection{Database Configuration}
A PostgreSQL Docker container was set up to serve as the database for the system. This allows for persistent storage of datasets, as well as support for multiple users and multiple datasets per user. The implemented schema passed into the Docker container by mounting the \texttt{schema.sql} file as a volume, which allows for easy updates to the database schema during development. The database contains three main tables:
\begin{itemize}
    \item \textbf{users}: contains user information such as username, email and password hash.
    \item \textbf{datasets}: contains dataset information such as dataset name, description and owner (foreign key to users table).
    \item \textbf{events}: contains the main data for the posts and comments.
\end{itemize}

\subsubsection{Low-Level Connector}
A low-level \texttt{PostgreConnector} module was implemented to handle the raw SQL queries for interacting with the database. It connects to the Docker container using environment variables for the database credentials, which are passed into the container through the \texttt{docker-compose.yaml} file. The connector provides methods for executing queries with parameters and supports rollback in the case of errors.

 Two main methods of the connector are:
\begin{itemize}
    \item \texttt{def execute(self, query, params=None, fetch=False) -> list}
    \item \texttt{def execute\_batch(self, query, values) -> list}
\end{itemize}

This module provides a simple interface for executing SQL queries. It's used by higher level modules to interact with the database without needing to worry about the details of database connections and query execution.

\subsubsection{Dataset Manager}
The dataset manager is a higher-level module that provides an interface for managing datasets in the database. It uses the low-level \texttt{PostgreConnector} to execute SQL queries, but provides more specific methods for dataset management, such as creating a new dataset, fetching a dataset by id, and updating dataset metadata. Dependency injection is used to pass an instance of the \texttt{PostgreConnector}.

The \texttt{DatasetManager} class is responsible for all database interactions relating to datasets, and draws a deliberate distinction between two categories of data:
\begin{itemize}
    \item \textbf{Dataset metadata} (the \texttt{datasets} table) refers to the metadata about a dataset like its name, owner, topic configuration, and processing status. Methods such as \texttt{save\_dataset\_info()}, \texttt{get\_dataset\_info()}, and \texttt{set\_dataset\_status()} operate on this layer.
    \item \textbf{Dataset content} (the \texttt{events} table) refers to the enriched event rows produced by the pipeline. \texttt{save\_dataset\_content()} performs a batch insert of the full enriched DataFrame, with NER entities serialised to JSONB via \texttt{psycopg2}'s \texttt{Json} wrapper, and emotion scores stored as flat numeric columns to allow direct SQL aggregation without requiring JSON parsing at query time.
\end{itemize}

\texttt{authorize\_user\_dataset()} enforces ownership by comparing the dataset's \texttt{user\_id} against the requesting user before any operation is performed, returning \texttt{False} rather than raising an exception so that the calling route handler can respond with an appropriate HTTP error.

NER output is stored as JSONB rather than in relational columns, as the number of extracted entities per post is arbitrary and varies between posts. Storing this into a fixed column structure would have been awkward and required a schema redesign.

This module is a simple interface to deal with datasets in the database, and abstracts away the details of SQL queries and database interactions from the rest of the application. It is used by the API endpoints to manage datasets and their content.

\subsubsection{Authentication Manager}
\label{sec:auth-manager}
The authentication manager is another higher-level module that provides an interface for managing user authentication in the database. It also uses the low-level \texttt{PostgreConnector} to execute SQL queries, but provides more specific methods for authentication management, such as creating a new user, fetching a user by id, and authenticating a user. It handles password hashing using the \texttt{bcrypt} library, which provides a secure way to hash and verify passwords. Similar to the dataset manager, dependency injection is used to pass an instance of the \texttt{PostgreConnector}.

The most important authentication methods implemented are as follows:
\begin{itemize}
    \item \texttt{register\_user(username: str, email: str, password: str) -> None}: Registers a new user, hashes their password, checks for duplicate usernames or emails, and stores the user in the database.
    \item \texttt{authenticate\_user(username: str, password: str) -> None | dict}: Authenticates a user by verifying the provided password against the stored hash, returning user information if successful or \texttt{None} if authentication fails.
    \item \texttt{get\_user\_by\_id(user\_id: int) -> None | dict}: Fetches a user's information from the database based on their user ID, returning a dictionary of user details if found or \texttt{None} if no such user exists.
\end{itemize}

Defensive programming is used in the authentication manager to handle edge cases like duplicate usernames or emails. This module is a simple interface that the higher level Flask API can call for easy management of user authentication and registration.

\subsection{Data Pipeline}
The data pipeline began with the data connectors mentioned in the previous section, which are responsible for fetching raw data from the source platforms. However they were not initially included as part of the data pipeline, as the initial system was designed to only support manual dataset uploads. The data connectors were used to fetch data for the Cork dataset, which was then uploaded automatically through the API. Once the automatic data fetching functionality was added, the connectors were integrated into the data pipeline.

\subsubsection{Data Enrichment}
The data enrichment process is responsible for taking the raw data retrieved from the connectors and transforming it into a format that is suitable for analysis. This involves several steps, including normalisation, NLP processing, and storage in the database.

Data Normalisation was intended to be a separate step in the data pipeline, but it was later combined with the enrichment process as normalisation is a very small part of the process and can be done in a few lines of code, therefore it was combined with data enrichment. In normalisation, the list of \texttt{Post} objects retrieved from the connectors is flattened into a unified list of "events", which is a Pandas DataFrame that contains both posts and comments in a single table.

The \texttt{enrich()} method is the primary method for dataset enrichment in the module, and provides two main functionalities:
\begin{itemize}
    \item \textbf{Column Derivation}: This involves adding new columns to the dataset that are derived from existing data, such as timestamp parsing to extract date and time components.
    \item \textbf{NLP Analysis}: NLP analysis is performed on the dataset to add new columns that contains NLP outputs. The NLP performed includes emotion classification, topic classification, and named entity recognition.
\end{itemize}

Column derivation is a process of combining or altering existing columns to create new columns useful to analysis. The original dataset contains a timestamp column that might need to be parsed into a datetime format, and then new columns can be derived from this, such as the date, time, weekday, hour of the event, which is needed for temporal analysis like heatmaps. Datetime parsing on it's own is not usually intensive, but multiplied across thousands of posts and comments, it can add up, therefore it's calculated before analysis.

\subsubsection{Data Storage}
Once the dataset is enriched, it is ready for storage. Datasets are stored in a PostgreSQL database. The dataset manager is used to handle the storage of datasets in the database, and it provides a simple interface for saving the enriched dataset content. The enriched dataset is stored in the \texttt{events} table, with each row representing an event (either a post or a comment).

One issue arose using dependency injection for the dataset manager. Since from the data enrichment stage onwards, the data pipeline runs on a separate Celery worker process, therefore dependency injection of non-serialisable objects like \texttt{PostgresConnector} or \texttt{DatasetManager} does not work, as these objects cannot be passed through the Redis queue. To solve this, the \texttt{PostgresConnector} and \texttt{DatasetManager} are instantiated within the Celery worker process itself, rather than being passed in from the Flask API. While this introduces some tight coupling and possible synchronisation issues, these are not issues at this scale of project since both the Celery worker and database module use single-threaded connections to the database, but it's worth noting that this could be an issue if the project scaled up and had multiple Celery workers in parallel.

\subsection{NLP Module}
The NLP module is responsible for adding new columns to the dataset that contain the NLP outputs, three types of NLP analysis are performed: emotion classification, topic classification, and named entity recognition. It is instantiated once per dataset during the enrichment phase and runs on the provided Pandas DataFrame.

\subsubsection{Emotion Classification}
\label{sec:emotion-classification}
For emotional classification, initially a pre-trained VADER sentiment analysis model was used, which provides a very simple way to classify text into positive, negative, and neutral emotions. Though for ethnographic analysis, a more complex emotional model that can capture more nuance is needed, therefore the VADER model was later replaced with a fine-tuned transformer-based model that can classify text into a wider range of emotions. 

GoEMOTION \cite{demszky2020goemotions} was considered as a potential model for emotional classification, as it is extremely nuanced and can capture a wide range of emotions, however it has 27 emotion classes, which was too many for the purposes of this project, as it would have been difficult to visualise and analyse such a large number of emotion classes. 

A middle ground was found with the "Emotion English DistilRoBERTa-base" model from HuggingFace \cite{hartmann2022emotionenglish}, which is a fine-tuned transformer-based model that can classify text into 6 emotion classes: anger, disgust, fear, joy, sadness, neutral and surprise.

As the project progressed and more posts were classified, the "surprise" and "neutral" emotions were found to be dominating the dataset, which made it difficult to analyse the other emotions. This may be because the model is not fine-tuned for internet slang, and usage of exclamation marks and emojis, which are common in social media posts, may be classified as "surprise" or "neutral" rather than the intended emotion. Therefore, the "surprise" and "neutral" emotion classes were removed from the dataset, and the confidence numbers were re-normalised to the remaining 5 emotions.

\subsubsection{Topic Classification}
For topic classification, a zero-shot classification approach was used, which allows for classification of text into arbitrary topic classes without needing to fine-tune a model for each specific set of topics. Initially, attempts were made to automatically generate topic classes based on the most common words in the dataset using TF-IDF, but this led to generic and strange classes that weren't useful for analysis. Therefore, it was decided that a topic list would be provided manually, either by the user or using a generic list of broad common topics. 

Initially, the "all-mpnet-base-v2" \cite{all_mpnet_base_v2} was used as the base model for the zero-shot classification, which is a general-purpose sentence embedding model. While this worked well and produced good results, it was slow to run inference on large datasets, and would often take hours to classify a dataset of over 60,000 posts and comments.

Eventually, the "MiniLM-L6-v2 " \cite{minilm_l6_v2} was chosen as the base model for zero-shot classification, which is a smaller and faster sentence embedding model. While it may not produce quite as good results as the larger model, it still produces good results and is much faster to run inference on, which makes it more practical for use in this project.

\subsubsection{Entity Recognition}
At this point, the NLP pipeline was taking a long time to run on large datasets (such as the Cork dataset), therefore any NER (Named Entity Recognition) model that was added needed to be small and fast to run inference on large datasets. The "dslim/bert-base-NER" model from HuggingFace \cite{dslim_bert_base_ner} was chosen as it is a fine-tuned BERT model that can perform named entity recognition, and is relatively small and fast compared to other NER models.

This model outputs a list of entities for each post, and each entity has a type, which are:
\begin{itemize}
    \item \textbf{PER}: Person
    \item \textbf{ORG}: Organisation
    \item \textbf{LOC}: Location
    \item \textbf{MISC}: Miscellaneous
\end{itemize}

Since the model outputs have a variable length, they are stored in the database as a \texttt{JSONB} field, which allows for flexible storage of the variable number of entities per post.

\subsubsection{Optimization}
Many issues arose with the performance of the NLP module, as running inference on large datasets can take a long time, especially when using transformer-based models. To optimize the performance of the NLP module, several techniques were used:
\begin{itemize}
    \item \textbf{Batch Processing}: Instead of running inference on each post individually, posts are processed in batches.
    \item \textbf{Model Caching}: Models are loaded once and cached in memory, rather than being loaded from disk for each inference.
    \item \textbf{Batch Size Backoff}: If the model runs out of memory during inference, the batch size is automatically reduced and the inference is retried until it succeeds.
\end{itemize}

An example of the batch size backoff implementation is shown in figure \ref{fig:nlp_backoff}.

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/nlp_backoff.png}
    \caption{Batch Size Backoff Implementation}
    \label{fig:nlp_backoff}
\end{figure}

\subsection{Ethnographic Statistics}
This section will discuss the implementation of the various ethnographic statistics that are available through the API endpoints, such as temporal analysis, linguistic analysis, emotional analysis, user analysis, interactional analysis, and cultural analysis. Each of these are available through the API and visualised in the frontend. 

\subsubsection{Temporal Analysis}
Two statistics are implemented for temporal analysis:
\begin{itemize}
    \item \textbf{Posts Per Day}: A simple count of the number of posts and comments per day, which can be visualised as a line chart or bar chart to show trends over time.
    \item \textbf{Time Heatmap}: A heatmap of posts and comments by hour of the day and day of the week, which can show patterns in when users are most active.
\end{itemize}

Both of these statistics are implemented using Pandas queries to aggregate the data by the relevant time periods, and lists of dictionaries are returned to the API for visualisation in the frontend.

\subsubsection{Linguistic Analysis}
Linguistic analysis includes three statistics:
\begin{itemize}
    \item \textbf{Word Frequency}: A count of the most common words used in the dataset, which can be visualised as a word cloud or bar chart.
    \item \textbf{N-grams}: A count of the most common n-grams (sequences of n words) used in the dataset, which can also be visualised as a word cloud or bar chart.
    \item \textbf{Lexical Diversity}: A measure of the diversity of the language used in the dataset, calculated as the ratio of unique words to total words.
\end{itemize}

Both word frequency and n-grams are calculated using the \texttt{collections.Counter} class, which provides a convenient way to count the occurrences of words and n-grams in the dataset. N-Grams take in a number \texttt{n} as a parameter, which specifies the length of the n-grams to calculate. For example, if \texttt{n} = 2, the most common two word phrases will be returned.

Lexical diversity is calculated using a simple formula that divides the number of unique words by the total number of words in the dataset.

This class requires a word exclusion list to be provided, which is a list of common words that should be excluded from the analysis, such as stop words and common words that are not relevant for analysis. These are passed in from the higher level StatGen class.

\subsubsection{User Analysis}
User analysis returns two major statistics:
\begin{itemize}
    \item \textbf{Top Users}: A count of the most active users in the dataset, which can be visualised as a bar chart or table.
    \item \textbf{Per User Analysis}: A breakdown of statistics for each user, such as the number of posts and comments, average sentiment, and most common words used by that user. Each user will be analysed as follows:
    \begin{itemize}
        \item Total number of events (posts and comments).
        \item Average emotion distribution across their events.
        \item Average topic distribution across their events.
        \item Comment-to-post ratio.
        \item Vocabulary information such as top words used and lexical diversity.
    \end{itemize}
\end{itemize}

\subsubsection{Interactional Analysis}
Interactional analysis includes three statistics:
\begin{itemize}
    \item \textbf{Interaction Graph}: A graph of interactions between users, where nodes represent users and edges represent interactions.
    \item \textbf{Top Interaction Pairs}: A count of the most common pairs of users that interact with each other, which can be visualised as a bar chart or table.
    \item \textbf{Conversation Concentration}: A measure of how concentrated conversations are around certain users.
\end{itemize}

The conversation concentration statistic shows the inequality of contributions in conversations, described in Section-\ref{sec:passive_participation}. It identifies the total number of unique commenters, calculates what share of all comments are produced by the most active top 10\% of authors, and measures how many authors only ever commented once. Put together, these metrics reveal the degree to which a community's conversation is driven by a small core of prolific contributors versus being broadly distributed. The metrics returned are:
\begin{itemize}
    \item \textbf{Total Commenting Users}: The total number of unique users who commented in the dataset.
    \item \textbf{Top 10\% Comment Share}: The percentage of all comments that were produced by the top 10\% most active commenters.
    \item \textbf{Top 10\% Author Count}: The number of unique users that make up the top 10\% most active commenters.
    \item \textbf{One-Time Commenters}: The percentage of users that only commented once in the dataset.
\end{itemize}

The interaction graph creates an index of post ids to authors to ensure fast and proper link when iterating over the dataset. In addition, issues arose with the distinction between someone replying to a post as a comment, and someone replying to a comment. The fix involved checking both \texttt{parent\_id} and \texttt{reply\_to} fields instead of just \texttt{reply\_to}.

\subsubsection{Emotional Analysis}
Emotional analysis includes four statistics:
\begin{itemize}
    \item \textbf{Average Emotion By Topic}: A breakdown of the average emotion scores for each topic.
    \item \textbf{Overall Emotional Average}: A breakdown of the average emotion scores for the entire dataset.
    \item \textbf{Dominant Emotion Distribution}: The distribution of dominant emotions per event in the dataset.
    \item \textbf{Average Emotion By Source}: A breakdown of the average emotion scores for each source platform.
\end{itemize}

Throughout development, the "surprise" and "neutral" emotion classes were in data pipeline, however they were removed from the emotional analysis as they were dominating the dataset and skewing the results.

\subsubsection{Cultural Analysis}
Cultural analysis includes three statistics:
\begin{itemize}
    \item \textbf{Identity Markers}: Statistics regarding in-group vs out-group markers, how common each are and average emotions with each, visualised as KPIs.
    \item \textbf{Stance Markers}: Returns hedge, certainty, deontic and permissive markers, how common each are and average emotions with each, visualised as KPIs.
    \item \textbf{Average Emotions Per Entity}: A breakdown of the average emotion scores for each named entity type (person, organisation, location, miscellaneous).
\end{itemize}

For stance and identity markers, the Python module \texttt{re} was used to find certain words in a post along with the counts of each. \texttt{re} was used instead of a more complex NLP approach as the goal is simply to find certain words quickly, whereas a more complex approach would be far slower.

With the identity markers, in-group markers such as "we", "us", "our" were counted, as well as out-group markers such as "they", "them", "their". For stance markers, hedge markers such as "maybe", "possibly", "might" were counted, as well as certainty markers such as "definitely", "certainly", "undoubtedly", deontic markers such as "should", "must", "ought to", and permissive markers such as "can", "could", "may". An example of the implementation for stance markers can be seen in figure \ref{fig:stance_markers}.

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/stance_markers.png}
    \caption{Finding Stance Markers with Regular Expressions}
    \label{fig:stance_markers}
\end{figure}

\subsubsection{StatGen Class}
The \texttt{StatGen} (Statistics Generator) class is a higher level module that aggregates all of the different statistics into a single class that is called by the API endpoints to generate the statistics.

Initially, all statistics were implemented within this class, however as the class grew larger and larger, it was refactored to delegate the different categories of statistics to separate classes, listed in the sections above. The class directly instantiates these analysis classes. Dependency injection of the analysis classes was considered for looser coupling, but since they were split purely for organisational and neatness purposes, extra decoupling complexity wasn't needed.

Beyond improving the quality of the code, the other main function of this class is to provide a single centralised area to manage statistical filtering. Each statistical method of the class will take in a dictionary of filters as a parameter, then the private method \texttt{\_prepare\_filtered\_df} will apply the filters to the dataset and return the filtered dataset. Four types of filters are supported:
\begin{itemize}
    \item \texttt{start\_date}: A date string that filters the dataset to only include events after the specified date.
    \item \texttt{end\_date}: A date string that filters the dataset to only include events before the specified date.
    \item \texttt{source}: A string that filters the dataset to only include events from the specified source platform.
    \item \texttt{search\_query}: A string that filters the dataset to only include events that contain the search query in their content.
\end{itemize}

Initially, stateful filtering was implemented where the filters would be stored within the \texttt{StatGen} object and are applied to all subsequent methods. The filters were applied once and could then be reset. This worked during initial stages when only one dataset was being tested, however with multiple datasets, this stateful filtering applied to other datasets (even with other users) and caused confusion, therefore a stateless approach was implemented where the filters are passed in as a parameter to each method, and the filtered dataset is returned for that method only, without affecting any other methods or datasets.

\subsection{Flask API}
The Flask API is responsible for providing the backend data to the frontend. It provides endpoints for user management, dataset management, and analysis endpoints. It also handles authentication and access control for the API. In addition, it handles extra data through some POST endpoints, such as filtering parameters and auto-fetching parameters for the connectors.

\subsubsection{User Management}
Three endpoints handle user lifecycle management. 

\texttt{POST /register} accepts a JSON body containing a username, email, and password, delegates validation and persistence to \texttt{AuthManager}, described in Section \ref{sec:auth-manager}, and returns a structured error if the username or email is already taken. 

\texttt{POST /login} verifies credentials through \texttt{AuthManager.authenticate\_user()} and, on success, returns a signed JWT access token created with Flask-JWT-Extended's \texttt{create\_access\_token()}. The user's integer ID is embedded as the token identity, which is retrieved on subsequent requests using \texttt{get\_jwt\_identity()}. The token expiry is configurable through the \texttt{JWT\_ACCESS\_TOKEN\_EXPIRES} environment variable. 

\texttt{GET /profile} is a protected endpoint that verifies the token and returns the user's profile information, and serves as a method for the frontend to display user information.

\subsubsection{Dataset Management}
Dataset management is split across several endpoints that cover the full lifecycle of a dataset from creation through deletion.

\texttt{GET /user/datasets} returns the list of all datasets owned by the authenticated user, used to populate the datasets page in the frontend. 

\texttt{GET /dataset/<id>} returns the metadata for a single dataset.

\texttt{PATCH /dataset/<id>} allows the user to rename it. 

\texttt{DELETE /dataset/<id>} removes the dataset and all associated events from the database. 

All of these routes begin with an ownership check via \texttt{dataset\_manager.authorize\_user\_dataset()}, and return a \texttt{403} if the requesting user does not own the dataset in question.

\texttt{POST /datasets/upload} handles manual file upload. It expects a multipart form submission containing a \texttt{.jsonl} posts file, a \texttt{.json} topics file, and a dataset name string. The \texttt{.jsonl} file is read directly into a Pandas DataFrame using \texttt{pd.read\_json(lines=True)}, and the topics file is loaded with the standard \texttt{json} library. Once the dataset metadata is saved to the database, the serialised DataFrame and topics dictionary are passed to the \texttt{process\_dataset} Celery task via \texttt{.delay()}, and the endpoint returns immediately with a \texttt{202 Accepted} response containing the new dataset ID. This non-blocking behaviour is essential given that NLP enrichment can take several minutes for large datasets.

\texttt{POST /datasets/scrape} handles automated data fetching. The request body contains a list of source configurations, each specifying a connector and optional search query, category, and limit. Careful validation is performed on the source configurations, as any failure within the Celery task would cause a silent failure. The dataset metadata is saved to the database, and the \texttt{fetch\_and\_process\_dataset} task is dispatched asynchronously via Celery. This task fetches each source's data using the appropriate connector, combines the result into a single DataFrame, then passes it through the same enrichment and storage process.

\texttt{GET /dataset/<id>/status} allows the frontend to poll the state of a dataset. It returns the current status string and message stored in the \texttt{datasets} table, which the Celery worker updates at each stage of the pipeline, from \texttt{"fetching"} through \texttt{"processing"} to \texttt{"complete"} or \texttt{"error"}.

\texttt{GET /dataset/<id>/all} returns the full raw event table for a dataset as a list of records, which powers the raw data viewer in the frontend.

\subsubsection{Analysis Endpoints}
Several endpoints are implemented that return each ethnographic statistic generated by the \texttt{StatGen} class. Each endpoint takes a URL parameter for the dataset ID, and an optional JSON body containing filter parameters.

For each type of analysis, there is a corresponding endpoint, the base configuration being: \texttt{/dataset/<id>/<analysis\_type>}

Each endpoint needs a JWT authorization header that corresponds to the user that owns that dataset, and the dataset ID is validated against the user's datasets to ensure they have access to it. The endpoint then fetches the entire dataset, and passes it through the global \texttt{StatGen} instance to generate statistics. The resulting statistics are returned as JSON to the frontend for visualisation.

\subsubsection{Error Handling}
Each route handler wraps its logic in a \texttt{try/except} block that catches three categories of exception. \texttt{NotAuthorisedException} maps to a \texttt{403} response. \texttt{NonExistentDatasetException} maps to \texttt{404}. \texttt{ValueError}, which is raised by input validation in the manager layers, maps to \texttt{400}. 

A bare \texttt{Exception} try-catch handles anything unexpected and returns a generic \texttt{500}, while printing a full traceback to the server log via \texttt{traceback.format\_exc()} for debugging. Error messages returned to the client are deliberately vague for unexpected errors, to avoid leaking implementation details.

\subsection{React Frontend}
The frontend is a single-page application built with React and Typescript. It communicates with the Flask backend over a REST API using Axios, and JWT tokens are attached to every authenticated request using an Axios default header. 

React Router is used for client-side routing, and the main pages of the application are:
\begin{itemize}
    \item \textbf{Login Page}: A simple login form that allows users to login and register for an account.
    \item \textbf{Datasets Page}: A page that lists all of the user's datasets, and allows them to create new datasets through file upload or automated fetching.
    \item \textbf{Dataset Status Page}: A page that shows the status of a dataset, including the progress of the data pipeline and any errors that may have occurred.
    \item \textbf{Dataset Edit Page}: A page that allows users to rename or delete a dataset.
    \item \textbf{Dataset Upload Page}: A page that allows users to upload a dataset through a file upload form.
    \item \textbf{Dataset Auto-Fetch Page}: A page that allows users to create a dataset through automated data fetching, by selecting sources and providing search queries and limits.
    \item \textbf{Dataset Analysis Page}: A page that shows all of the ethnographic statistics for a dataset, with various visualisations such as line charts, bar charts and heatmaps.
\end{itemize}

\subsubsection{Layouts}
\paragraph{Main Layout}
The main layout of the application is a React component that includes a header with the application name, a navigation bar with links to the Datasets page, and a sign out button that clears the JWT token from local storage. The main layout also includes a container for the main content of each page, which is rendered using React Router's \texttt{Outlet} component.

When logged out, instead of showing the datasets and logout button, it simply shows a sign in button. The navigation bar can be seen in Figure \ref{fig:nav_bar}. The main layout is used for all pages. 

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/navbar.png}
    \caption{Navigation Bar in the Main Layout}
    \label{fig:nav_bar}
\end{figure}

\paragraph{Analysis Layout}
The analysis layout is a React component that is used for all of the analysis pages. It still uses the Main Layout, however it adds an extra navigation bar that lets users switch between the different types of analysis, such as temporal analysis or linguistic analysis. This allows the code for navigation to be written once and used across all analysis pages, instead of in each analysis page separately. It also simplifies the URL structure, as all analysis pages have the same base URL of \texttt{/dataset/<id>/analysis}, and the type of analysis is determined by an injected React component.

In addition to an extra navigation bar, it also contains a filter component that allows users to apply filters such as search queries and date filters to the dataset, which are passed in as parameters to the API endpoints to filter the statistics. This allows users to easily filter the dataset and see how the statistics change based on the filters. The analysis layout can be seen in Figure \ref{fig:analysis_layout}.

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/analysis_bar.png}
    \caption{Analysis Layout with Navigation and Filter Components}
    \label{fig:analysis_layout}
\end{figure}

\subsubsection{Analysis Page}
The Analysis page fires six API requests in parallel to fetch the six categories of statistics (temporal, linguistic, user, interactional, emotional and cultural), and each category is rendered in a separate section on the page with its own visualisation. The API requests are fired when the page loads, and also whenever the filters are updated. This allows the API calls to be centralised into a single component, such that any change in the filters will automatically update all of the statistics on the page. Applying filters re-fetches all six endpoints with new query parameters.

The majority of statistics are displayed using a custom KPI component that shows the name of the statistic, the value, and a secondary label for other information. An example of this can be seen in Figure \ref{fig:kpi_card}. The statistics that are not displayed as KPIs, such as the temporal analysis line chart and heatmap, will be discussed in the next sections.

\begin{figure}
    \centering
    \includegraphics[width=0.5\textwidth]{img/kpi_card.png}
    \caption{Custom KPI Component for Displaying Statistics}
    \label{fig:kpi_card}
\end{figure}

\paragraph{Summary}
The summary section contains basic KPI cards such as total posts, total users, comments per post, lurker ratio, time range and sources. Beyond KPIs it also contains a word cloud of the most common words in the dataset, which is generated using the \texttt{react-wordcloud} library. The word cloud provides a visual representation of the most common words in the dataset, with the size of each word corresponding to its frequency.

A heatmap is included in the Summary section (taken from the temporal analysis endpoint) that shows the distribution of posts and comments by hour of the day and day of the week. This allows users to quickly see when users are most active in the dataset. The heatmap is generated using the \texttt{nivo} library, which provides a convenient way to create a heatmap visualization in React.

An example of the heatmap can be seen in Figure \ref{fig:heatmap}.

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/heatmap.png}
    \caption{Heatmap of Posts and Comments by Hour and Day in the Cork Dataset}
    \label{fig:heatmap}
\end{figure}

\paragraph{Emotional}
The emotional analysis section contains KPI cards for the average emotion scores for each emotion class, as well as slightly adjusted KPI cards for showing the emotions per topic, as they include secondary and tertiary labels that show the model confidence for that emotion and the number of posts that were classified with that emotion.

\paragraph{Users}
The users analysis section contains an interactive interaction graph that shows the replies and chains between users, this was done with the \texttt{react-force-graph} library, which provides a convenient way to create an interactive graph visualization in React. The nodes of the graph represent users, and the edges represent interactions between users (such as replies). The graph filters out edges with less than two interactions and removes deleted-user nodes (like "[Deleted User]") to remove noise.

An example of the interaction graph can be seen in Figure \ref{fig:interaction_graph}.

The rest of the statistics in the users section are displayed as KPI cards.

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/interaction_graph.png}
    \caption{Interaction Graph Showing User Interactions in the Cork Dataset}
    \label{fig:interaction_graph}
\end{figure}

\paragraph{Interactional}
The interactional analysis section contains KPI cards for the conversation concentration metrics, as well as a bar chart showing the top interaction pairs, which is generated using the \texttt{nivo} library. A pie chart is used to show the inequality of contributions in conversations, with the share of comments from the top 10\% most active commenters shown in one color, and the share of comments from the rest of the commenters shown in another color.

\subsubsection{Corpus Explorer}
The corpus explorer is a feature that allows users to explore the raw data of the dataset. It is implemented as a table that shows all of the posts and comments in the dataset, along with their metadata such as author, timestamp, and topic. It uses the \texttt{/dataset/<id>/all} API endpoint to fetch the raw data from the backend. It allows a user to click on most statistics and see the underlying posts that make up that statistic. For example, if a user clicks on the "City Center" topic, then the corpus explorer will filter to only show posts that were classified with the "City Center" topic. 

This is purely a frontend feature, and did not require any additional backend implementation beyond the existing API endpoint that returns the raw dataset. Initially, it was thought that performance would be an issue with loading the entire dataset into the frontend, however with some optimisations such as pagination and lazy loading, it was possible to load even large datasets without performance issues.

The full dataset is fetched once per filter state and then cached in component state. Subsequent explore actions within the same filter state reuse this cached payload rather than making further API requests. The component itself only renders 60 posts at a time, and implements pagination to navigate the dataset and keep performance smooth. This allows users to explore the raw data without overwhelming the frontend with too much data at once.

The Corpus Explorer addresses a limitation of some ethnographic analysis programs, which is statistical outputs are summaries, and a summary can be misleading. By making the source texts viewable from any figure in the dashboard, a researcher can verify the accuracy of the statistics.

\subsubsection{Styling}
Where possible, styling is kept with a centralised styling file in the frontend, which contains all of the common styles such as colors, fonts, and spacing. 

\texttt{palette.ts} contains the color palette for the application, which is used across all components to ensure a consistent look and feel. \texttt{appLayout.ts} contains the layout style for the structure and margins of the main layout. For each individual component / page, a separate TS file is used for styling.

All analysis pages use a grid layout to structure the different cards and visualisations, which allows for a clean and organised presentation of the statistics. 

\begin{figure}
    \centering
    \includegraphics[width=1.0\textwidth]{img/frontend.png}
    \caption{Summary Page of the Application}
    \label{fig:summary_page}
\end{figure}

\subsection{Deployment}
To deploy the application, Docker was used to containerise both the backend and frontend, and Docker Compose was used to orchestrate the different containers. There are five main containers in the application:
\begin{itemize}
    \item \textbf{Backend Container}: This container runs the Flask API, and is built from the \texttt{backend/Dockerfile}.
    \item \textbf{Frontend Container}: This container runs the React frontend, and is built from the \texttt{frontend/Dockerfile}.
    \item \textbf{Database Container}: This container runs the PostgreSQL database, and uses the official PostgreSQL image from Docker Hub.
    \item \textbf{Celery Worker Container}: This container runs the Celery worker, which is responsible for running the NLP enrichment and data processing tasks in the background. It is built from the same image as the backend container, but runs a different command to start the Celery worker instead of the Flask API.
    \item \textbf{Redis Container}: This container runs Redis. It uses the official Redis image from Docker Hub.
\end{itemize}

To run the application, the user needs to have Docker and Docker Compose installed on their machine. They then need to fill in the necessary environment variables in the \texttt{.env} file, for which there is a template provided as \texttt{.env.example}. The example env file contains defaults for most variables, except for the Reddit and Google API credentials that will need to be sourced. In addition, they will need to set the JWT secret key to a random 128-bit string for security reasons.

Once the environment variables are set, the user can run the command \texttt{docker compose up -d} in the root directory of the project, which will build and start all of the containers. The application will then be accessible at \texttt{http://localhost:5173} in the user's web browser.

\newpage
\section{Evaluation}
\subsection{User Feedback}
A demo session was held with a group of digital ethnographers from the  MIGDIS research group to gather feedback on the  design, functionality and usefulness of the system.

\subsubsection{Positive Reception}
The dashboard was described as user-friendly, with the tabbed interface making it straightforward to navigate between analytical perspectives. Participants noted that the system was useful for organising large datasets into meaningful sections such as emotions and locations, and considered it a practical tool for digital ethnography research with clear potential for further development.

\subsubsection{Suggested Improvements}
Several suggestions were made for improving the system by the participants, which are discussed in more detail below.

\paragraph{Deeper Emotional Analysis}
The current five-emotion model was seen as a good starting point, but ultimately lacking in nuance. They noted that out of the five existing emotions (joy, sadness, anger, fear, disgust), four of them were negative emotions, and there was a lack of nuanced positive emotions such as hope, pride, relief, etc. In the beginning stages of the project, the GoEmotions model \cite{demszky2020goemotions}, which has 27 emotion classes, was considered but ultimately rejected due to timeline constraints and complexity. However given the feedback, it's worth reconsidering for a much more nuanced emotional analysis.

\paragraph{Improved Corpus Explorer}
The corpus explorer was seen as a useful feature, however it was noted that it could be improved in a few ways:
\begin{itemize}
    \item Adding more metadata to each post, such as the NLP classifications (emotions, topics) and possibly even more than just the top emotion and topic.
    \item Adding a search and filter functionality to the corpus explorer, so that users can easily find specific posts.
    \item Rather than a flat list of posts, they should be organised into chains of comments to reflect and show the conversation structures and chains.
\end{itemize}

\paragraph{Multilingual Support}
Currently, the system only supports English language datasets, due to the Emotion and NER models being trained on English language data. However, multilingual support was suggested as a potential improvement, as the MIGDIS research group works with datasets in both English and Turkish. This would involve either using multilingual NLP models, or allowing users to specify the language of their dataset and then using the correct models for that language.

\paragraph{Flexible Topic List}
The current implementation of topics is based on a fixed list that is defined upon uploading the dataset (or the default list). It was suggested that it would be useful to be able to adjust the topic list after the dataset has been uploaded. As for the feasibility of this, it would require re-running the topic classification for the entire dataset, but ultimately it is feasible to implement.

\paragraph{Emotion Colour Grading}
Currently, in the corpus explorer and other areas where emotions are visualised, the posts aren't coloured at all. It was suggested that it would be useful to have some kind of colour grading based on the emotions, so posts that are joyful might be yellow, or posts that are angry might be red. This would allow users to quickly scan through the posts and get a sense of the emotional tone of the dataset. Though if the GoEmotions model is implemented, this might not be feasible as there are 27 emotion classes, so it would require a more complex colour scheme.

\paragraph{Popularity Indicators}
The dashboard currently provides no indication of how much engagement a post received. Adding reply counts or upvote scores alongside each post would allow researchers to distinguish between posts that generated significant discussion and those that did not, which is relevant for participation inequality analysis.

\subsection{NLP Accuracy}
The accuracy of the NLP models used in the system was evaluated using a small manually annotated dataset.  By taking 50 random examples of posts from the Cork dataset and manually annotating their topic and emotion, then comparing these annotations to the model's predictions, the accuracy of the models can be estimated. Keep in mind that this is a small sample size and is tied to a specific dataset, with specific pre-defined topics, so it may not be representative of the overall accuracy of the models across different datasets and topics.

To do this, this command was run on the Docker database container to extract 50 random posts from the Cork dataset:
\begin{verbatim}
docker exec crosspost_db psql -U postgres -d mydatabase -x -c 
"SELECT 
    id, 
    title, 
    content, 
    topic, 
    topic_confidence, 
    emotion_joy, 
    emotion_sadness, 
    emotion_anger, 
    emotion_fear, 
    emotion_disgust
FROM events 
WHERE dataset_id = 1 
ORDER BY RANDOM() 
LIMIT 50;" > output.txt
\end{verbatim}

The \texttt{WHERE dataset\_id = 1} clause specifies that we only want posts from the Cork dataset. The NLP outputs (topic and emotion predictions) were removed using \texttt{grep} on the output file, to analyse without bias.

Then the output was manually annotated with topic and emotional labels, using the same topic list and emotion classes as the model. The model's predictions were then compared to the manual annotations to calculate the accuracy for both topic classification and emotion classification. The metric will be accuracy, which is the number of correct predictions divided by the total number of predictions.

The results of this evaluation are as follows:
\begin{itemize}
    \item \textbf{Dominant Emotional Classification Accuracy}: 68\% (34 out of 50 posts were correctly classified with the dominant emotion).
    \item \textbf{Topic Classification Accuracy}: 64\% (32 out of 50 posts were correctly classified with the correct topic).
\end{itemize}


\subsubsection{Emotional Classification Discussion}
The emotional classification was notably limited in some regards. The decision described in Section \ref{sec:emotion-classification} to remove the "neutral" and "surprise" emotion classes from the emotional analysis was made after observing that the two classes were dominating the dataset. However, restricting the neutral class led to some posts being misclassified as another emotion which may not have been accurate, for example, take the content of the eleventh post in the output file (Record 11):

\begin{quote}
    \textit{26+7=1}
\end{quote}

This post was classified as "anger" with a confidence of 0.22, however this post is arguably neutral and the model's classification is inaccurate. Due to this, the neutral class was reintegrated into the emotional analysis.

In addition, some confusion arose between the "disgust" and "anger" emotion classes, as they can be quite similar in some contexts. For instance, take the content of the third post in the output file:

\begin{quote}
    \textit{That's exactly what Ruanair do in Cork, they never introduce a new route, they come in on existing routes, eliminate the competition and then either close the route or move it. They have done this on two polish routes, Cork to Dublin, Newcastle etc.}
\end{quote}

The model classified this post as "disgust" with a confidence of 0.35 and "anger" with a confidence of 0.38. This is a borderline case, and even two different human annotators could disagree on whether this post is more "disgust" or "anger", so it's understandable that the model would struggle with this. This highlights the limitations of the emotional classification, as emotions can be quite nuanced and subjective, and a model may not always capture the true emotional tone of a post accurately.

A significant reason that the accuracy was sitting around (60–70\%) is the model’s inability to represent the multi-dimensional nature of human emotion. Many posts express multiple emotions simultaneously (e.g., frustration mixed with humour), yet the model is constrained to selecting a single dominant class. This leads to misclassification in cases where no single emotion is clearly dominant.

In addition, the temporary exclusion of the “neutral” class forced inherently neutral posts into a specific category, artificially lowering accuracy. Borderline cases between closely related emotions (such as anger and disgust) also contributed to disagreement between manual annotations and model predictions, which shows how subjective emotional expressions can be.

\subsubsection{Topical Classification Discussion}
The topic classification also had some limitations, particularly with posts that contained multiple topics. For example, take the content of the 26th post in the output file:
\begin{quote}
    \textit{We're staying in the city centre so walkable to most places. I checked electrics website earlier. Looked nice. Ended up booking Joules for Thursday then for Friday, we will try a new place called "conways yard" that was recommended here. In hoping to watch the England match there so I'd imagine if have to get there well before kick off (8pm) to get a seat bear a TV.}
\end{quote}

This post was classified with the topic "Rugby" with a topic confidence of 0.47, which is quite high by most standards. However this could arguably be classified as "City Center" or even "Pubs" due to the mention of the city centre and the pub "Conway's Yard". This highlights a limitation of the topic classification, which is that it can struggle with posts that contain multiple topics, as it is only able to assign one dominant topic to each post. 

To address this, making the topic classification more similar to the emotional classification might be beneficial. That is, instead of just assigning one dominant topic to each post, the model could assign a confidence score for each topic class, which would allow posts to be classified with multiple topics if they have high confidence scores for multiple topics.

In addition, ensuring a well-curated topic list that is specific to the dataset can help improve the accuracy of the topic classification, as it reduces the chances of posts being misclassified into irrelevant topics and reduces possible overlap between topics.

\subsection{Performance Benchmarks}
The benchmarks for the performance of the system were measured in terms of the time taken for each stage of the data pipeline, including both fetching and NLP processing. The benchmarks were measured in many configurations, such as different dataset sizes, different numbers of sources for fetching, pre-gathered or auto-fetched.

It must be noted that this benchmark will be based on posts, and with each post comes a number of comments, so the total number of events (posts + comments) will be higher than the number of posts, and the performance will be affected by the total number of events rather than just the number of posts. Therefore, the benchmarks are based on the number of posts, as this is a more intuitive metric for users to understand when creating datasets.

Celery tasks return the time taken for a function to complete, so both the fetching and NLP processing times are recorded in the logs. This was tested on a machine with an AMD Ryzen 7 5800X3D, NVIDIA RTX 3070 Ti, 16GB of RAM and Arch Linux OS.

\subsubsection{NLP Performance}
This section will outline the performance of the NLP processing, which is the process of enriching the dataset with the NLP models after the data has been fetched. The performance of this feature is measured in terms of the time taken to process a certain number of posts through the NLP pipeline, which includes both emotion classification and topic classification. The benchmarks are as follows:
\begin{itemize}
    \item \textbf{10 posts}: 0.40s
    \item \textbf{100 posts}: 6.73s
    \item \textbf{1000 posts}: 67.12s
\end{itemize}

Overall this tends to follow a linear trend, with the time taken increasing linearly with the number of posts. As noted above, the number of events the pipeline is processing is likely 10-20x the number of posts, due to comments, so the actual number of events being processed is likely around 1000 for the 100 post benchmark, and around 10,000 for the 1000 post benchmark. 

The 1000 posts benchmark for \texttt{Boards.ie} took 312.83s for NLP processing, which is much higher than the other sources. This is likely due to the fact that \texttt{Boards.ie} is a forum site, with long running conversations that can last years, therefore the number of comments per thread is significantly higher than other sources. There is an average of around 900 comments per post in the \texttt{Boards.ie} dataset, compared to ~30 comments per post in the Reddit and YouTube datasets, which explains the significant increase in NLP processing time for the \texttt{Boards.ie} dataset.

\subsubsection{Auto-fetching Performance}
This section outlines the performance of the auto-fetching feature, which is the process of fetching data from the sources using the connectors. The performance of this feature is measured in terms of the time taken to fetch a certain number of posts from each source. The benchmarks are shown in Table \ref{tab:performance_benchmarks}.

One important thing to note is that the YouTube API does not return more than 500 posts for a given search query, so the 1000 posts benchmark for YouTube is not available.

\begin{table}[!h]
    \centering
    \begin{tabular}{|c|c|c|c|}
        \hline
        \textbf{Size} & \textbf{Reddit} & \textbf{Boards.ie} & \textbf{YouTube} \\
        \hline
        10 posts & 3.25s & 103.28s & 2.08s \\
        100 posts & 37.46s & 1182.71s & 12.52s \\
        1000 posts & 482.87s & 11196.19s & N/A \\
        \hline
    \end{tabular}
    \caption{Performance Benchmarks for the Auto-Fetch Process}
    \label{tab:performance_benchmarks}
\end{table}

\texttt{Boards.ie} is by far the slowest, likely to be a combination of two factors: web scraping is simply slower than using an API, as comments have to be fetched page by page, with the connector loading and parsing each page fully \textbf{and} \texttt{Boards.ie} threads have a significantly higher number of comments per post due to the forum nature of the site. Though the rate of post-fetching from \texttt{Boards.ie} is poor, it scaled linearly with the number of posts.

Reddit was much faster than \texttt{Boards.ie}, likely due to the fact that it uses an API. Though it is affected by rate limits, and during the 1000-post benchmark test, the API rate limits was hit once, where it stalled for exactly 120 seconds, which when taken into account, also shows that Reddit scales linearly with the number of posts.

YouTube was the fastest source, likely due to the fact that it also uses an API and hit no rate limits. However, the YouTube API does not allow fetching more than 500 posts for a given search query. For 500 posts, the time taken was 74.80s. If we extrapolate the time taken for 1000 posts, it would be around 149.60s, which is still much faster than the other sources and scaled linearly with the number of posts.

\subsection{Cork Dataset Findings}
The Cork dataset, described in Section \ref{sec:cork_dataset}, was analysed using the system, and several findings were observed from the analysis.

\subsubsection{Temporal Findings}
Temporal activity patterns show that the most active hours for posting are weekdays between 1pm and 3pm, which suggests that users post during their lunch breaks in the early afternoon. There is also a smaller peak in activity in the evenings around 8pm, which is likely when users are off work. The least active hours are early mornings between 1am and 6am, which is expected as most users are likely asleep during these hours.

\begin{figure}[!h]
    \centering
    \includegraphics[width=1\textwidth]{img/cork_temporal.png}
    \caption{Activity Heatmap for the Cork Dataset, where the X axis represents the hour of day and the Y axis represents the day of the week. Blue areas indicate higher activity, lighter areas indicate lower activity.}
    \label{fig:cork_temporal}
\end{figure} 

\subsubsection{Linguistic Findings}

\begin{figure}[!h]
    \centering
    \includegraphics[width=1\textwidth]{img/ngrams.png}
    \caption{Bigrams and Trigrams for the Cork Dataset, showing the most common two-word and three-word combinations in the dataset.}
    \label{fig:cork_linguistic}
\end{figure}

Figure \ref{fig:cork_linguistic} shows the most common bigrams and trigrams in the Cork dataset. Trigrams like "north main street" and "oliver plunkett street" suggest strong local geographic identity in the community, a shared physical space. Bigrams like "city centre" and "cork city" further reinforce this. 

"anti social behaviour" with 85 mentions suggests that this is a common civic concern within the community. The use of "years ago" suggests that there is a sense of nostalgia or reflection of the past in the community's discourse.

\subsubsection{Emotional Findings}
\begin{figure}[!h]
    \centering
    \includegraphics[width=1\textwidth]{img/moods.png}
    \caption{Average Emotion Scores for the Cork Dataset}
    \label{fig:cork_emotions}
\end{figure}

Figure \ref{fig:cork_emotions} shows the average emotion scores for the Cork dataset. The most dominant emotion in the dataset is "joy", which suggests that the community has a more positive tone than other online communities. However this could also be due to the fact that the "neutral" emotion class was removed from the analysis, which may have forced some neutral posts to be classified as "joy".

\begin{figure}[!h]
    \centering
    \includegraphics[width=1\textwidth]{img/topic_emotions.png}
    \caption{Topic-Emotion Analysis for Exams, Flooding and Food in the Cork Dataset}
    \label{fig:cork_emotions_by_topic}
\end{figure}

In Figure \ref{fig:cork_emotions_by_topic}, we can see that the "Food" topic has a much higher average "joy" score than any other emotion, shown by it having a model confidence of 0.53 for "joy". In addition this topic has a large sample size of 1390 posts, which reinforces the finding that the "Food" topic is associated with "joy" in the Cork dataset. 

The "Exams" topic has a dominant emotion of "sadness", which makes sense given the stressful nature of exams.

Interestingly, the "Flooding" topic has a dominant emotion of "anger". This could mean that users are angry about the flooding situation or the government response to flooding, however further analysis shows in Figure \ref{fig:flooding_posts} that it is a mixture of posts being misclassified as "flooding" due to the presence of words like "flood" and "water", and posts that are angry about the government response and infrastructure issues related to flooding. This highlights the limitations of the topic classification, as it can struggle with posts that contain multiple topics or posts that are misclassified into a topic due to the presence of certain keywords.

\begin{figure}
    \centering
    \includegraphics[width=1\textwidth]{img/flooding_posts.png}
    \caption{Posts Classified with the "Flooding" Topic}
    \label{fig:flooding_posts}
\end{figure}

\subsection{Limitations}
Several limitations of the system became apparent through development, evaluation and user testing.

\subsubsection{NLP Accuracy}
As shown in the accuracy evaluation above, both emotion and topic classification only achieve accuracy within the 60-70\% range. This is ok for detecting patterns across the entire dataset, but on an individual level, it's quite inaccurate. The removal of the "neutral" class, while initially motivated by the desire to surface more meaningful emotional signals, introduced misclassification for genuinely neutral posts such as simple arithmetic or factual statements.

\subsubsection{Temporal Coverage}
The system is designed to fetch only the most recent posts and comments from social media platforms, which means the dataset is limited to a relatively short time window, usually a few weeks at most. This limits the ability to perform true long-term temporal analysis or to study the evolution of a community over time.

\subsubsection{Platform Coverage}
The system currently supports three data sources: Reddit, YouTube, and Boards.ie. It still excludes major platforms such as Twitter/X, Facebook, and TikTok, all of which would be valuable for ethnography, however their APIs are either extremely restrictive or non-existent for academic research. Should someone need to study communities on those platforms, they would need to upload their own datasets manually.

\subsubsection{Boards.ie Scraping Fragility}
The Boards.ie connector relies on web scraping, which is very fragile and prone to breaking. The performance benchmarks for fetching from Boards.ie are significantly higher than the other sources. Fetching 100 posts from Boards.ie took around 20 minutes, which is much higher than the other sources.

\subsubsection{English-Only Support}
Two of three NLP models used in the system are trained exclusively on English-language data. This means the system cannot accurately analyse datasets in other languages, which limits its usefulness for researchers working with non-English communities. This was noted as a specific concern by participants in the user feedback session, who work with both English and Turkish datasets.

\newpage
\section{Conclusions}
\subsection{Reflection}
I have learned a lot through the process of building this system, both in terms of technical skills and personal growth. This project represented the most technically complex system I had built independently to date.

The analytical scope is the project's most visible limitation. Six analytical angles across many data sources sounds comprehensive, but with a single developer and a fixed timeline, the actual ethnographic depth achievable was modest. The decision between depth of ethnographic analysis and typical SaaS-type infrastructure and features was a tension throughout the project. Eventually a balance between the two was achieved, but there was some sacrifice in the analysis depth for the sake of building a more complete and polished system.

Planning the project was a challenge, as generally I tend to work iteratively. I jump in and start building straight away, and I find that the process of building helps me to figure out what I actually want to build. This led to some awkward parts in the report where design and implementation often overlapped and were made in a non-linear fashion. Creating the design section was difficult when implementation had already started, and design was still changed throughout the implementation process.

On a personal level, the project was a significant learning experience in terms of time management and project planning. The planning and implementation of the project was ambitious but easy to get carried away with, and I found myself spending a lot of time on features that were not essential to the core functionality of the system. The implementation was felt productive and visible in a way that the writing of a report was not, I found myself spending more time on the implementation than the report, and the report was pushed to the sidelines until the end of the project.

\subsection{How the project was conducted}
\begin{figure}[!h]
    \centering
    \includegraphics[width=1\textwidth]{img/gantt.png}
    \caption{Gantt Chart of the Project Timeline}
    \label{fig:gantt_chart}
\end{figure}

The project was maintained and developed using Git for version control, with the repository being hosted on Github.

Starting in November, the project went through a few iterations of basic functionality such as data retrieval and storage. Research was done on digital ethnography, the traditional metrics used, and how they're implemented in code. The design of the system was also iterated on, with the initial design being a very simple frontend that showed simple aggregates, into a more complex and feature-rich dashboard with multiple analytical perspectives and NLP enrichments. 

The majority of real development and implementation took place between January and April, with the final month of April being focused on testing, bug fixing, writing the report and preparation for the open day. The project was developed in an agile and iterative way, with new features being added and improved upon throughout the development process, rather than having a fixed plan for the entire project from the beginning.

Git was used as a changelog of decisions and rationale, to aid writing the report. But if this project were to be done again, I would maintain the report alongside the implementation from the beginning, as it would have made writing the report much easier and less stressful at the end.

\subsection{Future Work}
This section discusses several potential areas for future work and improvements to the system.

\subsubsection{Improved Emotional Analysis}
As noted in the user feedback and accuracy evaluation sections, the emotional analysis could be improved by implementing a more nuanced emotion classification model, such as the GoEmotions model with 27 emotion classes \cite{demszky2020goemotions}. 

This would require some changes to the database schema, as currently, the "events" table contains a column for each of the five emotion classes, which would not be feasible with 27 emotion classes. A more flexible schema would be needed, such as having a separate "emotions" table that contains the emotion classifications for each post, with columns for the post ID, emotion class, and confidence score. 

Or something similar to how NER classifications are stored, which are simply \texttt{JSONB} columns that contain a list of all the classifications for each post, which allows for a variable number of classifications and is more flexible for future changes to the emotion classification.

\subsubsection{Multilingual Support}
The project was largely built around English language datasets, therefore the emotional and NER models are trained on English language data and would not work with other languages. Beyond the NLP models, the stances and identity markers currently implemented use English-specific keywords such as "we", "us", "I", "me".

To support multilingual datasets, multilingual NLP models could be implemented to allow language detection to be automatic. However, as the specific stance and identity markers would be required for different languages, a better solution would be for the user to specify the language of their dataset upon uploading, and then the system could use the correct NLP models, stance/identity marker lists and stop words for that language.

\subsubsection{Improved Corpus Explorer}
The corpus explorer could be improved by allowing users to see more metadata for each post, such as the NLP classifications and possibly even more than just the top emotion and topic.

In addition, reconstructing the reply chains and conversation structures in the corpus explorer would allow users to see the context of each post and how they relate to each other. It would allow researchers to gauge the power dynamics between users and the conversational structures.

Colour grading each post in the corpus explorer based on its emotional classification would be both aesthetically pleasing and useful for users to quickly scan through the posts and get a sense of the emotional tone of the dataset.

\newpage
\bibliography{references}

\end{document}
