docs(readme): update readme

.gitignore

@@ -13,3 +13,4 @@ dist/
 helper
 db
 report/build
+.DS_Store

README.md

@@ -1,29 +1,49 @@
 # crosspost
-**crosspost** is a browser-based tool designed to support *digital ethnography*, the study of how people interact, communicate, and form culture in online spaces such as forums, social media platforms, and comment-driven communities.
+A web-based analytics platform for exploring online communities. Built as a final year CS project at UCC, crosspost ingests data from Reddit, YouTube, and Boards.ie, runs NLP analysis on it (emotion detection, topic classification, named entity recognition, stance markers), and surfaces the results through an interactive dashboard.
+The motivating use case is digital ethnography — studying how people talk, what they talk about, and how culture forms in online spaces. The included dataset is centred on Cork, Ireland.
 
-The project aims to make it easier for students, researchers, and journalists to collect, organise, and explore online discourse in a structured and ethical way, without requiring deep technical expertise.
+## What it does
+- Fetch posts and comments from Reddit, YouTube, and Boards.ie (or upload your own .jsonl file)
+- Normalise everything into a unified schema regardless of source
+- Run NLP analysis asynchronously in the background via Celery workers
+- Explore results through a tabbed dashboard: temporal patterns, word clouds, emotion breakdowns, user activity, interaction graphs, topic clusters, and more
+- Multi-user support — each user has their own datasets, isolated from everyone else
 
-By combining data ingestion, analysis, and visualisation in a single system, crosspost turns raw online interactions into meaningful insights about how conversations emerge, evolve, and spread across platforms.
+# Prerequisites
+- Docker & Docker Compose
+- A Reddit App (client id & secret)
+- YouTube Data v3 API Key
 
-## Goals for this project
+# Setup
-- Collect data ethically: enable users to link/upload text, images, and interaction data (messages etc) from specified online communities. Potentially and automated method for importing (using APIs or scraping techniques) could be included as well.
+1) **Clone the Repo**
-- Organise content: Store gathered material in a structured database with tagging for themes, dates, and sources.
+```
-Analyse patterns: Use natural language processing (NLP) to detect frequent keywords, sentiment, and interaction networks.
+git clone https://github.com/your-username/crosspost.git
-- Visualise insights: Present findings as charts, timelines, and network diagrams to reveal how conversations and topics evolve.
+cd crosspost
-- Have clearly stated and explained ethical and privacy guidelines for users. The student will design the architecture, implement data pipelines, integrate basic NLP models, and create an interactive dashboard. 
+```
 
-Beyond programming, the project involves applying ethical research principles, handling data responsibly, and designing for non-technical users. By the end, the project will demonstrate how computer science can bridge technology and social research — turning raw online interactions into meaningful cultural insights.
+2) **Configure Enviornment Vars**
+```
+cp example.env .env
+```
+Fill in each required empty env. Some are already filled in, these are sensible defaults that usually don't need to be changed
 
-## Scope
+3) **Start everything**
+```
+docker compose up -d
+```
 
-This project focuses on:
+This starts:
-- Designing a modular data ingestion pipeline
+- `crosspost_db` — PostgreSQL on port 5432
-- Implementing backend data processing and storage
+- `crosspost_redis` — Redis on port 6379
-- Integrating lightweight NLP-based analysis
+- `crosspost_flask` — Flask API on port 5000
-- Building a simple, accessible frontend for exploration and visualisation
+- `crosspost_worker` — Celery worker for background NLP/fetching tasks
+- `crosspost_frontend` — Vite dev server on port 5173
 
-# Requirements
+# Data Format for Manual Uploads
+If you want to upload your own data rather than fetch it via the connectors, the expected format is newline-delimited JSON (.jsonl) where each line is a post object:
+```json
+{"id": "abc123", "author": "username", "title": "Post title", "content": "Post body", "url": "https://...", "timestamp": 1700000000.0, "source": "reddit", "comments": []}
+```
 
-- **Python** ≥ 3.9
+# Notes
-- **Python packages** listed in `requirements.txt`
+- **GPU support**: The Celery worker is configured with `--pool=solo` to avoid memory conflicts when multiple NLP models are loaded. If you have an NVIDIA GPU, uncomment the deploy.resources block in docker-compose.yml and make sure the NVIDIA Container Toolkit is installed.
-- npm ≥ version 11 

docker-compose.dev.yml

@@ -28,7 +28,7 @@ services:
       - .env
     ports:
       - "5000:5000"
-    command: flask --app server.app run --host=0.0.0.0 --debug
+    command: gunicorn server.app:app --bind 0.0.0.0:5000 --workers 2 --threads 4
     depends_on:
       - postgres
       - redis
@@ -48,13 +48,13 @@ services:
     depends_on:
       - postgres
       - redis
-    # deploy:
+    deploy:
-    #   resources:
+      resources:
-    #     reservations:
+        reservations:
-    #       devices:
+          devices:
-    #         - driver: nvidia
+            - driver: nvidia
-    #           count: 1
+              count: 1
-    #           capabilities: [gpu]
+              capabilities: [gpu]
 
   frontend:
     build:

dto/user.py

@@ -1,8 +0,0 @@
-# Generic User Data Transfer Object for social media platforms
-class User:
-    def __init__(self, username: str, created_utc: int, ):
-        self.username = username
-        self.created_utc = created_utc
-
-        # Optionals
-        self.karma = None

report/references.bib

@@ -68,3 +68,82 @@
   year    = {2024},
   doi     = {10.1016/j.nlp.2024.100059}
 }
+
+@article{coleman2010ethnographic,
+  ISSN = {00846570},
+  URL = {http://www.jstor.org/stable/25735124},
+  abstract = {This review surveys and divides the ethnographic corpus on digital media into three broad but overlapping categories: the cultural politics of digital media, the vernacular cultures of digital media, and the prosaics of digital media. Engaging these three categories of scholarship on digital media, I consider how ethnographers are exploring the complex relationships between the local practices and global implications of digital media, their materiality and politics, and thier banal, as well as profound, presence in cultural life and modes of communication. I consider the way these media have become central to the articulation of cherished beliefs, ritual practices, and modes of being in the world; the fact that digital media culturally matters is undeniable but showing how, where, and why it matters is necessary to push against peculiarly narrow presumptions about the universality of digital experience.},
+  author = {E. Gabriella Coleman},
+  journal = {Annual Review of Anthropology},
+  pages = {487--505},
+  publisher = {Annual Reviews},
+  title = {Ethnographic Approaches to Digital Media},
+  urldate = {2026-04-15},
+  volume = {39},
+  year = {2010}
+}
+
+@article{shen2021stance,
+  author  = {Shen, Qian and Tao, Yating},
+  title   = {Stance Markers in {English} Medical Research Articles and Newspaper Opinion Columns: A Comparative Corpus-Based Study},
+  journal = {PLOS ONE},
+  volume  = {16},
+  number  = {3},
+  pages   = {e0247981},
+  year    = {2021},
+  doi     = {10.1371/journal.pone.0247981}
+}
+
+@incollection{medvedev2019anatomy,
+  author    = {Medvedev, Alexey N. and Lambiotte, Renaud and Delvenne, Jean-Charles},
+  title     = {The Anatomy of Reddit: An Overview of Academic Research},
+  booktitle = {Dynamics On and Of Complex Networks III},
+  series    = {Springer Proceedings in Complexity},
+  publisher = {Springer},
+  year      = {2019},
+  pages     = {183--204}
+}
+
+@misc{cook2023ethnography,
+  author       = {Cook, Chloe},
+  title        = {What is the Difference Between Ethnography and Digital Ethnography?},
+  year         = {2023},
+  month        = jan,
+  day          = {19},
+  howpublished = {\url{https://ethosapp.com/blog/what-is-the-difference-between-ethnography-and-digital-ethnography/}},
+  note         = {Accessed: 2026-04-16},
+  organization = {EthOS}
+}
+
+@misc{giuffre2026sentiment,
+  author       = {Giuffre, Steven},
+  title        = {What is Sentiment Analysis?},
+  year         = {2026},
+  month        = mar,
+  howpublished = {\url{https://www.vonage.com/resources/articles/sentiment-analysis/}},
+  note         = {Accessed: 2026-04-16},
+  organization = {Vonage}
+}
+
+@misc{mungalpara2022stemming,
+  author       = {Mungalpara, Jaimin},
+  title        = {Stemming Lemmatization Stopwords and {N}-Grams in {NLP}},
+  year         = {2022},
+  month        = jul,
+  day          = {26},
+  howpublished = {\url{https://jaimin-ml2001.medium.com/stemming-lemmatization-stopwords-and-n-grams-in-nlp-96f8e8b6aa6f}},
+  note         = {Accessed: 2026-04-16},
+  organization = {Medium}
+}
+
+@misc{chugani2025ethicalscraping,
+  author       = {Chugani, Vinod},
+  title        = {Ethical Web Scraping: Principles and Practices},
+  year         = {2025},
+  month        = apr,
+  day          = {21},
+  howpublished = {\url{https://www.datacamp.com/blog/ethical-web-scraping}},
+  note         = {Accessed: 2026-04-16},
+  organization = {DataCamp}
+}
+

requirements.txt

@@ -16,3 +16,4 @@ Requests==2.32.5
 sentence_transformers==5.2.2
 torch==2.10.0
 transformers==5.1.0
+gunicorn==25.3.0

server/connectors/base.py

@@ -1,21 +1,18 @@
 from abc import ABC, abstractmethod
 from dto.post import Post
+import os
 
 
 class BaseConnector(ABC):
-    # Each subclass declares these at the class level
+    source_name: str  # machine readable
-    source_name: str  # machine-readable: "reddit", "youtube"
+    display_name: str  # human readablee
-    display_name: str  # human-readable: "Reddit", "YouTube"
+    required_env: list[str] = []  
-    required_env: list[str] = []  # env vars needed to activate
 
     search_enabled: bool
     categories_enabled: bool
 
     @classmethod
     def is_available(cls) -> bool:
-        """Returns True if all required env vars are set."""
-        import os
-
         return all(os.getenv(var) for var in cls.required_env)
 
     @abstractmethod

server/connectors/boards_api.py

@@ -87,7 +87,7 @@ class BoardsAPI(BaseConnector):
             post = self._parse_thread(html, post_url)
             return post
 
-        with ThreadPoolExecutor(max_workers=10) as executor:
+        with ThreadPoolExecutor(max_workers=5) as executor:
             futures = {executor.submit(fetch_and_parse, url): url for url in urls}
 
             for i, future in enumerate(as_completed(futures)):

server/connectors/reddit_api.py

@@ -234,6 +234,7 @@ class RedditAPI(BaseConnector):
                 if response.status_code == 429:
                     try:
                         wait_time = int(response.headers.get("X-Ratelimit-Reset", backoff))
+                        wait_time += 1  # Add a small buffer to ensure the rate limit has reset
                     except ValueError:
                         wait_time = backoff
 

server/connectors/youtube_api.py

@@ -1,5 +1,6 @@
 import os
 import datetime
+import logging
 
 from dotenv import load_dotenv
 from googleapiclient.discovery import build
@@ -9,9 +10,11 @@ from dto.comment import Comment
 from server.connectors.base import BaseConnector
 
 load_dotenv()
-
 API_KEY = os.getenv("YOUTUBE_API_KEY")
 
+logger = logging.getLogger(__name__)
+logger.setLevel(logging.INFO)
+
 
 class YouTubeAPI(BaseConnector):
     source_name: str = "youtube"
@@ -77,11 +80,30 @@ class YouTubeAPI(BaseConnector):
         return True
 
     def _search_videos(self, query, limit):
+        results = []
+        next_page_token = None
+
+        while len(results) < limit:
+            batch_size = min(50, limit - len(results))
+
             request = self.youtube.search().list(
-            q=query, part="snippet", type="video", maxResults=limit
+                q=query, 
+                part="snippet", 
+                type="video", 
+                maxResults=batch_size, 
+                pageToken=next_page_token
             )
+
             response = request.execute()
-        return response.get("items", [])
+            results.extend(response.get("items", []))
+            logging.info(f"Fetched {len(results)} out of {limit} videos for query '{query}'")
+
+            next_page_token = response.get("nextPageToken")
+            if not next_page_token:
+                logging.warning(f"No more pages of results available for query '{query}'")
+                break
+
+        return results[:limit]
 
     def _get_video_comments(self, video_id):
         request = self.youtube.commentThreads().list(