docs(readme): update readme

Reviewed-on: #11

.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
-- 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.
-- 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.
-- Visualise insights: Present findings as charts, timelines, and network diagrams to reveal how conversations and topics evolve.
-- 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. 
+# Setup
+1) **Clone the Repo**
+```
+git clone https://github.com/your-username/crosspost.git
+cd crosspost
+```
 
-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:
-- Designing a modular data ingestion pipeline
-- Implementing backend data processing and storage
-- Integrating lightweight NLP-based analysis
-- Building a simple, accessible frontend for exploration and visualisation
+This starts:
+- `crosspost_db` — PostgreSQL on port 5432
+- `crosspost_redis` — Redis on port 6379
+- `crosspost_flask` — Flask API on port 5000
+- `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
-- **Python packages** listed in `requirements.txt`
-- npm ≥ version 11 
+# Notes
+- **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.

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:
-    #   resources:
-    #     reservations:
-    #       devices:
-    #         - driver: nvidia
-    #           count: 1
-    #           capabilities: [gpu]
+    deploy:
+      resources:
+        reservations:
+          devices:
+            - driver: nvidia
+              count: 1
+              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

example.env

@@ -1,13 +1,16 @@
 # API Keys
 YOUTUBE_API_KEY=
+REDDIT_CLIENT_ID=
+REDDIT_CLIENT_SECRET=
 
 # Database
-POSTGRES_USER=
-POSTGRES_PASSWORD=
-POSTGRES_DB=
-POSTGRES_HOST=
+# Database
+POSTGRES_USER=postgres
+POSTGRES_PASSWORD=postgres
+POSTGRES_DB=mydatabase
+POSTGRES_HOST=postgres
 POSTGRES_PORT=5432
-POSTGRES_DIR=
+POSTGRES_DIR=./db
 
 # JWT
 JWT_SECRET_KEY=

frontend/src/App.tsx

@@ -5,7 +5,7 @@ import DatasetsPage from "./pages/Datasets";
 import DatasetStatusPage from "./pages/DatasetStatus";
 import LoginPage from "./pages/Login";
 import UploadPage from "./pages/Upload";
-import AutoScrapePage from "./pages/AutoScrape";
+import AutoFetchPage from "./pages/AutoFetch";
 import StatPage from "./pages/Stats";
 import { getDocumentTitle } from "./utils/documentTitle";
 import DatasetEditPage from "./pages/DatasetEdit";
@@ -23,7 +23,7 @@ function App() {
         <Route path="/" element={<Navigate to="/login" replace />} />
         <Route path="/login" element={<LoginPage />} />
         <Route path="/upload" element={<UploadPage />} />
-        <Route path="/auto-scrape" element={<AutoScrapePage />} />
+        <Route path="/auto-fetch" element={<AutoFetchPage />} />
         <Route path="/datasets" element={<DatasetsPage />} />
         <Route path="/dataset/:datasetId/status" element={<DatasetStatusPage />} />
         <Route path="/dataset/:datasetId/stats" element={<StatPage />} />

frontend/src/components/CorpusExplorer.tsx

@@ -1,4 +1,4 @@
-import { useEffect, useMemo, useState } from "react";
+import { useEffect, useState } from "react";
 import { Dialog, DialogPanel, DialogTitle } from "@headlessui/react";
 
 import StatsStyling from "../styles/stats_styling";
@@ -103,11 +103,6 @@ const CorpusExplorer = ({
     }
   }, [open, title, records.length]);
 
-  const visibleRecords = useMemo(
-    () => records.slice(0, visibleCount),
-    [records, visibleCount],
-  );
-
   const hasMoreRecords = visibleCount < records.length;
 
   return (
@@ -158,7 +153,7 @@ const CorpusExplorer = ({
                   paddingRight: 4,
                 }}
               >
-                {visibleRecords.map((record, index) => {
+                {records.slice(0, visibleCount).map((record, index) => {
                   const recordKey = getRecordKey(record, index);
                   const titleText = getRecordTitle(record);
                   const content = cleanText(record.content);

frontend/src/components/CulturalStats.tsx

@@ -8,11 +8,11 @@ import {
   buildHedgeSpec,
   buildIdentityBucketSpec,
   buildPermissionSpec,
-  getExplorerButtonStyle,
   type CorpusExplorerSpec,
 } from "../utils/corpusExplorer";
 
 const styles = StatsStyling;
+const exploreButtonStyle = { padding: "4px 8px", fontSize: 12 };
 
 type CulturalStatsProps = {
   data: CulturalAnalysisResponse;
@@ -22,7 +22,7 @@ type CulturalStatsProps = {
 const renderExploreButton = (onClick: () => void) => (
   <button
     onClick={onClick}
-    style={{ ...styles.buttonSecondary, ...getExplorerButtonStyle() }}
+    style={{ ...styles.buttonSecondary, ...exploreButtonStyle }}
   >
     Explore
   </button>

frontend/src/components/SummaryStats.tsx

@@ -26,12 +26,12 @@ import {
   buildDateBucketSpec,
   buildOneTimeUsersSpec,
   buildUserSpec,
-  getExplorerButtonStyle,
   type CorpusExplorerSpec,
 } from "../utils/corpusExplorer";
 
 const styles = StatsStyling;
 const MAX_WORDCLOUD_WORDS = 250;
+const exploreButtonStyle = { padding: "4px 8px", fontSize: 12 };
 
 const WORDCLOUD_OPTIONS = {
   rotations: 2,
@@ -80,7 +80,7 @@ function convertFrequencyData(data: FrequencyWord[]) {
 const renderExploreButton = (onClick: () => void) => (
   <button
     onClick={onClick}
-    style={{ ...styles.buttonSecondary, ...getExplorerButtonStyle() }}
+    style={{ ...styles.buttonSecondary, ...exploreButtonStyle }}
   >
     Explore
   </button>

frontend/src/components/UserModal.tsx

@@ -88,6 +88,15 @@ export default function UserModal({
                   </div>
                 </div>
               ) : null}
+
+              {userData.dominant_topic ? (
+                <div style={styles.topUserItem}>
+                  <div style={styles.topUserName}>Most Common Topic</div>
+                  <div style={styles.topUserMeta}>
+                    {userData.dominant_topic.topic} ({userData.dominant_topic.count} events)
+                  </div>
+                </div>
+              ) : null}
             </div>
           )}
         </DialogPanel>

frontend/src/components/UserStats.tsx

@@ -20,7 +20,7 @@ type GraphLink = {
   value: number;
 };
 
-function ApiToGraphData(apiData: InteractionGraph) {
+function toGraphData(apiData: InteractionGraph) {
   const links: GraphLink[] = [];
   const connectedNodeIds = new Set<string>();
 
@@ -56,7 +56,7 @@ const UserStats = ({
   onExplore,
 }: UserStatsProps) => {
   const graphData = useMemo(
-    () => ApiToGraphData(interactionGraph),
+    () => toGraphData(interactionGraph),
     [interactionGraph],
   );
   const graphContainerRef = useRef<HTMLDivElement | null>(null);

frontend/src/pages/AutoFetch.tsx

@@ -37,7 +37,7 @@ const supportsSearch = (source?: SourceOption): boolean =>
 const supportsCategories = (source?: SourceOption): boolean =>
   Boolean(source?.categories_enabled ?? source?.categoriesEnabled);
 
-const AutoScrapePage = () => {
+const AutoFetchPage = () => {
   const navigate = useNavigate();
   const [datasetName, setDatasetName] = useState("");
   const [sourceOptions, setSourceOptions] = useState<SourceOption[]>([]);
@@ -106,11 +106,11 @@ const AutoScrapePage = () => {
     );
   };
 
-  const autoScrape = async () => {
+  const autoFetch = async () => {
     const token = localStorage.getItem("access_token");
     if (!token) {
       setHasError(true);
-      setReturnMessage("You must be signed in to auto scrape a dataset.");
+      setReturnMessage("You must be signed in to auto fetch a dataset.");
       return;
     }
 
@@ -243,7 +243,7 @@ const AutoScrapePage = () => {
       setReturnMessage("");
 
       const response = await axios.post(
-        `${API_BASE_URL}/datasets/scrape`,
+        `${API_BASE_URL}/datasets/fetch`,
         requestBody,
         {
           headers: {
@@ -255,7 +255,7 @@ const AutoScrapePage = () => {
       const datasetId = Number(response.data.dataset_id);
 
       setReturnMessage(
-        `Auto scrape queued successfully (dataset #${datasetId}). Redirecting to processing status...`,
+        `Auto fetch queued successfully (dataset #${datasetId}). Redirecting to processing status...`,
       );
 
       setTimeout(() => {
@@ -267,11 +267,11 @@ const AutoScrapePage = () => {
         const message = String(
           requestError.response?.data?.error ||
             requestError.message ||
-            "Auto scrape failed.",
+            "Auto fetch failed.",
         );
-        setReturnMessage(`Auto scrape failed: ${message}`);
+        setReturnMessage(`Auto fetch failed: ${message}`);
       } else {
-        setReturnMessage("Auto scrape failed due to an unexpected error.");
+        setReturnMessage("Auto fetch failed due to an unexpected error.");
       }
     } finally {
       setIsSubmitting(false);
@@ -283,9 +283,9 @@ const AutoScrapePage = () => {
       <div style={styles.containerWide}>
         <div style={{ ...styles.card, ...styles.headerBar }}>
           <div>
-            <h1 style={styles.sectionHeaderTitle}>Auto Scrape Dataset</h1>
+            <h1 style={styles.sectionHeaderTitle}>Auto Fetch Dataset</h1>
             <p style={styles.sectionHeaderSubtitle}>
-              Select sources and scrape settings, then queue processing
+              Select sources and fetch settings, then queue processing
               automatically.
             </p>
             <p
@@ -295,7 +295,7 @@ const AutoScrapePage = () => {
                 color: "#9a6700",
               }}
             >
-              Warning: Scraping more than 250 posts from any single site can
+              Warning: Fetching more than 250 posts from any single site can
               take hours due to rate limits.
             </p>
           </div>
@@ -305,10 +305,10 @@ const AutoScrapePage = () => {
               ...styles.buttonPrimary,
               opacity: isSubmitting || isLoadingSources ? 0.75 : 1,
             }}
-            onClick={autoScrape}
+            onClick={autoFetch}
             disabled={isSubmitting || isLoadingSources}
           >
-            {isSubmitting ? "Queueing..." : "Auto Scrape and Analyze"}
+            {isSubmitting ? "Queueing..." : "Auto Fetch and Analyze"}
           </button>
         </div>
 
@@ -527,4 +527,4 @@ const AutoScrapePage = () => {
   );
 };
 
-export default AutoScrapePage;
+export default AutoFetchPage;

frontend/src/pages/Datasets.tsx

@@ -108,9 +108,9 @@ const DatasetsPage = () => {
             <button
               type="button"
               style={styles.buttonSecondary}
-              onClick={() => navigate("/auto-scrape")}
+              onClick={() => navigate("/auto-fetch")}
             >
-              Auto Scrape Dataset
+              Auto Fetch Dataset
             </button>
           </div>
         </div>

frontend/src/pages/Stats.tsx

@@ -66,45 +66,110 @@ const EMPTY_EXPLORER_STATE: ExplorerState = {
   error: "",
 };
 
-const getExplorerRecordIdentity = (record: DatasetRecord) =>
-  JSON.stringify({
-    post_id: record.post_id ?? null,
-    parent_id: record.parent_id ?? null,
-    reply_to: record.reply_to ?? null,
-    author: record.author ?? null,
-    type: record.type ?? null,
-    timestamp: record.timestamp ?? null,
-    dt: record.dt ?? null,
-    title: record.title ?? null,
-    content: record.content ?? null,
-    source: record.source ?? null,
-    topic: record.topic ?? null,
-  });
+const createExplorerState = (
+  spec: CorpusExplorerSpec,
+  patch: Partial<ExplorerState> = {},
+): ExplorerState => ({
+  open: true,
+  title: spec.title,
+  description: spec.description,
+  emptyMessage: spec.emptyMessage ?? "No matching records found.",
+  records: [],
+  loading: false,
+  error: "",
+  ...patch,
+});
 
-const dedupeExplorerRecords = (records: DatasetRecord[]) => {
-  const uniqueRecords: DatasetRecord[] = [];
-  const seen = new Set<string>();
+const compareRecordsByNewest = (a: DatasetRecord, b: DatasetRecord) => {
+  const aValue = String(a.dt ?? a.date ?? a.timestamp ?? "");
+  const bValue = String(b.dt ?? b.date ?? b.timestamp ?? "");
+  return bValue.localeCompare(aValue);
+};
 
-  for (const record of records) {
-    const identity = getExplorerRecordIdentity(record);
-    if (seen.has(identity)) {
-      continue;
+const parseJsonLikePayload = (value: string): unknown => {
+  const normalized = value
+    .replace(/\uFEFF/g, "")
+    .replace(/,\s*([}\]])/g, "$1")
+    .replace(/(:\s*)(NaN|Infinity|-Infinity)\b/g, "$1null")
+    .replace(/(\[\s*)(NaN|Infinity|-Infinity)\b/g, "$1null")
+    .replace(/(,\s*)(NaN|Infinity|-Infinity)\b/g, "$1null")
+    .replace(/(:\s*)None\b/g, "$1null")
+    .replace(/(:\s*)True\b/g, "$1true")
+    .replace(/(:\s*)False\b/g, "$1false")
+    .replace(/(\[\s*)None\b/g, "$1null")
+    .replace(/(\[\s*)True\b/g, "$1true")
+    .replace(/(\[\s*)False\b/g, "$1false")
+    .replace(/(,\s*)None\b/g, "$1null")
+    .replace(/(,\s*)True\b/g, "$1true")
+    .replace(/(,\s*)False\b/g, "$1false");
+
+  return JSON.parse(normalized);
+};
+
+const tryParseRecords = (value: string) => {
+  try {
+    return normalizeRecordPayload(parseJsonLikePayload(value));
+  } catch {
+    return null;
+  }
+};
+
+const parseRecordStringPayload = (payload: string): DatasetRecord[] | null => {
+  const trimmed = payload.trim();
+  if (!trimmed) {
+    return [];
   }
 
-    seen.add(identity);
-    uniqueRecords.push(record);
+  const direct = tryParseRecords(trimmed);
+  if (direct) {
+    return direct;
   }
 
-  return uniqueRecords;
+  const ndjsonLines = trimmed
+    .split(/\r?\n/)
+    .map((line) => line.trim())
+    .filter(Boolean);
+  if (ndjsonLines.length > 0) {
+    try {
+      return ndjsonLines.map((line) => parseJsonLikePayload(line)) as DatasetRecord[];
+    } catch {
+    }
+  }
+
+  const bracketStart = trimmed.indexOf("[");
+  const bracketEnd = trimmed.lastIndexOf("]");
+  if (bracketStart !== -1 && bracketEnd > bracketStart) {
+    const parsed = tryParseRecords(trimmed.slice(bracketStart, bracketEnd + 1));
+    if (parsed) {
+      return parsed;
+    }
+  }
+
+  const braceStart = trimmed.indexOf("{");
+  const braceEnd = trimmed.lastIndexOf("}");
+  if (braceStart !== -1 && braceEnd > braceStart) {
+    const parsed = tryParseRecords(trimmed.slice(braceStart, braceEnd + 1));
+    if (parsed) {
+      return parsed;
+    }
+  }
+
+  return null;
 };
 
 const normalizeRecordPayload = (payload: unknown): DatasetRecord[] => {
   if (typeof payload === "string") {
-    try {
-      return normalizeRecordPayload(JSON.parse(payload));
-    } catch {
-      throw new Error("Corpus endpoint returned a non-JSON string payload.");
+    const parsed = parseRecordStringPayload(payload);
+    if (parsed) {
+      return parsed;
     }
+
+    const preview = payload.trim().slice(0, 120).replace(/\s+/g, " ");
+    throw new Error(
+      `Corpus endpoint returned a non-JSON string payload.${
+        preview ? ` Response preview: ${preview}` : ""
+      }`,
+    );
   }
 
   if (
@@ -265,9 +330,7 @@ const StatPage = () => {
       },
     );
 
-    const normalizedRecords = dedupeExplorerRecords(
-      normalizeRecordPayload(response.data),
-    );
+    const normalizedRecords = normalizeRecordPayload(response.data);
 
     setAllRecords(normalizedRecords);
     setAllRecordsKey(filterKey);
@@ -275,47 +338,22 @@ const StatPage = () => {
   };
 
   const openExplorer = async (spec: CorpusExplorerSpec) => {
-    setExplorerState({
-      open: true,
-      title: spec.title,
-      description: spec.description,
-      emptyMessage: spec.emptyMessage ?? "No matching records found.",
-      records: [],
-      loading: true,
-      error: "",
-    });
+    setExplorerState(createExplorerState(spec, { loading: true }));
 
     try {
       const records = await ensureFilteredRecords();
       const context = buildExplorerContext(records);
-      const matched = dedupeExplorerRecords(
-        records.filter((record) => spec.matcher(record, context)),
-      );
-      matched.sort((a, b) => {
-        const aValue = String(a.dt ?? a.date ?? a.timestamp ?? "");
-        const bValue = String(b.dt ?? b.date ?? b.timestamp ?? "");
-        return bValue.localeCompare(aValue);
-      });
+      const matched = records
+        .filter((record) => spec.matcher(record, context))
+        .sort(compareRecordsByNewest);
 
-      setExplorerState({
-        open: true,
-        title: spec.title,
-        description: spec.description,
-        emptyMessage: spec.emptyMessage ?? "No matching records found.",
-        records: matched,
-        loading: false,
-        error: "",
-      });
+      setExplorerState(createExplorerState(spec, { records: matched }));
     } catch (e) {
-      setExplorerState({
-        open: true,
-        title: spec.title,
-        description: spec.description,
-        emptyMessage: spec.emptyMessage ?? "No matching records found.",
-        records: [],
-        loading: false,
+      setExplorerState(
+        createExplorerState(spec, {
           error: `Failed to load corpus records: ${String(e)}`,
-      });
+        }),
+      );
     }
   };
 

frontend/src/types/ApiTypes.ts

@@ -34,6 +34,11 @@ type Vocab = {
   top_words: FrequencyWord[];
 };
 
+type DominantTopic = {
+  topic: string;
+  count: number;
+};
+
 type User = {
   author: string;
   post: number;
@@ -41,6 +46,7 @@ type User = {
   comment_post_ratio: number;
   comment_share: number;
   avg_emotions?: Record<string, number>;
+  dominant_topic?: DominantTopic | null;
   vocab?: Vocab | null;
 };
 
@@ -162,6 +168,10 @@ type StanceMarkers = {
   certainty_per_1k_tokens: number;
   deontic_per_1k_tokens: number;
   permission_per_1k_tokens: number;
+  hedge_emotion_avg?: Record<string, number>;
+  certainty_emotion_avg?: Record<string, number>;
+  deontic_emotion_avg?: Record<string, number>;
+  permission_emotion_avg?: Record<string, number>;
 };
 
 type EntityEmotionAggregate = {
@@ -202,6 +212,7 @@ type FilterResponse = {
 
 export type {
   TopUser,
+  DominantTopic,
   Vocab,
   User,
   InteractionGraph,

frontend/src/utils/corpusExplorer.ts

@@ -1,5 +1,3 @@
-import type { CSSProperties } from "react";
-
 type EntityRecord = {
   text?: string;
   [key: string]: unknown;
@@ -58,11 +56,6 @@ const EMOTION_KEYS = [
   "emotion_sadness",
 ] as const;
 
-const shrinkButtonStyle: CSSProperties = {
-  padding: "4px 8px",
-  fontSize: 12,
-};
-
 const toText = (value: unknown) => {
   if (typeof value === "string") {
     return value;
@@ -83,6 +76,7 @@ const toText = (value: unknown) => {
 };
 
 const normalize = (value: unknown) => toText(value).trim().toLowerCase();
+const getAuthor = (record: DatasetRecord) => toText(record.author).trim();
 
 const getRecordText = (record: DatasetRecord) =>
   `${record.title ?? ""} ${record.content ?? ""}`.trim();
@@ -152,11 +146,11 @@ const matchesPhrase = (record: DatasetRecord, phrase: string) => {
     return false;
   }
 
-  return pattern.test(getRecordText(record).toLowerCase());
+  return pattern.test(getRecordText(record));
 };
 
 const recordIdentityBucket = (record: DatasetRecord) => {
-  const text = getRecordText(record).toLowerCase();
+  const text = getRecordText(record);
   const inHits = countMatches(IN_GROUP_PATTERN, text);
   const outHits = countMatches(OUT_GROUP_PATTERN, text);
 
@@ -171,48 +165,30 @@ const recordIdentityBucket = (record: DatasetRecord) => {
   return "tie";
 };
 
-const createAuthorEventCounts = (records: DatasetRecord[]) => {
-  const counts = new Map<string, number>();
+const buildExplorerContext = (records: DatasetRecord[]): CorpusExplorerContext => {
+  const authorByPostId = new Map<string, string>();
+  const authorEventCounts = new Map<string, number>();
+  const authorCommentCounts = new Map<string, number>();
+
   for (const record of records) {
-    const author = toText(record.author).trim();
+    const author = getAuthor(record);
     if (!author) {
       continue;
     }
-    counts.set(author, (counts.get(author) ?? 0) + 1);
-  }
-  return counts;
-};
 
-const createAuthorCommentCounts = (records: DatasetRecord[]) => {
-  const counts = new Map<string, number>();
-  for (const record of records) {
-    const author = toText(record.author).trim();
-    if (!author || record.type !== "comment") {
-      continue;
-    }
-    counts.set(author, (counts.get(author) ?? 0) + 1);
-  }
-  return counts;
-};
+    authorEventCounts.set(author, (authorEventCounts.get(author) ?? 0) + 1);
 
-const createAuthorByPostId = (records: DatasetRecord[]) => {
-  const map = new Map<string, string>();
-  for (const record of records) {
-    const postId = record.post_id;
-    const author = toText(record.author).trim();
-    if (postId === null || postId === undefined || !author) {
-      continue;
+    if (record.type === "comment") {
+      authorCommentCounts.set(author, (authorCommentCounts.get(author) ?? 0) + 1);
     }
-    map.set(String(postId), author);
-  }
-  return map;
-};
 
-const buildExplorerContext = (records: DatasetRecord[]): CorpusExplorerContext => ({
-  authorByPostId: createAuthorByPostId(records),
-  authorEventCounts: createAuthorEventCounts(records),
-  authorCommentCounts: createAuthorCommentCounts(records),
-});
+    if (record.post_id !== null && record.post_id !== undefined) {
+      authorByPostId.set(String(record.post_id), author);
+    }
+  }
+
+  return { authorByPostId, authorEventCounts, authorCommentCounts };
+};
 
 const buildAllRecordsSpec = (): CorpusExplorerSpec => ({
   title: "Corpus Explorer",
@@ -221,19 +197,27 @@ const buildAllRecordsSpec = (): CorpusExplorerSpec => ({
   matcher: () => true,
 });
 
-const buildUserSpec = (author: string): CorpusExplorerSpec => ({
+const buildUserSpec = (author: string): CorpusExplorerSpec => {
+  const target = normalize(author);
+
+  return {
     title: `User: ${author}`,
     description: `All records authored by ${author}.`,
     emptyMessage: `No records found for ${author}.`,
-  matcher: (record) => normalize(record.author) === normalize(author),
-});
+    matcher: (record) => normalize(record.author) === target,
+  };
+};
 
-const buildTopicSpec = (topic: string): CorpusExplorerSpec => ({
+const buildTopicSpec = (topic: string): CorpusExplorerSpec => {
+  const target = normalize(topic);
+
+  return {
     title: `Topic: ${topic}`,
     description: `Records assigned to the ${topic} topic bucket.`,
     emptyMessage: `No records found in the ${topic} topic bucket.`,
-  matcher: (record) => normalize(record.topic) === normalize(topic),
-});
+    matcher: (record) => normalize(record.topic) === target,
+  };
+};
 
 const buildDateBucketSpec = (date: string): CorpusExplorerSpec => ({
   title: `Date Bucket: ${date}`,
@@ -256,37 +240,52 @@ const buildNgramSpec = (ngram: string): CorpusExplorerSpec => ({
   matcher: (record) => matchesPhrase(record, ngram),
 });
 
-const buildEntitySpec = (entity: string): CorpusExplorerSpec => ({
+const buildEntitySpec = (entity: string): CorpusExplorerSpec => {
+  const target = normalize(entity);
+
+  return {
     title: `Entity: ${entity}`,
     description: `Records mentioning the ${entity} entity.`,
     emptyMessage: `No records found for the ${entity} entity.`,
     matcher: (record) => {
-    const target = normalize(entity);
       const entities = Array.isArray(record.ner_entities) ? record.ner_entities : [];
       return entities.some((item) => normalize(item?.text) === target) || matchesPhrase(record, entity);
     },
-});
+  };
+};
 
-const buildSourceSpec = (source: string): CorpusExplorerSpec => ({
+const buildSourceSpec = (source: string): CorpusExplorerSpec => {
+  const target = normalize(source);
+
+  return {
     title: `Source: ${source}`,
     description: `Records from the ${source} source.`,
     emptyMessage: `No records found for ${source}.`,
-  matcher: (record) => normalize(record.source) === normalize(source),
-});
+    matcher: (record) => normalize(record.source) === target,
+  };
+};
 
-const buildDominantEmotionSpec = (emotion: string): CorpusExplorerSpec => ({
+const buildDominantEmotionSpec = (emotion: string): CorpusExplorerSpec => {
+  const target = normalize(emotion);
+
+  return {
     title: `Dominant Emotion: ${emotion}`,
     description: `Records where ${emotion} is the strongest emotion score.`,
     emptyMessage: `No records found with dominant emotion ${emotion}.`,
-  matcher: (record) => getDominantEmotion(record) === normalize(emotion),
-});
+    matcher: (record) => getDominantEmotion(record) === target,
+  };
+};
 
-const buildReplyPairSpec = (source: string, target: string): CorpusExplorerSpec => ({
+const buildReplyPairSpec = (source: string, target: string): CorpusExplorerSpec => {
+  const sourceName = normalize(source);
+  const targetName = normalize(target);
+
+  return {
     title: `Reply Path: ${source} -> ${target}`,
     description: `Reply records authored by ${source} in response to ${target}.`,
     emptyMessage: `No reply records found for ${source} -> ${target}.`,
     matcher: (record, context) => {
-    if (normalize(record.author) !== normalize(source)) {
+      if (normalize(record.author) !== sourceName) {
         return false;
       }
 
@@ -295,49 +294,21 @@ const buildReplyPairSpec = (source: string, target: string): CorpusExplorerSpec
         return false;
       }
 
-    const replyTarget = context.authorByPostId.get(String(replyTo));
-    return normalize(replyTarget) === normalize(target);
+      return normalize(context.authorByPostId.get(String(replyTo))) === targetName;
     },
-});
+  };
+};
 
 const buildOneTimeUsersSpec = (): CorpusExplorerSpec => ({
   title: "One-Time Users",
   description: "Records written by authors who appear exactly once in the filtered corpus.",
   emptyMessage: "No one-time-user records found.",
   matcher: (record, context) => {
-    const author = toText(record.author).trim();
+    const author = getAuthor(record);
     return !!author && context.authorEventCounts.get(author) === 1;
   },
 });
 
-const buildTopCommentersSpec = (topAuthorCount: number): CorpusExplorerSpec => ({
-  title: "Top Commenters",
-  description: `Comment records from the top ${topAuthorCount} commenters in the filtered corpus.`,
-  emptyMessage: "No top-commenter records found.",
-  matcher: (record, context) => {
-    if (record.type !== "comment") {
-      return false;
-    }
-
-    const rankedAuthors = Array.from(context.authorCommentCounts.entries())
-      .sort((a, b) => b[1] - a[1])
-      .slice(0, topAuthorCount)
-      .map(([author]) => author);
-
-    return rankedAuthors.includes(toText(record.author).trim());
-  },
-});
-
-const buildSingleCommentAuthorsSpec = (): CorpusExplorerSpec => ({
-  title: "Single-Comment Authors",
-  description: "Comment records from authors who commented exactly once.",
-  emptyMessage: "No single-comment-author records found.",
-  matcher: (record, context) => {
-    const author = toText(record.author).trim();
-    return record.type === "comment" && !!author && context.authorCommentCounts.get(author) === 1;
-  },
-});
-
 const buildIdentityBucketSpec = (bucket: "in" | "out" | "tie"): CorpusExplorerSpec => {
   const labels = {
     in: "In-Group Posts",
@@ -376,9 +347,7 @@ const buildDeonticSpec = () =>
 const buildPermissionSpec = () =>
   buildPatternSpec("Permission Words", "Records containing permission language.", PERMISSION_PATTERN);
 
-const getExplorerButtonStyle = () => shrinkButtonStyle;
-
-export type { DatasetRecord, CorpusExplorerContext, CorpusExplorerSpec };
+export type { DatasetRecord, CorpusExplorerSpec };
 export {
   buildAllRecordsSpec,
   buildCertaintySpec,
@@ -393,13 +362,10 @@ export {
   buildOneTimeUsersSpec,
   buildPermissionSpec,
   buildReplyPairSpec,
-  buildSingleCommentAuthorsSpec,
   buildSourceSpec,
   buildTopicSpec,
-  buildTopCommentersSpec,
   buildUserSpec,
   buildWordSpec,
   getDateBucket,
-  getExplorerButtonStyle,
   toText,
 };

frontend/src/utils/documentTitle.ts

@@ -3,7 +3,7 @@ const DEFAULT_TITLE = "Ethnograph View";
 const STATIC_TITLES: Record<string, string> = {
   "/login": "Sign In",
   "/upload": "Upload Dataset",
-  "/auto-scrape": "Auto Scrape Dataset",
+  "/auto-fetch": "Auto Fetch Dataset",
   "/datasets": "My Datasets",
 };
 

report/references.bib

@@ -0,0 +1,149 @@
+@online{reddit_api,
+  author  = {{Reddit Inc.}},
+  title   = {Reddit API Documentation},
+  year    = {2025},
+  url     = {https://www.reddit.com/dev/api/},
+  urldate = {2026-04-08}
+}
+
+@misc{hartmann2022emotionenglish,
+  author={Hartmann, Jochen},
+  title={Emotion English DistilRoBERTa-base},
+  year={2022},
+  howpublished = {\url{https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/}},
+}
+
+@misc{all_mpnet_base_v2,
+  author={Microsoft Research},
+  title={All-MPNet-Base-V2},
+  year={2021},
+  howpublished = {\url{https://huggingface.co/sentence-transformers/all-mpnet-base-v2}},
+}
+
+@misc{minilm_l6_v2,
+  author={Microsoft Research},
+  title={MiniLM-L6-V2},
+  year={2021},
+  howpublished = {\url{https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2}},
+}
+
+@misc{dslim_bert_base_ner,
+  author={deepset},
+  title={dslim/bert-base-NER},
+  year={2018},
+  howpublished = {\url{https://huggingface.co/dslim/bert-base-NER}},
+}
+
+@inproceedings{demszky2020goemotions,
+ author = {Demszky, Dorottya and Movshovitz-Attias, Dana and Ko, Jeongwoo and Cowen, Alan and Nemade, Gaurav and Ravi, Sujith},
+ booktitle = {58th Annual Meeting of the Association for Computational Linguistics (ACL)},
+ title = {{GoEmotions: A Dataset of Fine-Grained Emotions}},
+ year = {2020}
+}
+
+@article{dominguez2007virtual,
+  author    = {Domínguez, Daniel and Beaulieu, Anne and Estalella, Adolfo and Gómez, Edgar and Schnettler, Bernt and Read, Rosie},
+  title     = {Virtual Ethnography},
+  journal   = {Forum Qualitative Sozialforschung / Forum: Qualitative Social Research},
+  year      = {2007},
+  volume    = {8},
+  number    = {3},
+  url       = {http://nbn-resolving.de/urn:nbn:de:0114-fqs0703E19}
+}
+
+@article{sun2014lurkers,
+  author  = {Sun, Na and Rau, Pei-Luen Patrick and Ma, Liang},
+  title   = {Understanding Lurkers in Online Communities: A Literature Review},
+  journal = {Computers in Human Behavior},
+  year    = {2014},
+  volume  = {38},
+  pages   = {110--117},
+  doi     = {10.1016/j.chb.2014.05.022}
+}
+
+@article{ahmad2024sentiment,
+  author  = {Ahmad, Waqar and others},
+  title   = {Recent Advancements and Challenges of NLP-based Sentiment Analysis: A State-of-the-art Review},
+  journal = {Natural Language Processing Journal},
+  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/analysis/cultural.py

@@ -67,6 +67,12 @@ class CulturalAnalysis:
 
     def get_stance_markers(self, df: pd.DataFrame) -> dict[str, Any]:
         s = df[self.content_col].fillna("").astype(str)
+        emotion_exclusions = {"emotion_neutral", "emotion_surprise"}
+        emotion_cols = [
+            c
+            for c in df.columns
+            if c.startswith("emotion_") and c not in emotion_exclusions
+        ]
 
         hedge_pattern = re.compile(
             r"\b(maybe|perhaps|possibly|probably|likely|seems|seem|i think|i feel|i guess|kind of|sort of|somewhat)\b"
@@ -88,7 +94,7 @@ class CulturalAnalysis:
             0, 1
         )
 
-        return {
+        result = {
             "hedge_total": int(hedge_counts.sum()),
             "certainty_total": int(certainty_counts.sum()),
             "deontic_total": int(deontic_counts.sum()),
@@ -107,6 +113,32 @@ class CulturalAnalysis:
             ),
         }
 
+        if emotion_cols:
+            emo = df[emotion_cols].apply(pd.to_numeric, errors="coerce").fillna(0.0)
+
+            result["hedge_emotion_avg"] = (
+                emo.loc[hedge_counts > 0].mean()
+                if (hedge_counts > 0).any()
+                else pd.Series(0.0, index=emotion_cols)
+            ).to_dict()
+            result["certainty_emotion_avg"] = (
+                emo.loc[certainty_counts > 0].mean()
+                if (certainty_counts > 0).any()
+                else pd.Series(0.0, index=emotion_cols)
+            ).to_dict()
+            result["deontic_emotion_avg"] = (
+                emo.loc[deontic_counts > 0].mean()
+                if (deontic_counts > 0).any()
+                else pd.Series(0.0, index=emotion_cols)
+            ).to_dict()
+            result["permission_emotion_avg"] = (
+                emo.loc[perm_counts > 0].mean()
+                if (perm_counts > 0).any()
+                else pd.Series(0.0, index=emotion_cols)
+            ).to_dict()
+
+        return result
+
     def get_avg_emotions_per_entity(
         self, df: pd.DataFrame, top_n: int = 25, min_posts: int = 10
     ) -> dict[str, Any]:

server/analysis/linguistic.py

@@ -1,17 +1,30 @@
-import pandas as pd
 import re
-
 from collections import Counter
-from itertools import islice
+from dataclasses import dataclass
+
+import pandas as pd
+
+
+@dataclass(frozen=True)
+class NGramConfig:
+    min_token_length: int = 3
+    min_count: int = 2
+    max_results: int = 100
 
 
 class LinguisticAnalysis:
     def __init__(self, word_exclusions: set[str]):
         self.word_exclusions = word_exclusions
+        self.ngram_config = NGramConfig()
 
-    def _tokenize(self, text: str):
-        tokens = re.findall(r"\b[a-z]{3,}\b", text)
-        return [t for t in tokens if t not in self.word_exclusions]
+    def _tokenize(self, text: str, *, include_exclusions: bool = False) -> list[str]:
+        pattern = rf"\b[a-z]{{{self.ngram_config.min_token_length},}}\b"
+        tokens = re.findall(pattern, text)
+
+        if include_exclusions:
+            return tokens
+
+        return [token for token in tokens if token not in self.word_exclusions]
 
     def _clean_text(self, text: str) -> str:
         text = re.sub(r"http\S+", "", text)  # remove URLs
@@ -21,13 +34,24 @@ class LinguisticAnalysis:
         text = re.sub(r"\S+\.(jpg|jpeg|png|webp|gif)", "", text)
         return text
 
+    def _content_texts(self, df: pd.DataFrame) -> pd.Series:
+        return df["content"].dropna().astype(str).apply(self._clean_text).str.lower()
+
+    def _valid_ngram(self, tokens: tuple[str, ...]) -> bool:
+        if any(token in self.word_exclusions for token in tokens):
+            return False
+
+        if len(set(tokens)) == 1:
+            return False
+
+        return True
+
     def word_frequencies(self, df: pd.DataFrame, limit: int = 100) -> list[dict]:
-        texts = df["content"].dropna().astype(str).str.lower()
+        texts = self._content_texts(df)
 
         words = []
         for text in texts:
-            tokens = re.findall(r"\b[a-z]{3,}\b", text)
-            words.extend(w for w in tokens if w not in self.word_exclusions)
+            words.extend(self._tokenize(text))
 
         counts = Counter(words)
 
@@ -40,25 +64,39 @@ class LinguisticAnalysis:
 
         return word_frequencies.to_dict(orient="records")
 
-    def ngrams(self, df: pd.DataFrame, n=2, limit=100):
-        texts = df["content"].dropna().astype(str).apply(self._clean_text).str.lower()
+    def ngrams(self, df: pd.DataFrame, n: int = 2, limit: int | None = None) -> list[dict]:
+        if n < 2:
+            raise ValueError("n must be at least 2")
+
+        texts = self._content_texts(df)
         all_ngrams = []
+        result_limit = limit or self.ngram_config.max_results
 
         for text in texts:
-            tokens = re.findall(r"\b[a-z]{3,}\b", text)
+            tokens = self._tokenize(text, include_exclusions=True)
 
-            # stop word removal causes strange behaviors in ngrams
-            # tokens = [w for w in tokens if w not in self.word_exclusions]
+            if len(tokens) < n:
+                continue
 
-            ngrams = zip(*(islice(tokens, i, None) for i in range(n)))
-            all_ngrams.extend([" ".join(ng) for ng in ngrams])
+            for index in range(len(tokens) - n + 1):
+                ngram_tokens = tuple(tokens[index : index + n])
+                if self._valid_ngram(ngram_tokens):
+                    all_ngrams.append(" ".join(ngram_tokens))
 
         counts = Counter(all_ngrams)
+        filtered_counts = [
+            (ngram, count)
+            for ngram, count in counts.items()
+            if count >= self.ngram_config.min_count
+        ]
+
+        if not filtered_counts:
+            return []
 
         return (
-            pd.DataFrame(counts.items(), columns=["ngram", "count"])
-            .sort_values("count", ascending=False)
-            .head(limit)
+            pd.DataFrame(filtered_counts, columns=["ngram", "count"])
+            .sort_values(["count", "ngram"], ascending=[False, True])
+            .head(result_limit)
             .to_dict(orient="records")
         )
 

server/analysis/stat_gen.py

@@ -89,39 +89,17 @@ class StatGen:
             df.to_json(orient="records", date_format="iso", date_unit="s")
         )
 
-    def _dedupe_records(self, records: list[dict]) -> list[dict]:
-        unique_records = []
-        seen = set()
-
-        for record in records:
-            key_data = {
-                "post_id": record.get("post_id"),
-                "parent_id": record.get("parent_id"),
-                "reply_to": record.get("reply_to"),
-                "author": record.get("author"),
-                "type": record.get("type"),
-                "timestamp": record.get("timestamp"),
-                "dt": record.get("dt"),
-                "title": record.get("title"),
-                "content": record.get("content"),
-                "source": record.get("source"),
-                "topic": record.get("topic"),
-            }
-            key = json.dumps(key_data, sort_keys=True, separators=(",", ":"))
-            if key in seen:
-                continue
-
-            seen.add(key)
-            unique_records.append(record)
-
-        return unique_records
-
     ## Public Methods
     def filter_dataset(self, df: pd.DataFrame, filters: dict | None = None) -> list[dict]:
         filtered_df = self._prepare_filtered_df(df, filters)
-        return self._dedupe_records(self._json_ready_records(filtered_df))
+        return self._json_ready_records(filtered_df)
 
-    def temporal(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+    def temporal(
+        self,
+        df: pd.DataFrame,
+        filters: dict | None = None,
+        dataset_id: int | None = None,
+    ) -> dict:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return {
@@ -129,7 +107,12 @@ class StatGen:
             "weekday_hour_heatmap": self.temporal_analysis.heatmap(filtered_df),
         }
 
-    def linguistic(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+    def linguistic(
+        self,
+        df: pd.DataFrame,
+        filters: dict | None = None,
+        dataset_id: int | None = None,
+    ) -> dict:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return {
@@ -139,7 +122,12 @@ class StatGen:
             "lexical_diversity": self.linguistic_analysis.lexical_diversity(filtered_df)
         }
 
-    def emotional(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+    def emotional(
+        self,
+        df: pd.DataFrame,
+        filters: dict | None = None,
+        dataset_id: int | None = None,
+    ) -> dict:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return {
@@ -149,7 +137,12 @@ class StatGen:
             "emotion_by_source": self.emotional_analysis.emotion_by_source(filtered_df)
         }
 
-    def user(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+    def user(
+        self,
+        df: pd.DataFrame,
+        filters: dict | None = None,
+        dataset_id: int | None = None,
+    ) -> dict:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return {
@@ -157,7 +150,12 @@ class StatGen:
             "users": self.user_analysis.per_user_analysis(filtered_df)
         }
 
-    def interactional(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+    def interactional(
+        self,
+        df: pd.DataFrame,
+        filters: dict | None = None,
+        dataset_id: int | None = None,
+    ) -> dict:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return {
@@ -166,7 +164,12 @@ class StatGen:
             "conversation_concentration": self.interaction_analysis.conversation_concentration(filtered_df)
         }
 
-    def cultural(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+    def cultural(
+        self,
+        df: pd.DataFrame,
+        filters: dict | None = None,
+        dataset_id: int | None = None,
+    ) -> dict:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return {
@@ -175,7 +178,12 @@ class StatGen:
             "avg_emotion_per_entity": self.cultural_analysis.get_avg_emotions_per_entity(filtered_df)
         }
 
-    def summary(self, df: pd.DataFrame, filters: dict | None = None) -> dict:
+    def summary(
+        self,
+        df: pd.DataFrame,
+        filters: dict | None = None,
+        dataset_id: int | None = None,
+    ) -> dict:
         filtered_df = self._prepare_filtered_df(df, filters)
 
         return self.summary_analysis.summary(filtered_df)

server/analysis/user.py

@@ -71,6 +71,7 @@ class UserAnalysis:
         per_user = df.groupby(["author", "type"]).size().unstack(fill_value=0)
 
         emotion_cols = [col for col in df.columns if col.startswith("emotion_")]
+        dominant_topic_by_author = {}
 
         avg_emotions_by_author = {}
         if emotion_cols:
@@ -80,6 +81,31 @@ class UserAnalysis:
                 for author, row in avg_emotions.iterrows()
             }
 
+        if "topic" in df.columns:
+            topic_df = df[
+                df["topic"].notna()
+                & (df["topic"] != "")
+                & (df["topic"] != "Misc")
+            ]
+            if not topic_df.empty:
+                topic_counts = (
+                    topic_df.groupby(["author", "topic"])
+                    .size()
+                    .reset_index(name="count")
+                    .sort_values(
+                        ["author", "count", "topic"],
+                        ascending=[True, False, True],
+                    )
+                    .drop_duplicates(subset=["author"])
+                )
+                dominant_topic_by_author = {
+                    row["author"]: {
+                        "topic": row["topic"],
+                        "count": int(row["count"]),
+                    }
+                    for _, row in topic_counts.iterrows()
+                }
+
         # ensure columns always exist
         for col in ("post", "comment"):
             if col not in per_user.columns:
@@ -109,6 +135,7 @@ class UserAnalysis:
                     "comment_post_ratio": float(row.get("comment_post_ratio", 0)),
                     "comment_share": float(row.get("comment_share", 0)),
                     "avg_emotions": avg_emotions_by_author.get(author, {}),
+                    "dominant_topic": dominant_topic_by_author.get(author),
                     "vocab": vocab_by_author.get(
                         author,
                         {

server/app.py

@@ -152,9 +152,9 @@ def get_dataset_sources():
     return jsonify(list_metadata)
 
 
-@app.route("/datasets/scrape", methods=["POST"])
+@app.route("/datasets/fetch", methods=["POST"])
 @jwt_required()
-def scrape_data():
+def fetch_data():
     data = request.get_json()
     connector_metadata = get_connector_metadata()
 
@@ -424,7 +424,7 @@ def get_linguistic_analysis(dataset_id):
 
         dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
-        return jsonify(stat_gen.linguistic(dataset_content, filters)), 200
+        return jsonify(stat_gen.linguistic(dataset_content, filters, dataset_id=dataset_id)), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
     except NonExistentDatasetException:
@@ -448,7 +448,7 @@ def get_emotional_analysis(dataset_id):
 
         dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
-        return jsonify(stat_gen.emotional(dataset_content, filters)), 200
+        return jsonify(stat_gen.emotional(dataset_content, filters, dataset_id=dataset_id)), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
     except NonExistentDatasetException:
@@ -472,7 +472,7 @@ def get_summary(dataset_id):
 
         dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
-        return jsonify(stat_gen.summary(dataset_content, filters)), 200
+        return jsonify(stat_gen.summary(dataset_content, filters, dataset_id=dataset_id)), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
     except NonExistentDatasetException:
@@ -496,7 +496,7 @@ def get_temporal_analysis(dataset_id):
 
         dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
-        return jsonify(stat_gen.temporal(dataset_content, filters)), 200
+        return jsonify(stat_gen.temporal(dataset_content, filters, dataset_id=dataset_id)), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
     except NonExistentDatasetException:
@@ -520,7 +520,7 @@ def get_user_analysis(dataset_id):
 
         dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
-        return jsonify(stat_gen.user(dataset_content, filters)), 200
+        return jsonify(stat_gen.user(dataset_content, filters, dataset_id=dataset_id)), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
     except NonExistentDatasetException:
@@ -544,7 +544,7 @@ def get_cultural_analysis(dataset_id):
 
         dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
-        return jsonify(stat_gen.cultural(dataset_content, filters)), 200
+        return jsonify(stat_gen.cultural(dataset_content, filters, dataset_id=dataset_id)), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
     except NonExistentDatasetException:
@@ -568,7 +568,7 @@ def get_interaction_analysis(dataset_id):
 
         dataset_content = dataset_manager.get_dataset_content(dataset_id)
         filters = get_request_filters()
-        return jsonify(stat_gen.interactional(dataset_content, filters)), 200
+        return jsonify(stat_gen.interactional(dataset_content, filters, dataset_id=dataset_id)), 200
     except NotAuthorisedException:
         return jsonify({"error": "User is not authorised to access this content"}), 403
     except NonExistentDatasetException:

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: "reddit", "youtube"
-    display_name: str  # human-readable: "Reddit", "YouTube"
-    required_env: list[str] = []  # env vars needed to activate
+    source_name: str  # machine readable
+    display_name: str  # human readablee
+    required_env: list[str] = []  
 
     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

@@ -11,8 +11,7 @@ from server.connectors.base import BaseConnector
 
 logger = logging.getLogger(__name__)
 
-HEADERS = {"User-Agent": "Mozilla/5.0 (compatible; ForumScraper/1.0)"}
-
+HEADERS = {"User-Agent": "Mozilla/5.0 (compatible; Digital-Ethnography-Aid/1.0)"}
 
 class BoardsAPI(BaseConnector):
     source_name: str = "boards.ie"
@@ -88,7 +87,7 @@ class BoardsAPI(BaseConnector):
             post = self._parse_thread(html, post_url)
             return post
 
-        with ThreadPoolExecutor(max_workers=30) 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

@@ -1,6 +1,10 @@
 import requests
 import logging
 import time
+import os
+
+from dotenv import load_dotenv
+from requests.auth import HTTPBasicAuth
 
 from dto.post import Post
 from dto.user import User
@@ -9,6 +13,8 @@ from server.connectors.base import BaseConnector
 
 logger = logging.getLogger(__name__)
 
+CLIENT_ID = os.getenv("REDDIT_CLIENT_ID")
+CLIENT_SECRET = os.getenv("REDDIT_CLIENT_SECRET")
 
 class RedditAPI(BaseConnector):
     source_name: str = "reddit"
@@ -18,6 +24,8 @@ class RedditAPI(BaseConnector):
 
     def __init__(self):
         self.url = "https://www.reddit.com/"
+        self.token = None
+        self.token_expiry = 0
 
     # Public Methods #
     def get_new_posts_by_search(
@@ -172,8 +180,43 @@ class RedditAPI(BaseConnector):
         user.karma = user_data["total_karma"]
         return user
     
+    def _get_token(self):
+        if self.token and time.time() < self.token_expiry:
+            return self.token
+
+        logger.info("Fetching new Reddit access token...")
+
+        auth = HTTPBasicAuth(CLIENT_ID, CLIENT_SECRET)
+
+        data = {
+            "grant_type": "client_credentials"
+        }
+
+        headers = {
+            "User-Agent": "python:ethnography-college-project:0.1 (by /u/ThisBirchWood)"
+        }
+
+        response = requests.post(
+            "https://www.reddit.com/api/v1/access_token",
+            auth=auth,
+            data=data,
+            headers=headers,
+        )
+
+        response.raise_for_status()
+        token_json = response.json()
+
+        self.token = token_json["access_token"]
+        self.token_expiry = time.time() + token_json["expires_in"] - 60
+
+        logger.info(
+            f"Obtained new Reddit access token (expires in {token_json['expires_in']}s)"
+        )
+
+        return self.token
+
     def _fetch_post_overviews(self, endpoint: str, params: dict) -> dict:
-        url = f"{self.url}{endpoint}"
+        url = f"https://oauth.reddit.com/{endpoint.lstrip('/')}"
         max_retries = 15
         backoff = 1  # seconds
 
@@ -182,13 +225,18 @@ class RedditAPI(BaseConnector):
                 response = requests.get(
                     url,
                     headers={
-                        "User-agent": "python:ethnography-college-project:0.1 (by /u/ThisBirchWood)"
+                        "User-agent": "python:ethnography-college-project:0.1 (by /u/ThisBirchWood)",
+                        "Authorization": f"Bearer {self._get_token()}",
                     },
                     params=params,
                 )
 
                 if response.status_code == 429:
-                    wait_time = response.headers.get("Retry-After", backoff)
+                    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
 
                     logger.warning(
                         f"Rate limited by Reddit API. Retrying in {wait_time} seconds..."

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(

server/core/datasets.py

@@ -26,7 +26,34 @@ class DatasetManager:
     def get_dataset_content(self, dataset_id: int) -> pd.DataFrame:
         query = "SELECT * FROM events WHERE dataset_id = %s"
         result = self.db.execute(query, (dataset_id,), fetch=True)
-        return pd.DataFrame(result)
+        df = pd.DataFrame(result)
+        if df.empty:
+            return df
+
+        dedupe_columns = [
+            column
+            for column in [
+                "post_id",
+                "parent_id",
+                "reply_to",
+                "author",
+                "type",
+                "timestamp",
+                "dt",
+                "title",
+                "content",
+                "source",
+                "topic",
+            ]
+            if column in df.columns
+        ]
+
+        if dedupe_columns:
+            df = df.drop_duplicates(subset=dedupe_columns, keep="first")
+        else:
+            df = df.drop_duplicates(keep="first")
+
+        return df.reset_index(drop=True)
 
     def get_dataset_info(self, dataset_id: int) -> dict:
         query = "SELECT * FROM datasets WHERE id = %s"
@@ -52,6 +79,16 @@ class DatasetManager:
         if event_data.empty:
             return
 
+        dedupe_columns = [
+            column for column in ["id", "type", "source"] if column in event_data.columns
+        ]
+        if dedupe_columns:
+            event_data = event_data.drop_duplicates(subset=dedupe_columns, keep="first")
+        else:
+            event_data = event_data.drop_duplicates(keep="first")
+
+        self.delete_dataset_content(dataset_id)
+
         query = """
             INSERT INTO events (
                 dataset_id,

server/queue/tasks.py

@@ -1,3 +1,5 @@
+from time import time
+
 import pandas as pd
 import logging
 
@@ -46,6 +48,7 @@ def fetch_and_process_dataset(
 
     try:
         for metadata in source_info:
+            fetch_start = time()
             name = metadata["name"]
             search = metadata.get("search")
             category = metadata.get("category")
@@ -57,8 +60,11 @@ def fetch_and_process_dataset(
             )
             posts.extend(post.to_dict() for post in raw_posts)
 
+        fetch_time = time() - fetch_start
         df = pd.DataFrame(posts)
 
+        nlp_start = time()
+
         dataset_manager.set_dataset_status(
             dataset_id, "processing", "NLP Processing Started"
         )
@@ -66,9 +72,11 @@ def fetch_and_process_dataset(
         processor = DatasetEnrichment(df, topics)
         enriched_df = processor.enrich()
 
+        nlp_time = time() - nlp_start
+
         dataset_manager.save_dataset_content(dataset_id, enriched_df)
         dataset_manager.set_dataset_status(
-            dataset_id, "complete", "NLP Processing Completed Successfully"
+            dataset_id, "complete", f"Completed Successfully. Fetch time: {fetch_time:.2f}s, NLP time: {nlp_time:.2f}s"
         )
     except Exception as e:
         dataset_manager.set_dataset_status(