From d44a716f9bac86880bb68c7df39ce9cd2176d131 Mon Sep 17 00:00:00 2001
From: HarshVadodariya <145587549+HarshVadodariya@users.noreply.github.com>
Date: Wed, 1 Apr 2026 22:46:50 +0530
Subject: [PATCH] Add files via upload
Used the notebook to discover the story,
story found.
spike logic tested.
diffusion tested.
bridge authors found.
topic clusters validated.
---
01_story_discovery.ipynb | 7419 ++++++++++++++++++++++++++++++++++++++
1 file changed, 7419 insertions(+)
create mode 100644 01_story_discovery.ipynb
diff --git a/01_story_discovery.ipynb b/01_story_discovery.ipynb
new file mode 100644
index 000000000..68abf6e1f
--- /dev/null
+++ b/01_story_discovery.ipynb
@@ -0,0 +1,7419 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "026709d2-4ed1-4da2-8eb6-fad50c4883a4",
+ "metadata": {},
+ "source": [
+ "## How do political narratives emerge in one ideological subreddit and amplify across others over time?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "7c0da77a-2c38-4fe2-b2d8-72d6b02bee7e",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# Core libraries\n",
+ "import json\n",
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "from datetime import datetime\n",
+ "\n",
+ "# Display settings for better inspection\n",
+ "pd.set_option(\"display.max_columns\", 50)\n",
+ "pd.set_option(\"display.max_colwidth\", 200)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "7f3d2443-a90d-40e3-8912-4e38c823374c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "✅ Loaded 8799 records\n",
+ "⚠️ Skipped 0 malformed rows\n"
+ ]
+ }
+ ],
+ "source": [
+ "def load_jsonl(filepath):\n",
+ " records = []\n",
+ " malformed_count = 0\n",
+ " \n",
+ " with open(filepath, 'r', encoding='utf-8') as f:\n",
+ " for i, line in enumerate(f):\n",
+ " try:\n",
+ " records.append(json.loads(line))\n",
+ " except json.JSONDecodeError:\n",
+ " malformed_count += 1\n",
+ " continue\n",
+ " \n",
+ " print(f\"✅ Loaded {len(records)} records\")\n",
+ " print(f\"⚠️ Skipped {malformed_count} malformed rows\")\n",
+ " \n",
+ " return records\n",
+ "\n",
+ "# Example usage\n",
+ "file_path = \"data.jsonl\"\n",
+ "raw_data = load_jsonl(file_path)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "c51e334e-f62e-468b-8830-3c8d53be6ea2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Normalized shape: (8799, 5235)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def normalize_data(records):\n",
+ " return pd.json_normalize(records, sep=\"_\")\n",
+ "\n",
+ "df_raw = normalize_data(raw_data)\n",
+ "\n",
+ "print(\"Normalized shape:\", df_raw.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "3148c1b5-e120-4012-98df-3ab935d52872",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def extract_columns(df):\n",
+ " def pick(*cols, default=np.nan):\n",
+ " for col in cols:\n",
+ " if col in df.columns:\n",
+ " return df[col]\n",
+ " return pd.Series([default] * len(df), index=df.index)\n",
+ "\n",
+ " clean_df = pd.DataFrame({\n",
+ " \"id\": pick(\"id\", \"data_id\"),\n",
+ " \"subreddit\": pick(\"subreddit\", \"data_subreddit\"),\n",
+ " \"author\": pick(\"author\", \"data_author\"),\n",
+ " \"title\": pick(\"title\", \"data_title\", default=\"\"),\n",
+ " \"selftext\": pick(\"selftext\", \"data_selftext\", default=\"\"),\n",
+ " \"created_utc\": pick(\"created_utc\", \"data_created_utc\"),\n",
+ " \"score\": pick(\"score\", \"data_score\", default=0),\n",
+ " \"num_comments\": pick(\"num_comments\", \"data_num_comments\", default=0),\n",
+ " \"url\": pick(\"url\", \"data_url\"),\n",
+ " \"crosspost_parent\": pick(\"crosspost_parent\", \"data_crosspost_parent\"),\n",
+ " \"num_crossposts\": pick(\"num_crossposts\", \"data_num_crossposts\", default=0),\n",
+ " })\n",
+ "\n",
+ " return clean_df\n",
+ "\n",
+ "\n",
+ "df = extract_columns(df_raw)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "345e9294-88a2-4429-8b50-3932b5935d1b",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/p3/v44286ln6_n4l9qyr4p480hc0000gn/T/ipykernel_15564/4110095374.py:4: DeprecationWarning: datetime.datetime.utcfromtimestamp() is deprecated and scheduled for removal in a future version. Use timezone-aware objects to represent datetimes in UTC: datetime.datetime.fromtimestamp(timestamp, datetime.UTC).\n",
+ " return datetime.utcfromtimestamp(float(ts))\n"
+ ]
+ }
+ ],
+ "source": [
+ "def parse_timestamps(df):\n",
+ " def safe_parse(ts):\n",
+ " try:\n",
+ " return datetime.utcfromtimestamp(float(ts))\n",
+ " except:\n",
+ " return pd.NaT\n",
+ "\n",
+ " df[\"created_utc\"] = df[\"created_utc\"].apply(safe_parse)\n",
+ " return df\n",
+ "\n",
+ "\n",
+ "df = parse_timestamps(df)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "4a7f27cd-bdaa-466b-9908-70e7f11f1cd5",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "✅ Cleaned dataset shape: (8799, 11)\n"
+ ]
+ }
+ ],
+ "source": [
+ "def clean_data(df):\n",
+ " # Fill missing text\n",
+ " df[\"title\"] = df[\"title\"].fillna(\"\")\n",
+ " df[\"selftext\"] = df[\"selftext\"].fillna(\"\")\n",
+ "\n",
+ " # Remove empty posts\n",
+ " df = df[\n",
+ " (df[\"title\"].str.strip() != \"\") |\n",
+ " (df[\"selftext\"].str.strip() != \"\")\n",
+ " ]\n",
+ "\n",
+ " # Remove invalid timestamps\n",
+ " df = df[~df[\"created_utc\"].isna()]\n",
+ "\n",
+ " # Remove missing subreddit (important for grouping)\n",
+ " df = df.dropna(subset=[\"subreddit\"])\n",
+ "\n",
+ " # Deduplicate (prefer id if available)\n",
+ " if \"id\" in df.columns:\n",
+ " df = df.drop_duplicates(subset=[\"id\"])\n",
+ " else:\n",
+ " df = df.drop_duplicates(subset=[\"title\", \"selftext\", \"author\"])\n",
+ "\n",
+ " # Reset index\n",
+ " df = df.reset_index(drop=True)\n",
+ "\n",
+ " return df\n",
+ "\n",
+ "\n",
+ "df = clean_data(df)\n",
+ "\n",
+ "print(\"✅ Cleaned dataset shape:\", df.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "9cb39b3a-0c65-4ff3-8f27-0b5e781d4d76",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Dataset Shape: (8799, 11)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Dataset Shape:\", df.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "e60057c7-afb8-48b0-95ec-da24ca787cb0",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Columns:\n",
+ "['id', 'subreddit', 'author', 'title', 'selftext', 'created_utc', 'score', 'num_comments', 'url', 'crosspost_parent', 'num_crossposts']\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(\"Columns:\")\n",
+ "print(df.columns.tolist())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "7d338c69-7fcf-480b-9961-6a9c6ef9414b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " Missing Count | \n",
+ " Missing % | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | crosspost_parent | \n",
+ " 8561 | \n",
+ " 97.295147 | \n",
+ "
\n",
+ " \n",
+ " | id | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
\n",
+ " \n",
+ " | subreddit | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " | author | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
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+ " \n",
+ " | title | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
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+ " \n",
+ " | selftext | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " | created_utc | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " | score | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " | num_comments | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " | url | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ " | num_crossposts | \n",
+ " 0 | \n",
+ " 0.000000 | \n",
+ "
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ " Missing Count Missing %\n",
+ "crosspost_parent 8561 97.295147\n",
+ "id 0 0.000000\n",
+ "subreddit 0 0.000000\n",
+ "author 0 0.000000\n",
+ "title 0 0.000000\n",
+ "selftext 0 0.000000\n",
+ "created_utc 0 0.000000\n",
+ "score 0 0.000000\n",
+ "num_comments 0 0.000000\n",
+ "url 0 0.000000\n",
+ "num_crossposts 0 0.000000"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "missing_summary = df.isnull().sum().sort_values(ascending=False)\n",
+ "missing_percent = (missing_summary / len(df)) * 100\n",
+ "\n",
+ "missing_df = pd.DataFrame({\n",
+ " \"Missing Count\": missing_summary,\n",
+ " \"Missing %\": missing_percent\n",
+ "})\n",
+ "\n",
+ "missing_df"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "b0e24940-2c6c-4488-a48e-3d962ffa4f71",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Top Subreddits:\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "subreddit\n",
+ "neoliberal 993\n",
+ "politics 993\n",
+ "worldpolitics 989\n",
+ "socialism 985\n",
+ "Liberal 984\n",
+ "Conservative 980\n",
+ "Anarchism 974\n",
+ "democrats 932\n",
+ "Republican 853\n",
+ "PoliticalDiscussion 116\n",
+ "Name: count, dtype: int64"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "top_subreddits = df[\"subreddit\"].value_counts().head(10)\n",
+ "\n",
+ "print(\"Top Subreddits:\")\n",
+ "top_subreddits"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "e26b87a5-f4e7-4475-a152-1a2c8dd8fa80",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/p3/v44286ln6_n4l9qyr4p480hc0000gn/T/ipykernel_15564/1582602367.py:4: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+ " .apply(lambda x: x.sample(min(len(x), n_samples)))\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " subreddit | \n",
+ " author | \n",
+ " title | \n",
+ " score | \n",
+ " num_comments | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 596 | \n",
+ " Anarchism | \n",
+ " Deathofimperialists | \n",
+ " Making an anarchist story | \n",
+ " 2 | \n",
+ " 0 | \n",
+ "
\n",
+ " \n",
+ " | 559 | \n",
+ " Anarchism | \n",
+ " CrimethInc-Ex-Worker | \n",
+ " A list of some of the events that will take place around the country in the days leading up to the presidential inauguration in connection with the \"Festivals of Resistance\" call to action. | \n",
+ " 52 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 1310 | \n",
+ " Conservative | \n",
+ " RedditUser19984321 | \n",
+ " San Francisco Bill Would Let People Sue Grocery Stores for Closing Too Soon(Article) | \n",
+ " 173 | \n",
+ " 53 | \n",
+ "
\n",
+ " \n",
+ " | 1761 | \n",
+ " Conservative | \n",
+ " Aggravating-Guest-12 | \n",
+ " Is there any place to find full episodes of Rush Limbaugh? | \n",
+ " 5 | \n",
+ " 7 | \n",
+ "
\n",
+ " \n",
+ " | 2690 | \n",
+ " Liberal | \n",
+ " Doom_Walker | \n",
+ " I don't understand the double standard. | \n",
+ " 235 | \n",
+ " 98 | \n",
+ "
\n",
+ " \n",
+ " | 2214 | \n",
+ " Liberal | \n",
+ " MantaRay2256 | \n",
+ " Remember the right wing 2009 TEA Party? Liberals need something just like that! | \n",
+ " 131 | \n",
+ " 73 | \n",
+ "
\n",
+ " \n",
+ " | 2945 | \n",
+ " PoliticalDiscussion | \n",
+ " BlockAffectionate413 | \n",
+ " Do you think any politician will in near future have as much personal loyalty and control over the party as Donald Trump does? | \n",
+ " 127 | \n",
+ " 113 | \n",
+ "
\n",
+ " \n",
+ " | 2954 | \n",
+ " PoliticalDiscussion | \n",
+ " Substantial-Soup-730 | \n",
+ " Is what Trump is doing the inevitable consequences of expanding the power of the executive branch over time? | \n",
+ " 0 | \n",
+ " 84 | \n",
+ "
\n",
+ " \n",
+ " | 3608 | \n",
+ " Republican | \n",
+ " Equivalent-Ad8645 | \n",
+ " Truth about Canadian tariffs | \n",
+ " 0 | \n",
+ " 49 | \n",
+ "
\n",
+ " \n",
+ " | 3765 | \n",
+ " Republican | \n",
+ " Equivalent-Ad8645 | \n",
+ " Here's Stephen Miller's Hilarious Exchange With CNN's Jake Tapper Over Federal Workers | \n",
+ " 10 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " subreddit author \\\n",
+ "596 Anarchism Deathofimperialists \n",
+ "559 Anarchism CrimethInc-Ex-Worker \n",
+ "1310 Conservative RedditUser19984321 \n",
+ "1761 Conservative Aggravating-Guest-12 \n",
+ "2690 Liberal Doom_Walker \n",
+ "2214 Liberal MantaRay2256 \n",
+ "2945 PoliticalDiscussion BlockAffectionate413 \n",
+ "2954 PoliticalDiscussion Substantial-Soup-730 \n",
+ "3608 Republican Equivalent-Ad8645 \n",
+ "3765 Republican Equivalent-Ad8645 \n",
+ "\n",
+ " title \\\n",
+ "596 Making an anarchist story \n",
+ "559 A list of some of the events that will take place around the country in the days leading up to the presidential inauguration in connection with the \"Festivals of Resistance\" call to action. \n",
+ "1310 San Francisco Bill Would Let People Sue Grocery Stores for Closing Too Soon(Article) \n",
+ "1761 Is there any place to find full episodes of Rush Limbaugh? \n",
+ "2690 I don't understand the double standard. \n",
+ "2214 Remember the right wing 2009 TEA Party? Liberals need something just like that! \n",
+ "2945 Do you think any politician will in near future have as much personal loyalty and control over the party as Donald Trump does? \n",
+ "2954 Is what Trump is doing the inevitable consequences of expanding the power of the executive branch over time? \n",
+ "3608 Truth about Canadian tariffs \n",
+ "3765 Here's Stephen Miller's Hilarious Exchange With CNN's Jake Tapper Over Federal Workers \n",
+ "\n",
+ " score num_comments \n",
+ "596 2 0 \n",
+ "559 52 3 \n",
+ "1310 173 53 \n",
+ "1761 5 7 \n",
+ "2690 235 98 \n",
+ "2214 131 73 \n",
+ "2945 127 113 \n",
+ "2954 0 84 \n",
+ "3608 0 49 \n",
+ "3765 10 1 "
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "def sample_by_subreddit(df, n_subreddits=5, n_samples=2):\n",
+ " sampled = (\n",
+ " df.groupby(\"subreddit\", group_keys=False)\n",
+ " .apply(lambda x: x.sample(min(len(x), n_samples)))\n",
+ " )\n",
+ " return sampled.head(n_subreddits * n_samples)\n",
+ "\n",
+ "\n",
+ "sample_df = sample_by_subreddit(df)\n",
+ "\n",
+ "sample_df[[\"subreddit\", \"author\", \"title\", \"score\", \"num_comments\"]]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4d66f328-9714-4ef6-a323-28649a0ee898",
+ "metadata": {},
+ "source": [
+ "### Prepare Time-Series Data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "id": "bc9906e1-72ae-429a-8c38-e0ff197759d2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Time-series shape: (575, 3)\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/var/folders/p3/v44286ln6_n4l9qyr4p480hc0000gn/T/ipykernel_15564/3889139319.py:28: DeprecationWarning: DataFrameGroupBy.apply operated on the grouping columns. This behavior is deprecated, and in a future version of pandas the grouping columns will be excluded from the operation. Either pass `include_groups=False` to exclude the groupings or explicitly select the grouping columns after groupby to silence this warning.\n",
+ " .apply(fill_missing_dates)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Ensure datetime format (timezone-safe)\n",
+ "df[\"created_utc\"] = pd.to_datetime(df[\"created_utc\"], utc=True, errors=\"coerce\")\n",
+ "\n",
+ "# Extract date (normalized to UTC date)\n",
+ "df[\"date\"] = df[\"created_utc\"].dt.floor(\"D\")\n",
+ "\n",
+ "# Drop invalid dates\n",
+ "df = df.dropna(subset=[\"date\", \"subreddit\"])\n",
+ "\n",
+ "# Group: daily post count per subreddit\n",
+ "ts_df = (\n",
+ " df.groupby([\"subreddit\", \"date\"])\n",
+ " .size()\n",
+ " .reset_index(name=\"post_count\")\n",
+ ")\n",
+ "\n",
+ "# Create full date range per subreddit (handle missing dates)\n",
+ "def fill_missing_dates(group):\n",
+ " full_range = pd.date_range(group[\"date\"].min(), group[\"date\"].max(), freq=\"D\")\n",
+ " group = group.set_index(\"date\").reindex(full_range, fill_value=0)\n",
+ " group.index.name = \"date\"\n",
+ " group = group.reset_index()\n",
+ " group[\"subreddit\"] = group[\"subreddit\"].iloc[0]\n",
+ " return group\n",
+ "\n",
+ "ts_df = (\n",
+ " ts_df.groupby(\"subreddit\", group_keys=False)\n",
+ " .apply(fill_missing_dates)\n",
+ " .reset_index(drop=True)\n",
+ ")\n",
+ "\n",
+ "print(\"Time-series shape:\", ts_df.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "id": "0a093b79-4b7a-4b3a-9855-8ec15a1563f3",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#Rolling Average (7-day)\n",
+ "# Sort for rolling calculation\n",
+ "ts_df = ts_df.sort_values([\"subreddit\", \"date\"])\n",
+ "\n",
+ "# 7-day rolling average\n",
+ "ts_df[\"rolling_avg_7d\"] = (\n",
+ " ts_df.groupby(\"subreddit\")[\"post_count\"]\n",
+ " .transform(lambda x: x.rolling(window=7, min_periods=1).mean())\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "a27e5478-47d3-4cb8-86eb-3dd0fe2fd4af",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total spikes detected: 42\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " subreddit | \n",
+ " spike_date | \n",
+ " post_count | \n",
+ " prev_avg | \n",
+ " spike_magnitude | \n",
+ " pct_increase | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Anarchism | \n",
+ " 2024-11-07 00:00:00+00:00 | \n",
+ " 14 | \n",
+ " 1.000000 | \n",
+ " 13.000000 | \n",
+ " 13.000000 | \n",
+ "
\n",
+ " \n",
+ " | 28 | \n",
+ " Anarchism | \n",
+ " 2024-12-05 00:00:00+00:00 | \n",
+ " 10 | \n",
+ " 4.857143 | \n",
+ " 5.142857 | \n",
+ " 1.058824 | \n",
+ "
\n",
+ " \n",
+ " | 32 | \n",
+ " Anarchism | \n",
+ " 2024-12-09 00:00:00+00:00 | \n",
+ " 19 | \n",
+ " 7.000000 | \n",
+ " 12.000000 | \n",
+ " 1.714286 | \n",
+ "
\n",
+ " \n",
+ " | 88 | \n",
+ " Anarchism | \n",
+ " 2025-02-03 00:00:00+00:00 | \n",
+ " 30 | \n",
+ " 14.000000 | \n",
+ " 16.000000 | \n",
+ " 1.142857 | \n",
+ "
\n",
+ " \n",
+ " | 104 | \n",
+ " Conservative | \n",
+ " 2025-02-11 00:00:00+00:00 | \n",
+ " 74 | \n",
+ " 1.000000 | \n",
+ " 73.000000 | \n",
+ " 73.000000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
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+ "text/plain": [
+ " subreddit spike_date post_count prev_avg \\\n",
+ "0 Anarchism 2024-11-07 00:00:00+00:00 14 1.000000 \n",
+ "28 Anarchism 2024-12-05 00:00:00+00:00 10 4.857143 \n",
+ "32 Anarchism 2024-12-09 00:00:00+00:00 19 7.000000 \n",
+ "88 Anarchism 2025-02-03 00:00:00+00:00 30 14.000000 \n",
+ "104 Conservative 2025-02-11 00:00:00+00:00 74 1.000000 \n",
+ "\n",
+ " spike_magnitude pct_increase \n",
+ "0 13.000000 13.000000 \n",
+ "28 5.142857 1.058824 \n",
+ "32 12.000000 1.714286 \n",
+ "88 16.000000 1.142857 \n",
+ "104 73.000000 73.000000 "
+ ]
+ },
+ "execution_count": 16,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Detect Spikes\n",
+ "def detect_spikes(df, threshold=1.0):\n",
+ " df = df.copy()\n",
+ "\n",
+ " # Previous rolling average (shifted)\n",
+ " df[\"prev_avg\"] = (\n",
+ " df.groupby(\"subreddit\")[\"rolling_avg_7d\"]\n",
+ " .shift(1)\n",
+ " )\n",
+ "\n",
+ " # Avoid division issues\n",
+ " df[\"prev_avg\"] = df[\"prev_avg\"].fillna(1)\n",
+ "\n",
+ " # Percent increase\n",
+ " df[\"pct_increase\"] = (\n",
+ " (df[\"post_count\"] - df[\"prev_avg\"]) / df[\"prev_avg\"]\n",
+ " )\n",
+ "\n",
+ " # Spike condition\n",
+ " df[\"is_spike\"] = df[\"pct_increase\"] > threshold\n",
+ "\n",
+ " return df\n",
+ "\n",
+ "\n",
+ "ts_df = detect_spikes(ts_df)\n",
+ "\n",
+ "# Extract spike dataframe\n",
+ "spike_df = ts_df[ts_df[\"is_spike\"]].copy()\n",
+ "\n",
+ "# Add magnitude column\n",
+ "spike_df[\"spike_magnitude\"] = spike_df[\"post_count\"] - spike_df[\"prev_avg\"]\n",
+ "\n",
+ "spike_df = spike_df[[\n",
+ " \"subreddit\",\n",
+ " \"date\",\n",
+ " \"post_count\",\n",
+ " \"prev_avg\",\n",
+ " \"spike_magnitude\",\n",
+ " \"pct_increase\"\n",
+ "]].rename(columns={\"date\": \"spike_date\"})\n",
+ "\n",
+ "print(\"Total spikes detected:\", len(spike_df))\n",
+ "spike_df.head()"
+ ]
+ },
+ {
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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "#Interactive Plot (Plotly)\n",
+ "import plotly.express as px\n",
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "# Limit to top subreddits (for readability)\n",
+ "top_subs = spike_df[\"subreddit\"].value_counts().head(5).index\n",
+ "plot_df = ts_df[ts_df[\"subreddit\"].isin(top_subs)]\n",
+ "\n",
+ "fig = px.line(\n",
+ " plot_df,\n",
+ " x=\"date\",\n",
+ " y=\"post_count\",\n",
+ " color=\"subreddit\",\n",
+ " title=\"Community Posting Volume Over Time\"\n",
+ ")\n",
+ "\n",
+ "# Add rolling average toggle (optional visualization)\n",
+ "for sub in top_subs:\n",
+ " sub_df = plot_df[plot_df[\"subreddit\"] == sub]\n",
+ " fig.add_trace(go.Scatter(\n",
+ " x=sub_df[\"date\"],\n",
+ " y=sub_df[\"rolling_avg_7d\"],\n",
+ " mode=\"lines\",\n",
+ " name=f\"{sub} (7d avg)\",\n",
+ " line=dict(dash=\"dash\")\n",
+ " ))\n",
+ "\n",
+ "# Highlight spikes\n",
+ "spike_plot = spike_df[spike_df[\"subreddit\"].isin(top_subs)]\n",
+ "\n",
+ "fig.add_trace(go.Scatter(\n",
+ " x=spike_plot[\"spike_date\"],\n",
+ " y=spike_plot[\"post_count\"],\n",
+ " mode=\"markers\",\n",
+ " name=\"Spikes\",\n",
+ " marker=dict(size=8, symbol=\"x\")\n",
+ "))\n",
+ "\n",
+ "fig.update_layout(\n",
+ " xaxis_title=\"Date\",\n",
+ " yaxis_title=\"Daily Post Count\",\n",
+ " hovermode=\"x unified\"\n",
+ ")\n",
+ "\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2b14a494-ad1f-4a04-96f6-0349f880b29b",
+ "metadata": {},
+ "source": [
+ "### Why Spikes Matter?"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8c56af16-a894-4e40-a209-96072bb1f4ef",
+ "metadata": {},
+ "source": [
+ "Spikes in posting activity often indicate:\n",
+ "- Emergence of a new political narrative\n",
+ "- Viral amplification of an existing topic\n",
+ "- Cross-community propagation events\n",
+ "\n",
+ "By tracking spikes across subreddits, we can:\n",
+ "- Identify **which communities initiate discussions**\n",
+ "- Detect **which communities amplify narratives later**\n",
+ "- Understand **information flow across Reddit ecosystems**\n",
+ "\n",
+ "This is critical for analyzing narrative diffusion and influence patterns."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "92aff167-0949-4d33-b4d2-3a4f44d16dfe",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Unique subreddits: 10\n",
+ "Date range: 2024-07-23 00:00:00+00:00 → 2025-02-18 00:00:00+00:00\n",
+ "Subreddits with only 1 day of data: 0\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Edge Case Checks\n",
+ "print(\"Unique subreddits:\", df[\"subreddit\"].nunique())\n",
+ "print(\"Date range:\", ts_df[\"date\"].min(), \"→\", ts_df[\"date\"].max())\n",
+ "\n",
+ "# Check for single-day subreddits\n",
+ "single_day = ts_df.groupby(\"subreddit\")[\"date\"].nunique()\n",
+ "print(\"Subreddits with only 1 day of data:\", (single_day == 1).sum())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "d9a2d004-7763-40c2-bf15-ae2b8b15b077",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "🏁 Subreddit that spikes first most often:\n",
+ "subreddit\n",
+ "Anarchism 1\n",
+ "Name: count, dtype: int64\n",
+ "\n",
+ "🔥 Subreddit with highest average spike magnitude:\n",
+ "subreddit\n",
+ "politics 298.0\n",
+ "Name: spike_magnitude, dtype: float64\n"
+ ]
+ }
+ ],
+ "source": [
+ "# First spike per subreddit\n",
+ "first_spikes = (\n",
+ " spike_df.sort_values(\"spike_date\")\n",
+ " .groupby(\"subreddit\")\n",
+ " .first()\n",
+ " .reset_index()\n",
+ ")\n",
+ "\n",
+ "# Count earliest spikes\n",
+ "earliest_counts = first_spikes[\"subreddit\"].value_counts()\n",
+ "\n",
+ "# Highest average spike magnitude\n",
+ "avg_spike = (\n",
+ " spike_df.groupby(\"subreddit\")[\"spike_magnitude\"]\n",
+ " .mean()\n",
+ " .sort_values(ascending=False)\n",
+ ")\n",
+ "\n",
+ "print(\"🏁 Subreddit that spikes first most often:\")\n",
+ "print(earliest_counts.head(1))\n",
+ "\n",
+ "print(\"\\n🔥 Subreddit with highest average spike magnitude:\")\n",
+ "print(avg_spike.head(1))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "5a18bd90-a1c7-4417-8652-24c363ebb41a",
+ "metadata": {},
+ "source": [
+ "### Cross-Community Narrative Spread Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "26c060ca-0898-4bd3-9391-e9022143044e",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Total crossposts: 238\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Filter crossposts\n",
+ "crossposts = df.dropna(subset=[\"crosspost_parent\"]).copy()\n",
+ "\n",
+ "print(\"Total crossposts:\", len(crossposts))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "f7d17537-f4fd-411c-a154-b757ac41d8ff",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Crossposts before mapping: 238\n",
+ "Crossposts after mapping: 238\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Create mapping from post id → subreddit\n",
+ "id_to_subreddit = df.set_index(\"id\")[\"subreddit\"].to_dict()\n",
+ "\n",
+ "def extract_source_subreddit(parent_id):\n",
+ " try:\n",
+ " # Format: t3_\n",
+ " clean_id = parent_id.split(\"_\")[-1]\n",
+ " return id_to_subreddit.get(clean_id, np.nan)\n",
+ " except:\n",
+ " return np.nan\n",
+ "\n",
+ "crossposts[\"source_subreddit\"] = crossposts[\"crosspost_parent\"].apply(extract_source_subreddit)\n",
+ "crossposts[\"destination_subreddit\"] = crossposts[\"subreddit\"]\n",
+ "\n",
+ "# Drop unresolved mappings\n",
+ "crossposts[\"source_subreddit\"] = crossposts[\"source_subreddit\"].fillna(\"unknown_source\")\n",
+ "\n",
+ "print(\"Crossposts before mapping:\", len(df.dropna(subset=[\"crosspost_parent\"])))\n",
+ "print(\"Crossposts after mapping:\", len(crossposts))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "5547d4c1-7d12-41d1-8368-1cc2e564ad84",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Crosspost flow rows: 5\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " source_subreddit | \n",
+ " destination_subreddit | \n",
+ " number_of_crossposts | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " unknown_source | \n",
+ " Anarchism | \n",
+ " 134 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " unknown_source | \n",
+ " Liberal | \n",
+ " 30 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " unknown_source | \n",
+ " Republican | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " unknown_source | \n",
+ " neoliberal | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " unknown_source | \n",
+ " socialism | \n",
+ " 72 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " source_subreddit destination_subreddit number_of_crossposts\n",
+ "0 unknown_source Anarchism 134\n",
+ "1 unknown_source Liberal 30\n",
+ "2 unknown_source Republican 1\n",
+ "3 unknown_source neoliberal 1\n",
+ "4 unknown_source socialism 72"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "crosspost_flow = (\n",
+ " crossposts.groupby([\"source_subreddit\", \"destination_subreddit\"])\n",
+ " .size()\n",
+ " .reset_index(name=\"number_of_crossposts\")\n",
+ ")\n",
+ "\n",
+ "print(\"Crosspost flow rows:\", len(crosspost_flow))\n",
+ "crosspost_flow.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "id": "b91a5183-1cc2-47ae-ab31-025f36dbbf70",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from urllib.parse import urlparse\n",
+ "\n",
+ "def extract_domain(url):\n",
+ " try:\n",
+ " return urlparse(url).netloc.lower()\n",
+ " except:\n",
+ " return np.nan\n",
+ "\n",
+ "df[\"domain\"] = df[\"url\"].apply(extract_domain)\n",
+ "\n",
+ "# Remove empty or malformed\n",
+ "df_urls = df.dropna(subset=[\"domain\"])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "id": "f452e653-e093-46d4-a871-04f3378c8861",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " url | \n",
+ " subreddit | \n",
+ " domain | \n",
+ " frequency | \n",
+ " num_subreddits | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 242 | \n",
+ " https://apnews.com/article/doge-faa-air-traffic-firings-safety-67981aec33b6ee72cbad8dcee31f3437 | \n",
+ " [neoliberal, democrats, Liberal, politics] | \n",
+ " apnews.com | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " | 7396 | \n",
+ " https://www.reuters.com/world/us/musk-aides-lock-government-workers-out-computer-systems-us-agency-sources-say-2025-01-31/ | \n",
+ " [democrats, Republican] | \n",
+ " www.reuters.com | \n",
+ " 4 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " | 4548 | \n",
+ " https://www.nbcnews.com/politics/national-security/trump-administration-wants-un-fire-nuclear-safety-workers-cant-figure-rcna192345 | \n",
+ " [neoliberal, Liberal, politics, Republican] | \n",
+ " www.nbcnews.com | \n",
+ " 4 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " | 2908 | \n",
+ " https://thehill.com/homenews/senate/5135759-whistleblower-portal-trump-administration/ | \n",
+ " [neoliberal, democrats] | \n",
+ " thehill.com | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " | 3873 | \n",
+ " https://www.dailywire.com/news/joe-biden-set-a-new-record-while-he-was-president-and-its-not-a-good-one | \n",
+ " [Republican, Conservative, politics] | \n",
+ " www.dailywire.com | \n",
+ " 3 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 7692 | \n",
+ " https://www.theguardian.com/us-news/2025/feb/06/ice-us-immigration-deportations-google | \n",
+ " [neoliberal, democrats] | \n",
+ " www.theguardian.com | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " | 2631 | \n",
+ " https://pjmedia.com/matt-margolis/2025/02/15/boom-doge-gets-a-sweet-victory-in-court-n4937010 | \n",
+ " [Republican, Conservative] | \n",
+ " pjmedia.com | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ " | 4533 | \n",
+ " https://www.nbcnews.com/politics/elections/wisconsin-supreme-court-race-2025-battleground-state-rcna191726 | \n",
+ " [neoliberal, democrats, politics] | \n",
+ " www.nbcnews.com | \n",
+ " 3 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 4956 | \n",
+ " https://www.politico.com/news/2025/02/16/trump-administration-firings-bird-flu-response-00204542 | \n",
+ " [neoliberal, democrats, politics] | \n",
+ " www.politico.com | \n",
+ " 3 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 536 | \n",
+ " https://dailycaller.com/2025/02/15/the-lefts-sue-everything-that-moves-strategy-may-end-up-delivering-trump-ultimate-victory/ | \n",
+ " [Republican, Conservative] | \n",
+ " dailycaller.com | \n",
+ " 3 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " url \\\n",
+ "242 https://apnews.com/article/doge-faa-air-traffic-firings-safety-67981aec33b6ee72cbad8dcee31f3437 \n",
+ "7396 https://www.reuters.com/world/us/musk-aides-lock-government-workers-out-computer-systems-us-agency-sources-say-2025-01-31/ \n",
+ "4548 https://www.nbcnews.com/politics/national-security/trump-administration-wants-un-fire-nuclear-safety-workers-cant-figure-rcna192345 \n",
+ "2908 https://thehill.com/homenews/senate/5135759-whistleblower-portal-trump-administration/ \n",
+ "3873 https://www.dailywire.com/news/joe-biden-set-a-new-record-while-he-was-president-and-its-not-a-good-one \n",
+ "7692 https://www.theguardian.com/us-news/2025/feb/06/ice-us-immigration-deportations-google \n",
+ "2631 https://pjmedia.com/matt-margolis/2025/02/15/boom-doge-gets-a-sweet-victory-in-court-n4937010 \n",
+ "4533 https://www.nbcnews.com/politics/elections/wisconsin-supreme-court-race-2025-battleground-state-rcna191726 \n",
+ "4956 https://www.politico.com/news/2025/02/16/trump-administration-firings-bird-flu-response-00204542 \n",
+ "536 https://dailycaller.com/2025/02/15/the-lefts-sue-everything-that-moves-strategy-may-end-up-delivering-trump-ultimate-victory/ \n",
+ "\n",
+ " subreddit domain \\\n",
+ "242 [neoliberal, democrats, Liberal, politics] apnews.com \n",
+ "7396 [democrats, Republican] www.reuters.com \n",
+ "4548 [neoliberal, Liberal, politics, Republican] www.nbcnews.com \n",
+ "2908 [neoliberal, democrats] thehill.com \n",
+ "3873 [Republican, Conservative, politics] www.dailywire.com \n",
+ "7692 [neoliberal, democrats] www.theguardian.com \n",
+ "2631 [Republican, Conservative] pjmedia.com \n",
+ "4533 [neoliberal, democrats, politics] www.nbcnews.com \n",
+ "4956 [neoliberal, democrats, politics] www.politico.com \n",
+ "536 [Republican, Conservative] dailycaller.com \n",
+ "\n",
+ " frequency num_subreddits \n",
+ "242 4 4 \n",
+ "7396 4 2 \n",
+ "4548 4 4 \n",
+ "2908 3 2 \n",
+ "3873 3 3 \n",
+ "7692 3 2 \n",
+ "2631 3 2 \n",
+ "4533 3 3 \n",
+ "4956 3 3 \n",
+ "536 3 2 "
+ ]
+ },
+ "execution_count": 27,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "url_group = (\n",
+ " df_urls.groupby(\"url\")\n",
+ " .agg({\n",
+ " \"subreddit\": lambda x: list(set(x)),\n",
+ " \"domain\": \"first\",\n",
+ " \"url\": \"count\"\n",
+ " })\n",
+ " .rename(columns={\"url\": \"frequency\"})\n",
+ " .reset_index()\n",
+ ")\n",
+ "\n",
+ "# Filter URLs shared across 2+ subreddits\n",
+ "url_group[\"num_subreddits\"] = url_group[\"subreddit\"].apply(len)\n",
+ "\n",
+ "cross_community_urls = url_group[url_group[\"num_subreddits\"] >= 2]\n",
+ "\n",
+ "cross_community_urls = cross_community_urls.sort_values(\"frequency\", ascending=False)\n",
+ "\n",
+ "cross_community_urls.head(10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 28,
+ "id": "a4567be7-9cca-4571-a99c-6e82201d6dea",
+ "metadata": {},
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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotly.express as px\n",
+ "\n",
+ "# Limit to top flows for readability\n",
+ "top_sources = crosspost_flow[\"source_subreddit\"].value_counts().head(10).index\n",
+ "\n",
+ "heatmap_df = crosspost_flow[\n",
+ " crosspost_flow[\"source_subreddit\"].isin(top_sources)\n",
+ "]\n",
+ "\n",
+ "fig = px.density_heatmap(\n",
+ " heatmap_df,\n",
+ " x=\"destination_subreddit\",\n",
+ " y=\"source_subreddit\",\n",
+ " z=\"number_of_crossposts\",\n",
+ " title=\"Cross-Community Narrative Flow (Crossposts)\",\n",
+ ")\n",
+ "\n",
+ "fig.update_layout(xaxis_title=\"Destination\", yaxis_title=\"Source\")\n",
+ "\n",
+ "fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "id": "e05095ba-5fb5-4642-a2b0-2eb2f57428b3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Nodes: 6\n",
+ "Edges: 5\n"
+ ]
+ }
+ ],
+ "source": [
+ "import networkx as nx\n",
+ "\n",
+ "G = nx.DiGraph()\n",
+ "\n",
+ "for _, row in crosspost_flow.head(50).iterrows():\n",
+ " G.add_edge(\n",
+ " row[\"source_subreddit\"],\n",
+ " row[\"destination_subreddit\"],\n",
+ " weight=row[\"number_of_crossposts\"]\n",
+ " )\n",
+ "\n",
+ "print(\"Nodes:\", G.number_of_nodes())\n",
+ "print(\"Edges:\", G.number_of_edges())"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "61b1023d-889a-4482-a681-7f64e4d5c0c9",
+ "metadata": {},
+ "source": [
+ "## Author Overlap and Narrative Bridge Analysis"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 31,
+ "id": "c6494cb3-c4aa-497f-a6de-61f8b5317b84",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Valid authors: 3597\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Remove deleted/invalid users\n",
+ "df_auth = df.copy()\n",
+ "\n",
+ "df_auth = df_auth[\n",
+ " (df_auth[\"author\"].notna()) &\n",
+ " (~df_auth[\"author\"].isin([\"[deleted]\", \"[removed]\", \"AutoModerator\"]))\n",
+ "]\n",
+ "\n",
+ "print(\"Valid authors:\", df_auth[\"author\"].nunique())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 32,
+ "id": "eca897bf-e189-4d84-98d3-63ac2a9b74e6",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " author | \n",
+ " num_subreddits | \n",
+ " total_posts | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " -------7654321 | \n",
+ " 1 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " -Clayburn | \n",
+ " 1 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " -Erase | \n",
+ " 1 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " -Jacob-Seed- | \n",
+ " 1 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " -Konrad- | \n",
+ " 1 | \n",
+ " 1 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " author num_subreddits total_posts\n",
+ "0 -------7654321 1 1\n",
+ "1 -Clayburn 1 1\n",
+ "2 -Erase 1 4\n",
+ "3 -Jacob-Seed- 1 1\n",
+ "4 -Konrad- 1 1"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "author_stats = (\n",
+ " df_auth.groupby(\"author\")\n",
+ " .agg(\n",
+ " num_subreddits=(\"subreddit\", \"nunique\"),\n",
+ " total_posts=(\"subreddit\", \"count\")\n",
+ " )\n",
+ " .reset_index()\n",
+ ")\n",
+ "\n",
+ "author_stats.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 33,
+ "id": "d22195d0-ee38-40c9-8163-61e0a7c748b2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Authors active in 2+ subreddits: 86\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " author | \n",
+ " num_subreddits | \n",
+ " total_posts | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 14 | \n",
+ " 1Rab | \n",
+ " 2 | \n",
+ " 8 | \n",
+ "
\n",
+ " \n",
+ " | 86 | \n",
+ " Advanced-Hat2338 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 180 | \n",
+ " Antique-Entrance-229 | \n",
+ " 2 | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 209 | \n",
+ " Ask4MD | \n",
+ " 2 | \n",
+ " 137 | \n",
+ "
\n",
+ " \n",
+ " | 211 | \n",
+ " AskandThink | \n",
+ " 2 | \n",
+ " 2 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " author num_subreddits total_posts\n",
+ "14 1Rab 2 8\n",
+ "86 Advanced-Hat2338 2 3\n",
+ "180 Antique-Entrance-229 2 3\n",
+ "209 Ask4MD 2 137\n",
+ "211 AskandThink 2 2"
+ ]
+ },
+ "execution_count": 33,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "bridge_authors = author_stats[author_stats[\"num_subreddits\"] >= 2]\n",
+ "\n",
+ "print(\"Authors active in 2+ subreddits:\", len(bridge_authors))\n",
+ "bridge_authors.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "id": "90f012c2-9875-46f2-bae9-d187956dfce9",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "author_subs = (\n",
+ " df_auth.groupby(\"author\")[\"subreddit\"]\n",
+ " .apply(lambda x: list(set(x)))\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "id": "4d0482ce-cd00-4189-a264-0ac5bf05cc38",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " subreddit_1 | \n",
+ " subreddit_2 | \n",
+ " shared_authors | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " neoliberal | \n",
+ " democrats | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " democrats | \n",
+ " neoliberal | \n",
+ " 3 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " Anarchism | \n",
+ " socialism | \n",
+ " 13 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " socialism | \n",
+ " Anarchism | \n",
+ " 13 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " neoliberal | \n",
+ " politics | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " subreddit_1 subreddit_2 shared_authors\n",
+ "0 neoliberal democrats 3\n",
+ "1 democrats neoliberal 3\n",
+ "2 Anarchism socialism 13\n",
+ "3 socialism Anarchism 13\n",
+ "4 neoliberal politics 4"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "from collections import defaultdict\n",
+ "import itertools\n",
+ "\n",
+ "overlap_counts = defaultdict(int)\n",
+ "\n",
+ "for subs in author_subs:\n",
+ " if len(subs) < 2:\n",
+ " continue\n",
+ " for a, b in itertools.combinations(subs, 2):\n",
+ " overlap_counts[(a, b)] += 1\n",
+ " overlap_counts[(b, a)] += 1 # symmetric\n",
+ "\n",
+ "# Convert to DataFrame\n",
+ "overlap_df = pd.DataFrame([\n",
+ " {\"subreddit_1\": k[0], \"subreddit_2\": k[1], \"shared_authors\": v}\n",
+ " for k, v in overlap_counts.items()\n",
+ "])\n",
+ "\n",
+ "overlap_df.head()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "id": "63b80ee1-6bbb-4b62-958c-5bb10621658f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
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+ " Conservative | \n",
+ " Liberal | \n",
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+ " democrats | \n",
+ " neoliberal | \n",
+ " politics | \n",
+ " socialism | \n",
+ " worldpolitics | \n",
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+ " 0.0 | \n",
+ " 0.0 | \n",
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+ " 16.0 | \n",
+ " 2.0 | \n",
+ " 1.0 | \n",
+ "
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+ " \n",
+ " | neoliberal | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
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+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 3.0 | \n",
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+ " \n",
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+ " 1.0 | \n",
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+ " 16.0 | \n",
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+ " 0.0 | \n",
+ "
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+ " \n",
+ " | socialism | \n",
+ " 13.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 1.0 | \n",
+ " 0.0 | \n",
+ " 2.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ "
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+ " \n",
+ " | worldpolitics | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 0.0 | \n",
+ " 1.0 | \n",
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ "subreddit_2 Anarchism Conservative Liberal PoliticalDiscussion \\\n",
+ "subreddit_1 \n",
+ "Anarchism 0.0 0.0 1.0 0.0 \n",
+ "Conservative 0.0 0.0 0.0 0.0 \n",
+ "Liberal 1.0 0.0 0.0 1.0 \n",
+ "PoliticalDiscussion 0.0 0.0 1.0 0.0 \n",
+ "Republican 0.0 19.0 0.0 0.0 \n",
+ "democrats 1.0 1.0 17.0 0.0 \n",
+ "neoliberal 0.0 0.0 1.0 0.0 \n",
+ "politics 1.0 9.0 6.0 1.0 \n",
+ "socialism 13.0 0.0 0.0 1.0 \n",
+ "worldpolitics 0.0 0.0 0.0 0.0 \n",
+ "\n",
+ "subreddit_2 Republican democrats neoliberal politics socialism \\\n",
+ "subreddit_1 \n",
+ "Anarchism 0.0 1.0 0.0 1.0 13.0 \n",
+ "Conservative 19.0 1.0 0.0 9.0 0.0 \n",
+ "Liberal 0.0 17.0 1.0 6.0 0.0 \n",
+ "PoliticalDiscussion 0.0 0.0 0.0 1.0 1.0 \n",
+ "Republican 0.0 0.0 0.0 0.0 0.0 \n",
+ "democrats 0.0 0.0 3.0 16.0 2.0 \n",
+ "neoliberal 0.0 3.0 0.0 4.0 0.0 \n",
+ "politics 0.0 16.0 4.0 0.0 0.0 \n",
+ "socialism 0.0 2.0 0.0 0.0 0.0 \n",
+ "worldpolitics 0.0 1.0 0.0 0.0 0.0 \n",
+ "\n",
+ "subreddit_2 worldpolitics \n",
+ "subreddit_1 \n",
+ "Anarchism 0.0 \n",
+ "Conservative 0.0 \n",
+ "Liberal 0.0 \n",
+ "PoliticalDiscussion 0.0 \n",
+ "Republican 0.0 \n",
+ "democrats 1.0 \n",
+ "neoliberal 0.0 \n",
+ "politics 0.0 \n",
+ "socialism 0.0 \n",
+ "worldpolitics 0.0 "
+ ]
+ },
+ "execution_count": 36,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "overlap_matrix = overlap_df.pivot_table(\n",
+ " index=\"subreddit_1\",\n",
+ " columns=\"subreddit_2\",\n",
+ " values=\"shared_authors\",\n",
+ " fill_value=0\n",
+ ")\n",
+ "\n",
+ "overlap_matrix"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "id": "a258fe02-2918-451b-ad35-e79db95663cd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
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+ " \n",
+ " | socialism | \n",
+ " 0.026052 | \n",
+ " 0.000000 | \n",
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+ " \n",
+ "
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+ "
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+ ],
+ "text/plain": [
+ "subreddit_2 Anarchism Conservative Liberal PoliticalDiscussion \\\n",
+ "subreddit_1 \n",
+ "Anarchism 0.000000 0.000000 0.001773 0.000000 \n",
+ "Conservative 0.000000 0.000000 0.000000 0.000000 \n",
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+ "socialism 0.026052 0.000000 0.000000 0.002004 \n",
+ "worldpolitics 0.000000 0.000000 0.000000 0.000000 \n",
+ "\n",
+ "subreddit_2 Republican democrats neoliberal politics socialism \\\n",
+ "subreddit_1 \n",
+ "Anarchism 0.000000 0.001773 0.000000 0.001773 0.023050 \n",
+ "Conservative 0.044393 0.002336 0.000000 0.021028 0.000000 \n",
+ "Liberal 0.000000 0.036797 0.002165 0.012987 0.000000 \n",
+ "PoliticalDiscussion 0.000000 0.000000 0.000000 0.009901 0.009901 \n",
+ "Republican 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
+ "democrats 0.000000 0.000000 0.010453 0.055749 0.006969 \n",
+ "neoliberal 0.000000 0.008242 0.000000 0.010989 0.000000 \n",
+ "politics 0.000000 0.032587 0.008147 0.000000 0.000000 \n",
+ "socialism 0.000000 0.004008 0.000000 0.000000 0.000000 \n",
+ "worldpolitics 0.000000 0.003802 0.000000 0.000000 0.000000 \n",
+ "\n",
+ "subreddit_2 worldpolitics \n",
+ "subreddit_1 \n",
+ "Anarchism 0.000000 \n",
+ "Conservative 0.000000 \n",
+ "Liberal 0.000000 \n",
+ "PoliticalDiscussion 0.000000 \n",
+ "Republican 0.000000 \n",
+ "democrats 0.003484 \n",
+ "neoliberal 0.000000 \n",
+ "politics 0.000000 \n",
+ "socialism 0.000000 \n",
+ "worldpolitics 0.000000 "
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Normalize by total authors in subreddit\n",
+ "subreddit_author_counts = df_auth.groupby(\"subreddit\")[\"author\"].nunique()\n",
+ "\n",
+ "normalized_overlap = overlap_matrix.copy()\n",
+ "\n",
+ "for sub in normalized_overlap.index:\n",
+ " if sub in subreddit_author_counts:\n",
+ " normalized_overlap.loc[sub] = normalized_overlap.loc[sub] / subreddit_author_counts[sub]\n",
+ "\n",
+ "normalized_overlap.fillna(0)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "id": "35f658b1-5a1f-4663-a61c-eb9b2a2d9071",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Nodes: 8\n",
+ "Edges: 9\n"
+ ]
+ }
+ ],
+ "source": [
+ "import networkx as nx\n",
+ "\n",
+ "G = nx.Graph()\n",
+ "\n",
+ "# Add edges (filter weak connections)\n",
+ "for _, row in overlap_df.iterrows():\n",
+ " if row[\"shared_authors\"] >= 2: # threshold\n",
+ " G.add_edge(\n",
+ " row[\"subreddit_1\"],\n",
+ " row[\"subreddit_2\"],\n",
+ " weight=row[\"shared_authors\"]\n",
+ " )\n",
+ "\n",
+ "print(\"Nodes:\", G.number_of_nodes())\n",
+ "print(\"Edges:\", G.number_of_edges())"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "id": "af81f3bb-4c1d-4f77-9263-b644d62b2270",
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "if G.number_of_nodes() > 0:\n",
+ " degree_centrality = nx.degree_centrality(G)\n",
+ " betweenness_centrality = nx.betweenness_centrality(G)\n",
+ "\n",
+ " centrality_df = pd.DataFrame({\n",
+ " \"subreddit\": list(degree_centrality.keys()),\n",
+ " \"degree_centrality\": list(degree_centrality.values()),\n",
+ " \"betweenness_centrality\": list(betweenness_centrality.values())\n",
+ " }).sort_values(\"betweenness_centrality\", ascending=False)\n",
+ "\n",
+ " centrality_df.head()\n",
+ "else:\n",
+ " print(\"Graph is empty — not enough overlap\")"
+ ]
+ },
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+ "standoff": 15
+ },
+ "zerolinecolor": "white",
+ "zerolinewidth": 2
+ }
+ }
+ },
+ "title": {
+ "text": "Subreddit Network Based on Shared Authors"
+ },
+ "xaxis": {
+ "autorange": true,
+ "range": [
+ -1.0980999137511744,
+ 0.6374442667405182
+ ],
+ "type": "linear"
+ },
+ "yaxis": {
+ "autorange": true,
+ "range": [
+ -0.5218826810453328,
+ 0.8078289923535777
+ ],
+ "type": "linear"
+ }
+ }
+ },
+ "image/png": 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",
+ "text/html": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "import plotly.graph_objects as go\n",
+ "\n",
+ "if G.number_of_nodes() > 0:\n",
+ "\n",
+ " pos = nx.spring_layout(G, seed=42)\n",
+ "\n",
+ " edge_x, edge_y = [], []\n",
+ " for edge in G.edges():\n",
+ " x0, y0 = pos[edge[0]]\n",
+ " x1, y1 = pos[edge[1]]\n",
+ " edge_x += [x0, x1, None]\n",
+ " edge_y += [y0, y1, None]\n",
+ "\n",
+ " node_x, node_y, text = [], [], []\n",
+ " for node in G.nodes():\n",
+ " x, y = pos[node]\n",
+ " node_x.append(x)\n",
+ " node_y.append(y)\n",
+ " text.append(node)\n",
+ "\n",
+ " fig = go.Figure()\n",
+ "\n",
+ " fig.add_trace(go.Scatter(\n",
+ " x=edge_x, y=edge_y,\n",
+ " mode=\"lines\",\n",
+ " line=dict(width=0.5),\n",
+ " hoverinfo=\"none\"\n",
+ " ))\n",
+ "\n",
+ " fig.add_trace(go.Scatter(\n",
+ " x=node_x, y=node_y,\n",
+ " mode=\"markers+text\",\n",
+ " text=text,\n",
+ " textposition=\"top center\",\n",
+ " marker=dict(size=10),\n",
+ " ))\n",
+ "\n",
+ " fig.update_layout(\n",
+ " title=\"Subreddit Network Based on Shared Authors\",\n",
+ " showlegend=False\n",
+ " )\n",
+ "\n",
+ " fig.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "14584cb2-48f9-4e5f-b8b2-03e468b7fea3",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " author | \n",
+ " num_subreddits | \n",
+ " total_posts | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 921 | \n",
+ " Healthy_Block3036 | \n",
+ " 3 | \n",
+ " 102 | \n",
+ "
\n",
+ " \n",
+ " | 3185 | \n",
+ " pleasureismylife | \n",
+ " 3 | \n",
+ " 36 | \n",
+ "
\n",
+ " \n",
+ " | 2004 | \n",
+ " Spiderwig144 | \n",
+ " 3 | \n",
+ " 25 | \n",
+ "
\n",
+ " \n",
+ " | 571 | \n",
+ " Delicious_Adeptness9 | \n",
+ " 3 | \n",
+ " 8 | \n",
+ "
\n",
+ " \n",
+ " | 1187 | \n",
+ " La-Sauge | \n",
+ " 3 | \n",
+ " 5 | \n",
+ "
\n",
+ " \n",
+ " | 632 | \n",
+ " Droughtg3xfc | \n",
+ " 3 | \n",
+ " 4 | \n",
+ "
\n",
+ " \n",
+ " | 1286 | \n",
+ " M_i_c_K | \n",
+ " 2 | \n",
+ " 246 | \n",
+ "
\n",
+ " \n",
+ " | 1092 | \n",
+ " John3262005 | \n",
+ " 2 | \n",
+ " 194 | \n",
+ "
\n",
+ " \n",
+ " | 209 | \n",
+ " Ask4MD | \n",
+ " 2 | \n",
+ " 137 | \n",
+ "
\n",
+ " \n",
+ " | 705 | \n",
+ " Equivalent-Ad8645 | \n",
+ " 2 | \n",
+ " 67 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " author num_subreddits total_posts\n",
+ "921 Healthy_Block3036 3 102\n",
+ "3185 pleasureismylife 3 36\n",
+ "2004 Spiderwig144 3 25\n",
+ "571 Delicious_Adeptness9 3 8\n",
+ "1187 La-Sauge 3 5\n",
+ "632 Droughtg3xfc 3 4\n",
+ "1286 M_i_c_K 2 246\n",
+ "1092 John3262005 2 194\n",
+ "209 Ask4MD 2 137\n",
+ "705 Equivalent-Ad8645 2 67"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "top_bridge_authors = bridge_authors.sort_values(\n",
+ " [\"num_subreddits\", \"total_posts\"],\n",
+ " ascending=False\n",
+ ").head(10)\n",
+ "\n",
+ "top_bridge_authors"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "id": "03a71d60-43f1-4ea9-9e84-7ddd58d4f633",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "author\n",
+ "Ask4MD [Republican, Conservative]\n",
+ "Delicious_Adeptness9 [Liberal, politics, PoliticalDiscussion]\n",
+ "Droughtg3xfc [Anarchism, socialism, democrats]\n",
+ "Equivalent-Ad8645 [Republican, Conservative]\n",
+ "Healthy_Block3036 [democrats, Liberal, politics]\n",
+ "John3262005 [neoliberal, democrats]\n",
+ "La-Sauge [democrats, Liberal, politics]\n",
+ "M_i_c_K [Republican, Conservative]\n",
+ "Spiderwig144 [democrats, Conservative, politics]\n",
+ "pleasureismylife [democrats, Liberal, politics]\n",
+ "Name: subreddit, dtype: object"
+ ]
+ },
+ "execution_count": 42,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "top_authors_list = top_bridge_authors[\"author\"].tolist()\n",
+ "\n",
+ "author_details = (\n",
+ " df_auth[df_auth[\"author\"].isin(top_authors_list)]\n",
+ " .groupby(\"author\")[\"subreddit\"]\n",
+ " .apply(lambda x: list(set(x)))\n",
+ ")\n",
+ "\n",
+ "author_details"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "id": "80747d83-c8bb-4ed3-ab1e-90b1f469b4ed",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "🌐 Most Connected Subreddit:\n",
+ "subreddit democrats\n",
+ "degree_centrality 0.571429\n",
+ "betweenness_centrality 0.5\n",
+ "Name: 1, dtype: object\n",
+ "\n",
+ "🌉 Top Cross-Community Authors:\n",
+ " author num_subreddits total_posts\n",
+ "921 Healthy_Block3036 3 102\n",
+ "3185 pleasureismylife 3 36\n",
+ "2004 Spiderwig144 3 25\n",
+ "571 Delicious_Adeptness9 3 8\n",
+ "1187 La-Sauge 3 5\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Most connected subreddit\n",
+ "if G.number_of_nodes() > 0:\n",
+ " top_subreddit = centrality_df.iloc[0]\n",
+ " print(\"🌐 Most Connected Subreddit:\")\n",
+ " print(top_subreddit)\n",
+ "\n",
+ "# Top bridge authors\n",
+ "print(\"\\n🌉 Top Cross-Community Authors:\")\n",
+ "print(top_bridge_authors.head(5))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "916c1381-9a4d-4867-823b-fcd1d145ea89",
+ "metadata": {},
+ "source": [
+ "### Topic Clustering & Narrative Discovery"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 45,
+ "id": "b98d999f-dfbd-497f-97dc-ef8bff2bd69c",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Text-ready rows: 8333\n"
+ ]
+ }
+ ],
+ "source": [
+ "import re\n",
+ "\n",
+ "def clean_text(text):\n",
+ " if not isinstance(text, str):\n",
+ " return \"\"\n",
+ " \n",
+ " # Remove URLs\n",
+ " text = re.sub(r\"http\\S+|www\\S+\", \"\", text)\n",
+ " \n",
+ " # Remove extra whitespace\n",
+ " text = re.sub(r\"\\s+\", \" \", text)\n",
+ " \n",
+ " return text.strip()\n",
+ "\n",
+ "\n",
+ "# Combine title + selftext\n",
+ "df[\"full_text\"] = (\n",
+ " df[\"title\"].fillna(\"\") + \" \" + df[\"selftext\"].fillna(\"\")\n",
+ ")\n",
+ "\n",
+ "df[\"full_text\"] = df[\"full_text\"].apply(clean_text)\n",
+ "\n",
+ "# Remove empty or very short text\n",
+ "df_text = df[df[\"full_text\"].str.len() > 20].copy()\n",
+ "\n",
+ "print(\"Text-ready rows:\", len(df_text))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 46,
+ "id": "4e6da396-bc8a-4250-a12f-92dca8d2a3f3",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d9d5ed11357747c6a0d3d7d79f2fbf47",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Loading weights: 0%| | 0/103 [00:00, ?it/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "\u001b[1mBertModel LOAD REPORT\u001b[0m from: sentence-transformers/all-MiniLM-L6-v2\n",
+ "Key | Status | | \n",
+ "------------------------+------------+--+-\n",
+ "embeddings.position_ids | UNEXPECTED | | \n",
+ "\n",
+ "Notes:\n",
+ "- UNEXPECTED:\tcan be ignored when loading from different task/architecture; not ok if you expect identical arch.\n"
+ ]
+ }
+ ],
+ "source": [
+ "from sentence_transformers import SentenceTransformer\n",
+ "import os\n",
+ "import numpy as np\n",
+ "\n",
+ "model = SentenceTransformer(\"all-MiniLM-L6-v2\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 47,
+ "id": "070476f0-d158-4016-abac-0740711b2773",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "✅ Loading cached embeddings...\n",
+ "Embedding shape: (8333, 384)\n"
+ ]
+ }
+ ],
+ "source": [
+ "embedding_path = \"embeddings.npy\"\n",
+ "\n",
+ "if os.path.exists(embedding_path):\n",
+ " print(\"✅ Loading cached embeddings...\")\n",
+ " embeddings = np.load(embedding_path)\n",
+ "else:\n",
+ " print(\"⚙️ Generating embeddings...\")\n",
+ " embeddings = model.encode(\n",
+ " df_text[\"full_text\"].tolist(),\n",
+ " show_progress_bar=True,\n",
+ " batch_size=64\n",
+ " )\n",
+ " np.save(embedding_path, embeddings)\n",
+ "\n",
+ "print(\"Embedding shape:\", embeddings.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 48,
+ "id": "98bc52c9-cf03-4425-ab21-c3693b7df8c2",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/opt/anaconda3/lib/python3.12/site-packages/umap/umap_.py:1952: UserWarning:\n",
+ "\n",
+ "n_jobs value 1 overridden to 1 by setting random_state. Use no seed for parallelism.\n",
+ "\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "UMAP output shape: (8333, 2)\n"
+ ]
+ }
+ ],
+ "source": [
+ "import umap\n",
+ "\n",
+ "# Configurable parameters\n",
+ "n_neighbors = 15\n",
+ "min_dist = 0.1\n",
+ "\n",
+ "if len(embeddings) > 2:\n",
+ " reducer = umap.UMAP(\n",
+ " n_neighbors=n_neighbors,\n",
+ " min_dist=min_dist,\n",
+ " n_components=2,\n",
+ " random_state=42\n",
+ " )\n",
+ " \n",
+ " embedding_2d = reducer.fit_transform(embeddings)\n",
+ "else:\n",
+ " # Fallback for very small datasets\n",
+ " embedding_2d = np.zeros((len(embeddings), 2))\n",
+ "\n",
+ "print(\"UMAP output shape:\", embedding_2d.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "id": "952662e9-bb1b-496c-8f64-55be12d6fb00",
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3 (ipykernel)",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.12.2"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}