From cc4d7827adefc292d712b28ef88eb674af99f135 Mon Sep 17 00:00:00 2001
From: Naitian Zhou <naitian@umich.edu>
Date: Wed, 15 Nov 2023 00:03:18 -0500
Subject: [PATCH] Add case studies and data

---
 memes/analysis/Case Studies.ipynb | 2113 +++++++++++++++++++++++++++++
 memes/data/README.md              |    3 +
 2 files changed, 2116 insertions(+)
 create mode 100644 memes/analysis/Case Studies.ipynb
 create mode 100644 memes/data/README.md

diff --git a/memes/analysis/Case Studies.ipynb b/memes/analysis/Case Studies.ipynb
new file mode 100644
index 0000000..697d789
--- /dev/null
+++ b/memes/analysis/Case Studies.ipynb	
@@ -0,0 +1,2113 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "4f8189e8",
+   "metadata": {},
+   "source": [
+    "# Preparation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7bcd8288",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import itertools\n",
+    "from collections import namedtuple, defaultdict\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6a5c03ef",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<frozen importlib._bootstrap>:219: RuntimeWarning: scipy._lib.messagestream.MessageStream size changed, may indicate binary incompatibility. Expected 56 from C header, got 64 from PyObject\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from scipy import stats\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns\n",
+    "from tqdm.auto import tqdm\n",
+    "\n",
+    "plt.rcParams[\"figure.dpi\"] = 300\n",
+    "\n",
+    "sns.set()\n",
+    "\n",
+    "from PIL import Image"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "abe53b51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from memes.utils import DATA_DIR, HashClusters, read_id_to_info\n",
+    "from memes.visualization_utils import draw_grid"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "2e2a6e9f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "29082578it [00:46, 631255.51it/s]\n",
+      "16662070it [00:52, 319238.39it/s]\n",
+      "10981044it [00:31, 347317.30it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "hc = HashClusters(\n",
+    "    \"../data/filepaths/all.tsv\",\n",
+    "    \"../data/imagehashes/all-8-processed-hashes.tsv\",\n",
+    "    \"../data/clusters/all-8-processed-leiden.tsv\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "fcaaecc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_template_clusters(path=DATA_DIR / \"semantic_clusters/all-8-processed-clusters.tsv\"):\n",
+    "    TplCluster = namedtuple(\"TplCluster\", [\"tpl_to_cluster\", \"cluster_to_tpl\"])\n",
+    "\n",
+    "    tpl_to_cluster = {}\n",
+    "    cluster_to_tpl = defaultdict(set)\n",
+    "    for line in open(path, \"r\"):\n",
+    "        tpl, cluster = line.strip().split(\"\\t\")\n",
+    "        tpl_to_cluster[tpl] = cluster\n",
+    "        cluster_to_tpl[cluster].add(tpl)\n",
+    "    return TplCluster(tpl_to_cluster, dict(cluster_to_tpl))\n",
+    "\n",
+    "template_clusters = load_template_clusters(DATA_DIR / \"semantic_clusters/all-8-processed-clusters-norm-50.tsv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "fdef8ba6",
+   "metadata": {},
+   "outputs": [
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+     ]
+    },
+    {
+     "name": "stdout",
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+      "/shared/2/projects/meme-culture/data/RS_2011-12.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2017-12.gz\n"
+     ]
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+     ]
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+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2019-10.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 60%|█████▉    | 94/157 [01:39<03:25,  3.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2018-07.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 61%|██████    | 95/157 [01:40<02:56,  2.84s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2011-03.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_v2_2009-11.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2014-04.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2018-02.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 63%|██████▎   | 99/157 [01:40<01:25,  1.47s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_v2_2010-03.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2016-01.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2019-07-01-24.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 65%|██████▍   | 102/157 [01:43<01:09,  1.27s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2020-05.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 69%|██████▉   | 108/157 [01:47<00:41,  1.17it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2012-11.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2015-08.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2013-12.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2011-10.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2015-06.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2018-12.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2019-03.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 70%|███████   | 110/157 [01:52<00:56,  1.20s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2018-10.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2019-04.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 71%|███████▏  | 112/157 [01:57<01:06,  1.48s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2020-10.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 72%|███████▏  | 113/157 [02:00<01:18,  1.78s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_v2_2010-02.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_v2_2009-05.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2019-07.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 74%|███████▍  | 116/157 [02:04<01:04,  1.57s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2017-10.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 75%|███████▍  | 117/157 [02:05<00:56,  1.40s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2012-09.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2021-01.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 76%|███████▌  | 119/157 [02:08<00:56,  1.48s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2016-06.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2014-06.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2015-07.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2020-04.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 78%|███████▊  | 123/157 [02:12<00:42,  1.26s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2011-11.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2019-06.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 82%|████████▏ | 129/157 [02:37<01:06,  2.37s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2011-07.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_v2_2009-04.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2014-01.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2016-08.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2013-03.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2019-12.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 83%|████████▎ | 131/157 [02:41<00:59,  2.28s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2011-06.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2017-09.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 85%|████████▍ | 133/157 [02:41<00:42,  1.77s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2014-05.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2013-09.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2019-11.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 87%|████████▋ | 136/157 [02:46<00:35,  1.69s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2012-04.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2017-01.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 88%|████████▊ | 138/157 [02:46<00:24,  1.31s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2015-11.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2017-08.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 89%|████████▉ | 140/157 [02:47<00:17,  1.03s/it]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2013-08.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2017-03.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 92%|█████████▏| 144/157 [02:47<00:08,  1.60it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_v2_2008-08.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2016-11.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2017-06.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 92%|█████████▏| 145/157 [02:48<00:07,  1.67it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2015-01.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2016-03.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2020-09.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 94%|█████████▍| 148/157 [02:52<00:08,  1.10it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2013-07.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2015-03.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2012-05.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2021-03.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 97%|█████████▋| 152/157 [02:55<00:04,  1.19it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_v2_2010-06.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2017-11.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 98%|█████████▊| 154/157 [02:55<00:02,  1.43it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2013-06.gz\n",
+      "/shared/2/projects/meme-culture/data/RS_2021-04.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      " 99%|█████████▉| 156/157 [02:58<00:00,  1.15it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/shared/2/projects/meme-culture/data/RS_2018-06.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 157/157 [02:59<00:00,  1.14s/it]\n"
+     ]
+    }
+   ],
+   "source": [
+    "subreddit_info = read_id_to_info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c942f179",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6d6d78a6f946460f870ee46a78453a8b",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/27494802 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4d463fe0db4c4e239a935a86283595f7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/784 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def template_to_ids(tpl):\n",
+    "    ids = set()\n",
+    "    phashes = hc.cluster_to_hash[tpl]\n",
+    "    for phash in phashes:\n",
+    "        ids.update(hc.hash_to_ids[phash])\n",
+    "    return ids\n",
+    "\n",
+    "def get_instances(templates):\n",
+    "    return list(itertools.chain.from_iterable([\n",
+    "        [dict(template=template, **subreddit_info[pid]._asdict()) for pid in template_to_ids(template)] for template in templates\n",
+    "    ]))\n",
+    "\n",
+    "def posts_in_subs(subs):\n",
+    "    instances = []\n",
+    "    for cluster in tqdm(template_clusters.cluster_to_tpl):\n",
+    "        instances += posts_in_subs_and_cluster(subs, cluster)\n",
+    "    return instances\n",
+    "        \n",
+    "def posts_in_subs_and_cluster(subs, cluster):\n",
+    "    templates = template_clusters.cluster_to_tpl[cluster]\n",
+    "    instances = get_instances(templates)\n",
+    "    return [{\"cluster\": cluster, **instance} for instance in instances if instance[\"subreddit\"] in subs]\n",
+    "\n",
+    "all_subs_in_dataset = set([post.subreddit for post in tqdm(subreddit_info.values())])\n",
+    "all_memes = posts_in_subs(all_subs_in_dataset)\n",
+    "df = pd.DataFrame(all_memes)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "cc377bc2",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 3825199 posts\n",
+      "There are 784 semantic clusters.\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"There are {len(df)} posts\")\n",
+    "print(f\"There are {len(df.cluster.value_counts())} semantic clusters.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "bc928047",
+   "metadata": {},
+   "source": [
+    "# Variation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5bf9fc0f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from collections import namedtuple, defaultdict\n",
+    "DocTermMatrix = namedtuple(\"DocTermMatrix\", (\"mat\", \"idx2word\", \"word2idx\", \"idx2doc\", \"doc2idx\"))\n",
+    "\n",
+    "def create_doc_term_mat(df, document_col, word_col, progress=False):\n",
+    "    idx2word = list(df[word_col].unique())\n",
+    "    word2idx = {v: k for k, v in enumerate(idx2word)}\n",
+    "    idx2doc = list(df[document_col].unique())\n",
+    "    doc2idx = {v: k for k, v in enumerate(idx2doc)}\n",
+    "    mat = np.zeros((len(doc2idx), len(word2idx)))\n",
+    "    for doc, word in tqdm(zip(df[document_col], df[word_col]), total=len(df), disable=not progress):\n",
+    "        mat[doc2idx[doc]][word2idx[word]] += 1\n",
+    "        \n",
+    "    return DocTermMatrix(mat, idx2word, word2idx, idx2doc, doc2idx)\n",
+    "\n",
+    "\n",
+    "def logodds(matrix, group1, group2=None, min_occurrences=0, zscore=True):\n",
+    "    \"\"\"\n",
+    "    Compute log odds for one against rest for K documents with N total unique words.\n",
+    "    \n",
+    "    The prior is computed over all of the words\n",
+    "    \n",
+    "    Filter out words with less than min_occurrences co-occurrences.\n",
+    "    \"\"\"\n",
+    "    if not (isinstance(group1, set) or isinstance(group1, list)):\n",
+    "        group1 = set([group1])\n",
+    "    if not (isinstance(group2, set) or isinstance(group2, list)):\n",
+    "        group2 = set([group2])\n",
+    "        \n",
+    "    mat = matrix.mat.copy()\n",
+    "    mat[mat < min_occurrences] = 0\n",
+    "\n",
+    "    _check_group1 = lambda x: x in group1\n",
+    "    _check_group2 = lambda x: (x in group2) if group2 is not None else (not _check_group1(x))\n",
+    "    \n",
+    "    eps = 1e-8\n",
+    "    def _log_odds_delta(mat, group1_idx, group2_idx, zscore):\n",
+    "        n_prior = mat.sum()\n",
+    "        g_1 = mat[group1_idx].sum(axis=0)\n",
+    "        g_2 = mat[group2_idx].sum(axis=0)\n",
+    "        n_1 = g_1.sum()\n",
+    "        n_2 = g_2.sum()\n",
+    "\n",
+    "        prior = mat.sum(axis=0)\n",
+    "\n",
+    "        l1 = (g_1 + prior) / ((n_1 + n_prior) - (g_1 + prior))\n",
+    "        l2 = (g_2 + prior) / ((n_2 + n_prior) - (g_2 + prior))\n",
+    "        delta = (np.log(l1) - np.log(l2))\n",
+    "        if zscore:\n",
+    "            sigma = np.sqrt(1 / (g_1 + prior) + 1 / (g_2 + prior))\n",
+    "            delta = delta / sigma\n",
+    "\n",
+    "        return delta\n",
+    "\n",
+    "\n",
+    "    group1_idx = [idx for idx, doc in enumerate(matrix.idx2doc) if _check_group1(doc)]\n",
+    "    group2_idx = [idx for idx, doc in enumerate(matrix.idx2doc) if _check_group2(doc)]\n",
+    "\n",
+    "    return _log_odds_delta(mat, group1_idx, group2_idx, zscore=zscore)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "87fcbfac",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def show_log_odds(df, document_col, word_col, group2=None, zscore=True, threshold=2):\n",
+    "    matrix = create_doc_term_mat(df, document_col, word_col)\n",
+    "    \n",
+    "    for c in matrix.doc2idx:\n",
+    "        lor = logodds(matrix, group1=c, group2=group2, zscore=zscore)\n",
+    "        print(c)\n",
+    "        for i in np.argsort(-np.abs(lor))[:10]:\n",
+    "            if np.abs(lor[i]) >= threshold:\n",
+    "                print(\"\\t\" + matrix.idx2word[i] + \"\\t\\t\" + str(lor[i]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "05eb3821",
+   "metadata": {},
+   "source": [
+    "## Does variation exist?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "bf82cb30",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ba2febe5f04f44b687d6e675450cecb0",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/100 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "topk = df.cluster.value_counts().index[:100]\n",
+    "all_pairs = []\n",
+    "for clu in tqdm(topk):\n",
+    "    matrix = create_doc_term_mat(df[df.cluster.eq(clu)], \"subreddit\", \"template\")\n",
+    "    \n",
+    "    for c in matrix.doc2idx:\n",
+    "        lor = logodds(matrix, group1=c, zscore=True)\n",
+    "        for i in np.argsort(-lor):\n",
+    "            if (lor[i]) > 1.96:\n",
+    "                all_pairs.append((c, clu, matrix.idx2word[i], lor[i]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "17865f47",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "significant = pd.DataFrame(all_pairs, columns=[\"subreddit\", \"cluster\", \"template\", \"zscore\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "0bf40559",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There is significant variation in 94 semantic clusters\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"There is significant variation in {len(significant.cluster.unique())} semantic clusters\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8be79a5d",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "This spans 391 templates and 26 subreddits\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(f\"This spans {len(significant.template.unique())} templates and {len(significant.subreddit.unique())} subreddits\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "dd44e366",
+   "metadata": {},
+   "source": [
+    "Some variables with notable variation:\n",
+    "\n",
+    "- Cluster 1 is the compare2 cluster\n",
+    "- Cluster 0 is the declarative cluster\n",
+    "- Cluster 139 is a \"said displeasing thing\" cluster\n",
+    "- Cluster 30 is a surprise narrative cluster\n",
+    "- Cluster 15 is a scalar cluster\n",
+    "- 14 is concern\n",
+    "- 19 is looming threat?\n",
+    "\n",
+    "- Cluster 39 is self-satisfied"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "118141cb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.7583405446933598"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "significant.zscore.mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "700bbfcb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>template</th>\n",
+       "      <th>zscore</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>subreddit</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Animemes</th>\n",
+       "      <td>2095</td>\n",
+       "      <td>6.068878</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>HistoryMemes</th>\n",
+       "      <td>108</td>\n",
+       "      <td>2.089408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>dankmemes</th>\n",
+       "      <td>60</td>\n",
+       "      <td>6.223277</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>memes</th>\n",
+       "      <td>16</td>\n",
+       "      <td>4.731957</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>memesITA</th>\n",
+       "      <td>253</td>\n",
+       "      <td>2.345918</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "             template    zscore\n",
+       "subreddit                      \n",
+       "Animemes         2095  6.068878\n",
+       "HistoryMemes      108  2.089408\n",
+       "dankmemes          60  6.223277\n",
+       "memes              16  4.731957\n",
+       "memesITA          253  2.345918"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "significant[significant.cluster.eq(\"30\")].groupby(\"subreddit\").agg({\"template\": \"first\", \"zscore\": \"first\"})"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8be57c8a",
+   "metadata": {},
+   "source": [
+    "# Innovation"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d8a1950",
+   "metadata": {},
+   "source": [
+    "## Entropy"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "e61774e5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import entropy\n",
+    "from scipy.spatial.distance import jensenshannon"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "8e16edbe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_entropy_values_for_cluster(semantic_cluster):\n",
+    "    \"\"\"\n",
+    "    Return size-k list of entropy values where\n",
+    "    \n",
+    "    k = the number of years the cluster has had meme instances\n",
+    "    ith index = the entropy value i years after the first instance\n",
+    "    \"\"\"\n",
+    "    templates = template_clusters.cluster_to_tpl[semantic_cluster]\n",
+    "    instances = get_instances(templates)\n",
+    "    df = pd.DataFrame(instances)\n",
+    "\n",
+    "    df.loc[:, \"timestamp\"] = pd.to_datetime(df.utc, unit=\"s\")\n",
+    "    \n",
+    "    # get year by num years since first date\n",
+    "    df.loc[:, \"year\"] = ((df.timestamp - df.timestamp.min()).dt.days / 365.2425).astype(int)\n",
+    "\n",
+    "    df = df[df.year != df.year.max]\n",
+    "\n",
+    "    counts_by_year_by_tpl = df.groupby([\"year\", df.template]).post_id.count().reset_index()\n",
+    "    counts_by_year_by_tpl[\"proportion\"] = counts_by_year_by_tpl.groupby(\"year\").post_id.transform(lambda x: x / x.sum())\n",
+    "    \n",
+    "    sample_size = counts_by_year_by_tpl.groupby(\"year\").post_id.sum().min()\n",
+    "    if sample_size < 30:\n",
+    "        return None, None\n",
+    "    distributions = counts_by_year_by_tpl.pivot(index=\"year\", columns=\"template\", values=\"proportion\").fillna(0)\n",
+    "\n",
+    "    years = distributions.index\n",
+    "    min_years = 5\n",
+    "    if len(years) < min_years:\n",
+    "        return None, None\n",
+    "    rng = np.random.default_rng(seed=0xb1ab)\n",
+    "    resampled = pd.DataFrame([\n",
+    "        rng.multinomial(sample_size, row)\n",
+    "        for row in distributions.to_numpy()\n",
+    "    ],\n",
+    "        index=years,\n",
+    "        columns=distributions.columns\n",
+    "    )\n",
+    "    max_years = 8\n",
+    "    entropies = entropy(resampled.to_numpy(), axis=1)\n",
+    "    \n",
+    "    data = pd.DataFrame({\n",
+    "        \"entropy\": entropies[:max_years],\n",
+    "        \"year\": years[:max_years],\n",
+    "        \"cluster\": [semantic_cluster] * min(len(years), max_years)\n",
+    "    })\n",
+    "#     return resampled\n",
+    "#     resampled[\"proportion\"] = resampled.groupby(\"year\").value.transform(lambda x: x / x.sum())\n",
+    "    \n",
+    "#     data[\"cluster\"] = semantic_cluster\n",
+    "#     data[\"timestamp\"] = data[\"timestamp\"] - data[\"timestamp\"].min()\n",
+    "    return data, resampled"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "ed017e0c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def draw_entropy():\n",
+    "    topk = list(template_clusters.cluster_to_tpl.keys())\n",
+    "    values = [get_entropy_values_for_cluster(clu)[0] for clu in (topk)]\n",
+    "#     resampled = [get_entropy_values_for_cluster(clu)[1] for clu in tqdm(topk)]\n",
+    "    values = [value for value in values if value is not None]\n",
+    "    entropies = pd.concat(values)\n",
+    "    print(f\"We have coverage over {len(df[df.cluster.isin(entropies.cluster.unique())])} posts\")\n",
+    "    print(f\"and {len(values)} clusters\")\n",
+    "    sns.pointplot(entropies, x=\"year\", y=\"entropy\")\n",
+    "    plt.ylabel(\"Entropy\")\n",
+    "    plt.xlabel(\"Years after introduction\")\n",
+    "    plt.savefig(\"../figures/entropy.pdf\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "6486c878",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "We have coverage over 958924 posts\n",
+      "and 146 clusters\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "draw_entropy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "5eb786d7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def get_js_dists(semantic_cluster):\n",
+    "    \"\"\"\n",
+    "    Return size-k list of entropy values where\n",
+    "    \n",
+    "    k = the number of years the cluster has had meme instances\n",
+    "    ith index = the entropy value i years after the first instance\n",
+    "    \"\"\"\n",
+    "    templates = template_clusters.cluster_to_tpl[semantic_cluster]\n",
+    "    instances = get_instances(templates)\n",
+    "    df = pd.DataFrame(instances)\n",
+    "\n",
+    "    df.loc[:, \"timestamp\"] = pd.to_datetime(df.utc, unit=\"s\")\n",
+    "#     df.loc[:, \"year\"] = df.timestamp.dt.year\n",
+    "    df.loc[:, \"year\"] = ((df.timestamp - df.timestamp.min()).dt.days / 365.2425).astype(int)\n",
+    "    df = df[df.year != df.year.max]\n",
+    "\n",
+    "    counts_by_year_by_tpl = df.groupby([df.year, df.template]).post_id.count().reset_index()\n",
+    "    counts_by_year_by_tpl[\"proportion\"] = counts_by_year_by_tpl.groupby(\"year\").post_id.transform(lambda x: x / x.sum())\n",
+    "    post_dist = counts_by_year_by_tpl.pivot(index=\"year\", columns=\"template\", values=\"proportion\").fillna(0)\n",
+    "    pre_dist = post_dist.shift()\n",
+    "\n",
+    "    sample_size = counts_by_year_by_tpl.groupby(\"year\").post_id.sum().min()\n",
+    "    if sample_size < 30:\n",
+    "        return None\n",
+    "\n",
+    "    data = []\n",
+    "    \n",
+    "    for (year, pre), (_, post) in zip(pre_dist.iterrows(), post_dist.iterrows()):\n",
+    "        dist = jensenshannon(pre, post)\n",
+    "        data.append({\n",
+    "            \"js_div\": dist,\n",
+    "            \"year\": year,\n",
+    "            \"cluster\": semantic_cluster\n",
+    "        })\n",
+    "    data = pd.DataFrame(data[1:])\n",
+    "#     data[\"year\"] = data[\"year\"] - data[\"year\"].min()\n",
+    "    return data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "5ef71df1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "577d54fa1403440aa5d71accd0c0cada",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/784 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "def jsdiv():\n",
+    "    topk = list(template_clusters.cluster_to_tpl.keys())#[:100]\n",
+    "    values = [get_js_dists(clu) for clu in tqdm(topk)]\n",
+    "    values = [value for value in values if value is not None]\n",
+    "    dists = pd.concat(values)\n",
+    "\n",
+    "    sns.lineplot(dists, x=\"year\", y=\"js_div\")\n",
+    "    plt.ylabel(\"JS Divergence\")\n",
+    "    plt.xlabel(\"Years after introduction\")\n",
+    "jsdiv()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9fbf022b",
+   "metadata": {},
+   "source": [
+    "## Diffusion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "aca574cf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def pmi(matrix):\n",
+    "    num_x, num_y = matrix.shape\n",
+    "    total = matrix.sum()\n",
+    "    p_xy = matrix / total\n",
+    "    p_x = matrix.sum(axis=1, keepdims=True) / total\n",
+    "    p_y = matrix.sum(axis=0, keepdims=True) / total\n",
+    "    pmi = np.log2(p_xy) - np.log2(p_x * p_y)\n",
+    "    \n",
+    "    return pmi\n",
+    "\n",
+    "\n",
+    "def npmi(matrix):\n",
+    "    h_xy = -np.log2(matrix / matrix.sum())\n",
+    "    res = pmi(matrix) / h_xy\n",
+    "    return np.where(np.isnan(res), -1, res)\n",
+    "   \n",
+    "def ppmi(matrix):\n",
+    "    return np.maximum(pmi(matrix), 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "731da727",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "2facdbd6aade4a3cb36e084fdd285fc7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/784 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-23-85692f592341>:7: RuntimeWarning: divide by zero encountered in log2\n",
+      "  pmi = np.log2(p_xy) - np.log2(p_x * p_y)\n"
+     ]
+    }
+   ],
+   "source": [
+    "pmi_dict = {}\n",
+    "for cluster in tqdm(template_clusters.cluster_to_tpl.keys()):\n",
+    "    matrix = create_doc_term_mat(df[df.cluster.eq(cluster)], \"subreddit\", \"template\")\n",
+    "    \n",
+    "    a = ppmi(matrix.mat)\n",
+    "\n",
+    "    for c, t in zip(*np.unravel_index(range(matrix.mat.size), matrix.mat.shape)):\n",
+    "        pmi_dict[(matrix.idx2word[t], matrix.idx2doc[c])] = a[c, t]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1f5202bf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df.loc[:, \"pmi\"] = df[[\"template\", \"subreddit\"]].apply(lambda x: pmi_dict[(x.template, x.subreddit)], axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "a21654f7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def first_subs_for_templates_in_cluster(semantic_cluster):\n",
+    "    templates = template_clusters.cluster_to_tpl[semantic_cluster]\n",
+    "    instances = get_instances(templates)\n",
+    "    df = pd.DataFrame(instances)\n",
+    "    \n",
+    "    return df.sort_values(by=\"utc\").groupby(\"template\").agg({\"subreddit\": \"first\"})\n",
+    "\n",
+    "def topk_proportions(x):\n",
+    "    counts = x.head(100).value_counts()\n",
+    "    counts /= counts.sum()\n",
+    "    return counts.iloc[0]\n",
+    "\n",
+    "def top_sub(x):\n",
+    "    counts = x.head(100).value_counts()\n",
+    "    counts /= counts.sum()\n",
+    "    return counts.index[0]\n",
+    "\n",
+    "def origin_analysis():\n",
+    "    # calculate the proportion of each subreddit among the starters\n",
+    "    starter_proportions = df.sort_values(\"utc\").groupby(\"template\").agg(\n",
+    "        proportion = (\"subreddit\", topk_proportions),\n",
+    "        subreddit = (\"subreddit\", top_sub)\n",
+    "    )\n",
+    "\n",
+    "    # bin proportions for later use\n",
+    "    starter_proportions[\"binned_proportion\"] = starter_proportions.proportion * 100 // 10\n",
+    "\n",
+    "    # filter for at least 200, to do before/after\n",
+    "    enough_posts = [tpl for tpl, ct in df.template.value_counts().to_dict().items() if ct > 200]\n",
+    "    ending = df[df.template.isin(set(enough_posts))]\n",
+    "\n",
+    "    # construct list of afters for each template\n",
+    "    tmp_dfs = []\n",
+    "    for cluster, inds in tqdm(ending.groupby(\"template\").groups.items()):\n",
+    "        tmp_dfs.append(\n",
+    "            ending.loc[inds].sort_values(\"utc\", ascending=True).iloc[100:]\n",
+    "        )\n",
+    "    ending_df = pd.concat(tmp_dfs, axis=0)\n",
+    "\n",
+    "    # calculate after PMI\n",
+    "    ending_pmi_dict = {}\n",
+    "    for cluster in tqdm(ending_df.cluster.unique()):\n",
+    "        matrix = create_doc_term_mat(ending_df[ending_df.cluster.eq(cluster)], \"subreddit\", \"template\")\n",
+    "\n",
+    "        a = ppmi(matrix.mat)\n",
+    "\n",
+    "        if matrix.mat.size == 0:\n",
+    "    #             print(cluster)\n",
+    "            continue\n",
+    "\n",
+    "        for c, t in zip(*np.unravel_index(np.array(range(matrix.mat.size)), matrix.mat.shape)):\n",
+    "            ending_pmi_dict[(matrix.idx2word[t], matrix.idx2doc[c])] = a[c, t]\n",
+    "\n",
+    "    ending_df.loc[:, \"pmi\"] = ending_df[[\"template\", \"subreddit\"]].apply(lambda x: ending_pmi_dict.get((x.template, x.subreddit), None), axis=1)\n",
+    "\n",
+    "\n",
+    "    starter_subs = starter_proportions.to_dict()[\"subreddit\"]\n",
+    "    mask = ending_df.subreddit.ne(df.template.apply(starter_subs.get))\n",
+    "\n",
+    "    final_pmis = pd.DataFrame(\n",
+    "        dict(pmi=starter_proportions.reset_index()[[\"template\", \"subreddit\"]].apply(lambda x: ending_pmi_dict.get((x.template, x.subreddit), None), axis=1),\n",
+    "        template=starter_proportions.index)\n",
+    "    ).set_index(\"template\").pmi\n",
+    "\n",
+    "\n",
+    "    # final_control is the avg PMI of non-starters\n",
+    "    # final_pmi is the PMI of the starter sub for each\n",
+    "\n",
+    "    starter_proportions.loc[:, \"final_control\"] = ending_df[mask].groupby(\"template\").pmi.agg(\"mean\")\n",
+    "    starter_proportions.loc[:, \"final_pmi\"] = final_pmis\n",
+    "\n",
+    "    return starter_proportions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "10a0d7d3",
+   "metadata": {
+    "code_folding": []
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "def linregplot(starter_proportions):\n",
+    "    data = starter_proportions\n",
+    "    data = data[data.proportion.between(0.5, 1, inclusive=\"left\")]\n",
+    "    sns.regplot(\n",
+    "        data=data,\n",
+    "        x=\"proportion\",\n",
+    "        y=data.final_pmi - data.final_control,\n",
+    "    )\n",
+    "    plt.ylabel(\"Origin PMI - Control PMI\")\n",
+    "    plt.xlabel(\"Proportion of Seed Posts by Innovator Subreddit\")\n",
+    "\n",
+    "def linregplotb(starter_proportions):\n",
+    "    data = starter_proportions\n",
+    "    data = data[data.proportion.between(0.5, 1, inclusive=\"left\")]\n",
+    "    sns.regplot(\n",
+    "        data=data,\n",
+    "        x=\"proportion\",\n",
+    "        y=data.final_pmi,\n",
+    "    )\n",
+    "    plt.ylabel(\"Origin PMI\")\n",
+    "    plt.xlabel(\"Proportion of Seed Posts by Innovator Subreddit\")\n",
+    "    plt.savefig(\"../figures/div_linregress.pdf\")\n",
+    "    \n",
+    "def compareplot(starter_proportions):\n",
+    "    data = starter_proportions.dropna(subset=[\"final_pmi\", \"final_control\"])\n",
+    "    data = data[data.proportion.between(0.5, 1, inclusive=\"left\")]\n",
+    "    data = data.melt(\n",
+    "        id_vars=[\"binned_proportion\", \"subreddit\"],\n",
+    "        value_vars=[\"final_pmi\", \"final_control\"],\n",
+    "        var_name=\"Group\",\n",
+    "        value_name=\"PPMI\"\n",
+    "    )\n",
+    "    sns.pointplot(\n",
+    "        data=data.assign(\n",
+    "            Group=data[\"Group\"].map({\"final_pmi\": \"Origin subreddit\", \"final_control\": \"Other subreddits\"})\n",
+    "        ),\n",
+    "        x=data.binned_proportion / 10,\n",
+    "        y=\"PPMI\",\n",
+    "        hue=\"Group\"\n",
+    "    )\n",
+    "    plt.xlabel(\"% of first 100 posts in origin subreddit\")\n",
+    "    plt.savefig(\"../figures/divergence.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "89547d3d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def regress(starter_proportions):\n",
+    "    # do paired ttest\n",
+    "    data = starter_proportions.dropna(subset=[\"final_pmi\", \"final_control\"])\n",
+    "    data = data[data.proportion.between(0.5, 1, inclusive=\"left\")]\n",
+    "#     results = stats.ttest_rel(data.final_pmi, data.final_control)\n",
+    "    print(\"Paired t-test for each cluster\")\n",
+    "    print(stats.linregress(data.proportion, data.final_pmi - data.final_control))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "bc8290ea",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d36866e0c65d4d8bada2a3ac8b3d26e5",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/3683 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8196ee16d5db492b806bd2316fc807d3",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/702 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "<ipython-input-23-85692f592341>:7: RuntimeWarning: divide by zero encountered in log2\n",
+      "  pmi = np.log2(p_xy) - np.log2(p_x * p_y)\n",
+      "<ipython-input-27-dcabfa9e21b3>:69: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
+      "  starter_proportions.loc[:, \"final_control\"] = ending_df[mask].groupby(\"template\").pmi.agg(\"mean\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "starter_proportions = origin_analysis()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "ce1c7009",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Paired t-test for each cluster\n",
+      "LinregressResult(slope=3.8349818189385787, intercept=-2.354880105716605, rvalue=0.5046915037221197, pvalue=1.1844118404801556e-59, stderr=0.21830066757602912, intercept_stderr=0.13621971707415811)\n"
+     ]
+    }
+   ],
+   "source": [
+    "regress(starter_proportions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "e4cfdb48",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "linregplot(starter_proportions)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "868b7cac",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "compareplot(starter_proportions)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3e2d932",
+   "metadata": {},
+   "source": [
+    "# Acculturation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d660334b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tmp = df.sort_values(\"utc\").groupby([\"subreddit\", \"user\"])\n",
+    "df[\"comment_timedelta\"] = tmp.utc.transform(lambda x: x - x.iloc[0])\n",
+    "df.comment_timedelta = pd.to_timedelta(df.comment_timedelta, unit=\"s\")\n",
+    "\n",
+    "a = df[\n",
+    "    df.user.ne(\"[deleted]\")\n",
+    "    & ~df.user.str.lower().str.contains(\"bot\")\n",
+    "    & df.comment_timedelta.dt.days.lt(12 * 30)\n",
+    "]\n",
+    "\n",
+    "user_counts = a.groupby([\"subreddit\", \"user\"]).post_id.count().reset_index()\n",
+    "keep_user_subs = {(x.subreddit, x.user) for x in user_counts[user_counts.post_id.gt(10)][[\"subreddit\", \"user\"]].itertuples()}\n",
+    "mask = a[[\"subreddit\", \"user\"]].apply(lambda x: (x.subreddit, x.user) in keep_user_subs, axis=1)\n",
+    "grouped = a[mask].groupby([\"subreddit\", \"user\", a[mask].comment_timedelta.dt.days // 30]).pmi.mean().reset_index()\n",
+    "\n",
+    "keep_groups = grouped.subreddit.value_counts()[grouped.subreddit.value_counts() >= 30].index\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d36ac260",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "len(g.user.unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "67edd139",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(0xb1ab)\n",
+    "g = grouped[grouped.subreddit.isin(keep_groups)].groupby('subreddit', group_keys=False).apply(lambda x: x.sample(min(len(x), 100), replace=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a12c188f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.pointplot(x=g.comment_timedelta, y=g.pmi)\n",
+    "\n",
+    "plt.xlabel(\"Months since first comment\")\n",
+    "plt.ylabel(\"PPMI\")\n",
+    "plt.savefig(\"../figures/acculturation.pdf\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "443b4154",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "print(stats.pearsonr(g.comment_timedelta, g.pmi))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd8e33ad",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "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": "3.8.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/memes/data/README.md b/memes/data/README.md
new file mode 100644
index 0000000..85bf1ed
--- /dev/null
+++ b/memes/data/README.md
@@ -0,0 +1,3 @@
+# Dataset
+
+Currently, the images are not available for download. However, semantic\_clusters\_roberta contains IDs of Reddit posts, the image URLs, and the cluster assignments, which you can use to reconstruct the data for all of the experiments.