{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Retrieving Bluesky datasets\n",
    "\n",
    "Retrieving Bluesky datasets with the Bluesky API."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieving Bluesky credentials and anonymity encryption key from credentials file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open(\".credentials\", \"r\") as file:\n",
    "    for line in file:\n",
    "        if 'bluesky_username' in line:\n",
    "            username = line.split('bluesky_username=')[1].strip()\n",
    "            break\n",
    "    for line in file:\n",
    "        if 'bluesky_password' in line:\n",
    "            password = line.split('bluesky_password=')[1].strip()\n",
    "            break"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initializing the Bluesky API."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from atproto import Client\n",
    "\n",
    "# Initialize and log in\n",
    "client = Client()\n",
    "client.login(username, password);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Converting the retrieval start and end time period to timestamps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from datetime import datetime, timezone, timedelta\n",
    "\n",
    "case = '...'\n",
    "\n",
    "start_date_string = '...'\n",
    "end_date_string = '...'\n",
    "\n",
    "start_timestamp = datetime.strptime(start_date_string, '%Y-%m-%d').replace(tzinfo=timezone.utc)\n",
    "end_timestamp = datetime.strptime(end_date_string, '%Y-%m-%d').replace(tzinfo=timezone.utc)\n",
    "\n",
    "iso_start_timestamp = start_timestamp.isoformat().replace('+00:00', 'Z')\n",
    "iso_end_timestamp = end_timestamp.isoformat().replace('+00:00', 'Z')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define functions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re, random, time\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tqdm import tqdm\n",
    "from more_itertools import chunked\n",
    "\n",
    "# Remove URLs, hashtags, mentions, emojis and whitespaces\n",
    "def clean_text(text):\n",
    "    if not isinstance(text, str):\n",
    "        return np.nan\n",
    "    text = re.sub(r'http\\S+|www\\S+|https\\S+', '', text, flags=re.MULTILINE)\n",
    "    text = re.sub(r'#\\S+', '', text)\n",
    "    text = re.sub(r'@\\S+', '', text)\n",
    "    text = re.sub(r'[^\\w\\s,]', '', text)\n",
    "    text = re.sub(r'\\s+', ' ', text).strip()\n",
    "    return text\n",
    "\n",
    "# Retry helper\n",
    "def call_with_retry(fn, *args, max_retries=5, base_delay=2, **kwargs):\n",
    "    last_exc = None\n",
    "    for attempt in range(1, max_retries + 1):\n",
    "        try:\n",
    "            return fn(*args, **kwargs)\n",
    "        except Exception as e:\n",
    "            last_exc = e\n",
    "            wait = base_delay * (2 ** (attempt - 1)) + random.uniform(0, 1)\n",
    "            print(f' [retry {attempt}/{max_retries}] {type(e).__name__}: {e} - retrying in {wait:.1f}s')\n",
    "            time.sleep(wait)\n",
    "    raise last_exc\n",
    "\n",
    "# Post field extraction\n",
    "def extract_post(post):\n",
    "    try:\n",
    "        text = clean_text(post.record.text) if hasattr(post.record, 'text') else None\n",
    "        iso_date = post.record.created_at if hasattr(post.record, 'created_at') else None\n",
    "        user = post.author.handle if hasattr(post.author, 'handle') else None\n",
    "        post_id = post.uri if hasattr(post, 'uri') else None\n",
    "        parent_id = (\n",
    "            post.record.reply.parent.uri\n",
    "            if hasattr(post.record, 'reply') and hasattr(post.record.reply, 'parent')\n",
    "            else 'no_parent'\n",
    "        )\n",
    " \n",
    "        if iso_date is None or post_id is None or user is None:\n",
    "            return None\n",
    " \n",
    "        date_obj = datetime.fromisoformat(iso_date.replace('Z', '+00:00'))\n",
    "        date_str = date_obj.strftime('%Y-%m-%d %H:%M:%S')\n",
    " \n",
    "        return {\n",
    "            'date': date_str,\n",
    "            'text': text if text is not None else np.nan,\n",
    "            'user': user,\n",
    "            'id': post_id,\n",
    "            'parentid': parent_id,\n",
    "        }, date_obj\n",
    "    except Exception as e:\n",
    "        print(f\"  Skipping malformed post: {e}\")\n",
    "        return None\n",
    "\n",
    "# Cleanup\n",
    "def _dedupe(rows):\n",
    "    if not rows:\n",
    "        return pd.DataFrame()\n",
    "    df = pd.DataFrame(rows)\n",
    "    before = len(df)\n",
    "    df = df.drop_duplicates(subset='id')\n",
    "    df = df.dropna(subset=['text', 'id', 'user'])\n",
    "    df = df[df['text'].astype(str).str.strip() != '']\n",
    "    after = len(df)\n",
    "    if before != after:\n",
    "        print(f\"(Filtered {before - after} duplicate/empty rows out of {before}.)\")\n",
    "    return df.reset_index(drop=True)\n",
    "\n",
    "        \n",
    "# Search for posts containing a specific keyword\n",
    "def get_posts_by_keyword(keyword, since, until, limit=300, page_size=100, max_retries=5):\n",
    "\n",
    "    state = _STATE.setdefault(keyword, {'until': until, 'rows': []})\n",
    " \n",
    "    if state['until'] != until and state['rows']:\n",
    "        print(f\"Resuming '{keyword}' from until={state['until']}, {len(state['rows'])} posts already fetched this session.\")\n",
    " \n",
    "    since_dt = datetime.fromisoformat(since.replace('Z', '+00:00'))\n",
    "    progress_bar = tqdm(total=limit, initial=min(len(state['rows']), limit), desc=\"Fetching posts\")\n",
    "\n",
    "    try:\n",
    "        while len(state['rows']) < limit:\n",
    "            try:\n",
    "                search_results = call_with_retry(client.app.bsky.feed.search_posts, {'q': keyword, 'since': since, 'until': state['until'], 'lang': 'en', 'limit': page_size}, max_retries=max_retries)\n",
    "            except Exception as e:\n",
    "                print(f\"Giving up on this page after {max_retries} retries: {e}\")\n",
    "                print(\"Progress so far is kept — just call this again to continue.\")\n",
    "                break\n",
    "    \n",
    "            posts = search_results.posts\n",
    "            if not posts:\n",
    "                print(\"No more posts returned — reached the end of available results.\")\n",
    "                break\n",
    "    \n",
    "            oldest_date_obj = None\n",
    "            for post in posts:\n",
    "                result = extract_post(post)\n",
    "                if result is None:\n",
    "                    continue\n",
    "                row, date_obj = result\n",
    "                state['rows'].append(row)\n",
    "                if oldest_date_obj is None or date_obj < oldest_date_obj:\n",
    "                    oldest_date_obj = date_obj\n",
    "    \n",
    "            progress_bar.update(len(posts))\n",
    "    \n",
    "            if oldest_date_obj is not None:\n",
    "                state['until'] = oldest_date_obj.isoformat().replace('+00:00', 'Z')\n",
    "                progress_bar.set_postfix_str(f'now at: {state[\"until\"]}')\n",
    "    \n",
    "                if oldest_date_obj < since_dt + timedelta(hours=1):\n",
    "                    print(\"Reached the `since` boundary.\")\n",
    "                    break\n",
    " \n",
    "    finally:\n",
    "        progress_bar.close()\n",
    " \n",
    "    print(f\"{len(state['rows'])} posts fetched in total this session for '{keyword}'. Call again with the same checkpoint_name to keep going.\")\n",
    "    return _dedupe(state['rows'])\n",
    "\n",
    "# Obtain posts from uri list\n",
    "def get_posts(uris, chunk_size=20, max_retries=5):\n",
    "    state = _STATE.setdefault('_by_uri', {'done_uris': set(), 'rows': []})\n",
    " \n",
    "    remaining = [u for u in uris if u not in state['done_uris']]\n",
    "    chunks = list(chunked(remaining, chunk_size))\n",
    " \n",
    "    progress_bar = tqdm(total=len(uris), initial=len(state['done_uris']), desc=\"Fetching posts\")\n",
    " \n",
    "    try:\n",
    "        for uribit in chunks:\n",
    "            try:\n",
    "                search_results = call_with_retry(client.app.bsky.feed.get_posts, {'uris': uribit}, max_retries=max_retries)\n",
    "            except Exception as e:\n",
    "                print(f\"Skipping this chunk of {len(uribit)} URIs after {max_retries} retries: {e}\")\n",
    "                continue\n",
    " \n",
    "            for post in search_results.posts:\n",
    "                result = extract_post(post)\n",
    "                if result is None:\n",
    "                    continue\n",
    "                row, _ = result\n",
    "                state['rows'].append(row)\n",
    " \n",
    "            state['done_uris'].update(uribit)\n",
    "            progress_bar.update(len(uribit))\n",
    " \n",
    "    finally:\n",
    "        progress_bar.close()\n",
    " \n",
    "    print(f\"{len(state['done_uris'])}/{len(uris)} URIs processed in total.\")\n",
    "    return _dedupe(state['rows'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_STATE = {}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve posts with a keyword."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "raw_df_data = get_posts_by_keyword('...', iso_start_timestamp, iso_end_timestamp, 100000)\n",
    "raw_df_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Retrieve absent posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "_STATE = {}\n",
    "id_list = raw_df_data['id'].to_list()\n",
    "id_list.append('no_parent')\n",
    "absent_parents = raw_df_data.loc[~raw_df_data['parentid'].isin(id_list), 'parentid'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_absent_data = get_posts(absent_parents)\n",
    "df_absent_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Combine dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data = pd.concat([raw_df_data, df_absent_data], ignore_index=True)\n",
    "df_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Remove non-english entries."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langdetect import detect\n",
    "\n",
    "def is_english(text):\n",
    "    try:\n",
    "        return detect(text) == 'en'\n",
    "    except:\n",
    "        return False\n",
    "    \n",
    "df_data = df_data[df_data['text'].apply(is_english)].reset_index(drop=True)\n",
    "df_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting the dataframe to a csv dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_data.to_csv(f'{case}.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Count the number of posts per user. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "user_posts = df_data.groupby('user')['id'].count()\n",
    "user_posts.describe()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter on social users."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "social_users = user_posts[(user_posts >= 5) & (user_posts <= 10)].index\n",
    "df_reduced_data = df_data[df_data['user'].isin(social_users)]\n",
    "df_reduced_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close reduced dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "closed_df_reduced_data = df_reduced_data.copy()\n",
    "\n",
    "id_list = closed_df_reduced_data['id'].to_list()\n",
    "id_list.append('no_parent')\n",
    "\n",
    "closed_df_reduced_data.loc[~closed_df_reduced_data['parentid'].isin(id_list), 'parentid'] = 'no_parent'\n",
    "\n",
    "closed_df_reduced_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Close dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "closed_df_data = df_data.copy()\n",
    "\n",
    "id_list = closed_df_data['id'].to_list()\n",
    "id_list.append('no_parent')\n",
    "\n",
    "closed_df_data.loc[~closed_df_data['parentid'].isin(id_list), 'parentid'] = 'no_parent'\n",
    "\n",
    "closed_df_data.info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting the closed reduced dataframe to a csv dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "closed_df_reduced_data.to_csv(f'{case}-social-closed.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exporting the closed dataframe to a csv dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "closed_df_data.to_csv(f'{case}-closed.csv', index=False)"
   ]
  }
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