hacker_news.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Analysis of Hacker News Website\n",
    "\n",
    "This notebook analyzes posts on Hacker News. The data can be found [here](https://www.kaggle.com/datasets/hacker-news/hacker-news-posts). The data set contains one year of posts up to September 26, 2016.\n",
    "\n",
    "## Goals\n",
    "Analyze which type of post and which time of creation of this post creates the most interaction - measured by comments."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/simon/Schreibtisch/dataquest/hacker_news\n"
     ]
    }
   ],
   "source": [
    "# Make data import from path easy on any machine \n",
    "\n",
    "import os\n",
    "from config.definitions import ROOT_DIR\n",
    "# print(os.path.join(ROOT_DIR, \"data\", \"HN_posts_year_to_Sep_26_2016.csv\"))\n",
    "path = os.path.join(ROOT_DIR, \"data\", \"HN_posts_year_to_Sep_26_2016.csv\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Read in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "with open(path) as hacker_news:\n",
    "    hacker_texts = csv.reader(hacker_news)\n",
    "    hn = list(hacker_texts)\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Inspection of first five rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['id', 'title', 'url', 'num_points', 'num_comments', 'author', 'created_at'], ['12579008', 'You have two days to comment if you want stem cells to be classified as your own', 'http://www.regulations.gov/document?D=FDA-2015-D-3719-0018', '1', '0', 'altstar', '9/26/2016 3:26'], ['12579005', 'SQLAR  the SQLite Archiver', 'https://www.sqlite.org/sqlar/doc/trunk/README.md', '1', '0', 'blacksqr', '9/26/2016 3:24'], ['12578997', 'What if we just printed a flatscreen television on the side of our boxes?', 'https://medium.com/vanmoof/our-secrets-out-f21c1f03fdc8#.ietxmez43', '1', '0', 'pavel_lishin', '9/26/2016 3:19'], ['12578989', 'algorithmic music', 'http://cacm.acm.org/magazines/2011/7/109891-algorithmic-composition/fulltext', '1', '0', 'poindontcare', '9/26/2016 3:16']]\n"
     ]
    }
   ],
   "source": [
    "print(hn[:5])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We analyze what drives comments below posts. In a first step, we get an understanding of the distribution of comments below posts."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{0: 212718, 1: 28055, 2: 9731, 3: 5016, 4: 3272, 5: 2208, 6: 1787, 7: 1390, 8: 1115, 9: 1064, 10: 962, 11: 846, 12: 726, 13: 631, 14: 626, 15: 560, 16: 550, 17: 514, 18: 524, 19: 485, 20: 435, 21: 448, 22: 396, 23: 348, 24: 375, 25: 343, 26: 355, 27: 320, 28: 302, 29: 290, 30: 309, 31: 271, 32: 315, 33: 260, 34: 230, 35: 273, 36: 229, 37: 262, 38: 234, 39: 244, 40: 238, 41: 239, 42: 235, 43: 239, 44: 233, 45: 253, 46: 211, 47: 212, 48: 212, 49: 213, 50: 215, 51: 204, 52: 218, 53: 207, 54: 192, 55: 197, 56: 186, 57: 186, 58: 179, 59: 192, 60: 168, 61: 154, 62: 182, 63: 163, 64: 164, 65: 157, 66: 162, 67: 149, 68: 147, 69: 166, 70: 135, 71: 131, 72: 135, 73: 119, 74: 115, 75: 139, 76: 114, 77: 131, 78: 104, 79: 109, 80: 116, 81: 114, 82: 106, 83: 113, 84: 120, 85: 99, 86: 93, 87: 106, 88: 89, 89: 84, 90: 94, 91: 88, 92: 93, 93: 85, 94: 90, 95: 79, 96: 86, 97: 61, 98: 79, 99: 83, 100: 76, 101: 68, 102: 59, 103: 66, 104: 68, 105: 76, 106: 68, 107: 59, 108: 67, 109: 75, 110: 63, 111: 53, 112: 56, 113: 51, 114: 50, 115: 52, 116: 54, 117: 42, 118: 41, 119: 70, 120: 50, 121: 41, 122: 39, 123: 41, 124: 53, 125: 36, 126: 41, 127: 56, 128: 42, 129: 52, 130: 52, 131: 52, 132: 47, 133: 40, 134: 29, 135: 44, 136: 41, 137: 38, 138: 45, 139: 41, 140: 34, 141: 24, 142: 33, 143: 27, 144: 36, 145: 51, 146: 40, 147: 41, 148: 29, 149: 35, 150: 26, 151: 32, 152: 34, 153: 35, 154: 34, 155: 37, 156: 27, 157: 38, 158: 25, 159: 28, 160: 29, 161: 18, 162: 29, 163: 24, 164: 32, 165: 30, 166: 29, 167: 27, 168: 30, 169: 33, 170: 28, 171: 31, 172: 15, 173: 25, 174: 29, 175: 27, 176: 19, 177: 19, 178: 16, 179: 18, 180: 30, 181: 23, 182: 19, 183: 19, 184: 23, 185: 10, 186: 25, 187: 26, 188: 28, 189: 21, 190: 12, 191: 18, 192: 18, 193: 18, 194: 19, 195: 17, 196: 14, 197: 22, 198: 16, 199: 21, 200: 16, 201: 12, 202: 20, 203: 18, 204: 21, 205: 18, 206: 15, 207: 10, 208: 13, 209: 22, 210: 13, 211: 18, 212: 13, 213: 15, 214: 7, 215: 11, 216: 18, 217: 11, 218: 11, 219: 16, 220: 9, 221: 11, 222: 11, 223: 19, 224: 12, 225: 7, 226: 12, 227: 11, 228: 13, 229: 18, 230: 6, 231: 7, 232: 25, 233: 14, 234: 13, 235: 12, 236: 6, 237: 14, 238: 8, 239: 10, 240: 13, 241: 9, 242: 11, 243: 7, 244: 10, 245: 6, 246: 13, 247: 4, 248: 11, 249: 10, 250: 12, 251: 9, 252: 10, 253: 5, 254: 5, 255: 12, 256: 5, 257: 9, 258: 6, 259: 9, 260: 5, 261: 7, 262: 10, 263: 5, 264: 4, 265: 2, 266: 12, 267: 7, 268: 9, 269: 6, 270: 5, 271: 3, 272: 7, 273: 6, 274: 8, 275: 5, 276: 4, 277: 4, 278: 3, 279: 8, 280: 9, 281: 7, 282: 7, 283: 5, 284: 5, 285: 2, 286: 3, 287: 5, 288: 5, 289: 4, 290: 6, 291: 2, 292: 3, 293: 2, 294: 8, 295: 4, 296: 4, 297: 6, 298: 6, 299: 7, 300: 1, 301: 6, 302: 5, 303: 4, 304: 5, 305: 2, 306: 3, 307: 5, 308: 4, 309: 5, 310: 3, 311: 8, 312: 2, 313: 6, 314: 7, 315: 2, 316: 5, 317: 5, 318: 6, 319: 3, 320: 2, 321: 4, 322: 4, 323: 7, 324: 3, 325: 2, 326: 5, 327: 5, 328: 1, 329: 1, 330: 3, 331: 4, 332: 3, 333: 5, 334: 2, 335: 2, 336: 5, 337: 3, 338: 5, 339: 6, 340: 4, 341: 5, 342: 3, 343: 3, 344: 1, 345: 1, 346: 3, 347: 2, 348: 6, 349: 1, 350: 1, 351: 6, 352: 3, 353: 2, 354: 2, 355: 1, 356: 4, 357: 1, 358: 3, 359: 7, 360: 1, 361: 3, 362: 4, 363: 1, 364: 6, 365: 4, 367: 2, 368: 2, 369: 1, 370: 3, 371: 2, 372: 2, 373: 1, 374: 2, 375: 1, 376: 6, 377: 1, 379: 3, 380: 1, 381: 8, 383: 3, 384: 2, 385: 3, 386: 3, 387: 3, 388: 1, 389: 3, 390: 4, 391: 1, 392: 4, 393: 2, 397: 3, 398: 3, 400: 2, 401: 2, 403: 2, 405: 1, 406: 1, 407: 2, 408: 2, 409: 1, 410: 1, 412: 2, 413: 2, 414: 1, 417: 1, 418: 1, 419: 2, 420: 3, 422: 1, 423: 3, 424: 2, 425: 1, 426: 1, 427: 1, 428: 2, 429: 2, 430: 2, 431: 4, 432: 4, 433: 1, 434: 1, 435: 3, 437: 3, 438: 1, 439: 2, 441: 1, 442: 1, 443: 1, 444: 1, 445: 1, 446: 1, 447: 1, 448: 3, 449: 3, 450: 1, 451: 1, 452: 1, 453: 2, 454: 1, 455: 1, 456: 1, 457: 2, 458: 2, 460: 1, 461: 2, 462: 3, 463: 1, 464: 1, 465: 1, 467: 1, 471: 1, 472: 1, 475: 1, 476: 1, 477: 2, 479: 2, 481: 1, 483: 2, 484: 1, 485: 1, 486: 1, 490: 1, 491: 1, 492: 1, 494: 1, 495: 2, 499: 2, 502: 4, 503: 1, 504: 2, 508: 1, 509: 1, 511: 2, 512: 1, 513: 3, 514: 2, 515: 2, 516: 1, 517: 1, 519: 2, 520: 1, 521: 1, 526: 1, 529: 1, 534: 1, 536: 1, 539: 1, 544: 1, 547: 1, 548: 1, 550: 1, 552: 2, 555: 1, 558: 1, 565: 1, 567: 1, 568: 2, 569: 1, 573: 1, 581: 1, 582: 1, 583: 1, 586: 1, 599: 1, 608: 1, 618: 1, 624: 1, 626: 1, 631: 1, 635: 1, 644: 1, 650: 1, 660: 1, 672: 1, 677: 1, 685: 1, 691: 1, 695: 1, 704: 1, 705: 2, 716: 1, 720: 1, 726: 1, 760: 2, 767: 1, 778: 1, 781: 1, 809: 1, 825: 1, 831: 1, 842: 1, 854: 1, 868: 1, 870: 1, 893: 1, 896: 1, 898: 1, 910: 1, 937: 1, 947: 1, 967: 1, 973: 1, 1007: 1, 1120: 1, 1448: 1, 1733: 1, 2531: 1}\n",
      "293119\n"
     ]
    }
   ],
   "source": [
    "comments_freq = {}\n",
    "\n",
    "for row in hn[1:]:\n",
    "    comment = row[4]\n",
    "    if comment not in comments_freq:\n",
    "        comments_freq[comment] = 1\n",
    "    else:\n",
    "        comments_freq[comment] += 1\n",
    "\n",
    "comments_freq_int = {int(key):value for key, value in comments_freq.items()}\n",
    "comments_freq_sorted = dict(sorted(comments_freq_int.items()))\n",
    "print(comments_freq_sorted)\n",
    "\n",
    "total_comments = sum(comments_freq_sorted.values())\n",
    "print(total_comments)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "What is the share of posts that received 0 comments?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7257052596385768\n"
     ]
    }
   ],
   "source": [
    "share_zero_comments = comments_freq[\"0\"] / total_comments\n",
    "print(share_zero_comments)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's visualize the share of posts with and without comments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.style as style\n",
    "\n",
    "# create dictionary\n",
    "data = {'0 comments': share_zero_comments, 'More than 0 comments': (1 - share_zero_comments)}\n",
    "\n",
    "# for the looks\n",
    "style.use(\"fivethirtyeight\")\n",
    "\n",
    "# create pie chart\n",
    "plt.pie(data.values(), labels=data.keys(),autopct='%1.1f%%')\n",
    "\n",
    "# add a legend\n",
    "# plt.legend(data.keys(), bbox_to_anchor=(1.3,0.8), loc=\"upper right\", bbox_transform=plt.gcf().transFigure)\n",
    "\n",
    "plt.title('Share of 0 comments under posts')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In what follows we will focus on the analysis of posts that have received comments. To this end we select the appropriate data and save it as a new variable. Similarly, we will separate out the column names of the data set's variables."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['12579008', 'You have two days to comment if you want stem cells to be classified as your own', 'http://www.regulations.gov/document?D=FDA-2015-D-3719-0018', '1', '0', 'altstar', '9/26/2016 3:26'], ['12579005', 'SQLAR  the SQLite Archiver', 'https://www.sqlite.org/sqlar/doc/trunk/README.md', '1', '0', 'blacksqr', '9/26/2016 3:24'], ['12578997', 'What if we just printed a flatscreen television on the side of our boxes?', 'https://medium.com/vanmoof/our-secrets-out-f21c1f03fdc8#.ietxmez43', '1', '0', 'pavel_lishin', '9/26/2016 3:19'], ['12578989', 'algorithmic music', 'http://cacm.acm.org/magazines/2011/7/109891-algorithmic-composition/fulltext', '1', '0', 'poindontcare', '9/26/2016 3:16'], ['12578979', 'How the Data Vault Enables the Next-Gen Data Warehouse and Data Lake', 'https://www.talend.com/blog/2016/05/12/talend-and-Â\\x93the-data-vaultÂ\\x94', '1', '0', 'markgainor1', '9/26/2016 3:14']]\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "headers = hn[0]\n",
    "hn_data = hn[1:]\n",
    "print(hn_data[:5])\n",
    "\n",
    "hn_data_comments = [item for item in hn_data if item[4] != \"0\"]\n",
    "print((total_comments - comments_freq[\"0\"]) == len(hn_data_comments))"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ask HN and Shown HN posts\n",
    "\n",
    "Now that we have loaded and prepared the data, we want to analyze two particular type of posts in the forum: `Ask HN` and `Show HN`.\n",
    "\n",
    "`Ask HN` posts pose a question to the Hacker News community.\n",
    "`Show HN` posts present something to the community that is potentially interesting such as projects or products.\n",
    "\n",
    "As a sales, marketing, business development, strategy or communication person one might be interested in finding out which type of post elicits more user interaction measured in comments. Specifically,\n",
    "+ Do `Ask HN` or `Show HN` posts receive more comments?\n",
    "+ Does the time of day a post was created have an influence on the amount of comments?\n",
    "\n",
    "Let's separate out the different posts:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6911\n",
      "5059\n",
      "68431\n",
      "[['12578908', 'Ask HN: What TLD do you use for local development?', '', '4', '7', 'Sevrene', '9/26/2016 2:53'], ['12578522', 'Ask HN: How do you pass on your work when you die?', '', '6', '3', 'PascLeRasc', '9/26/2016 1:17'], ['12577870', 'Ask HN: Why join a fund when you can be an angel?', '', '1', '3', 'anthony_james', '9/25/2016 22:48'], ['12577647', 'Ask HN: Someone uses stock trading as passive income?', '', '5', '2', '00taffe', '9/25/2016 21:50'], ['12576946', 'Ask HN: How hard would it be to make a cheap, hackable phone?', '', '2', '1', 'hkt', '9/25/2016 19:30']]\n"
     ]
    }
   ],
   "source": [
    "ask_posts = []\n",
    "show_posts = []\n",
    "other_posts = []\n",
    "\n",
    "for post in hn_data_comments:\n",
    "    title = post[1]\n",
    "    if title.lower().startswith(\"ask hn\"):\n",
    "        ask_posts.append(post)\n",
    "    elif title.lower().startswith(\"show hn\"):\n",
    "        show_posts.append(post)\n",
    "    else:\n",
    "        other_posts.append(post)\n",
    "\n",
    "print(len(ask_posts))\n",
    "print(len(show_posts))\n",
    "print(len(other_posts))\n",
    "\n",
    "print(ask_posts[:5])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Do `Ask` or `Show` posts receive more comments on average?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "13.744175951381855\n",
      "9.810832180272781\n"
     ]
    }
   ],
   "source": [
    "total_ask_comments = 0\n",
    "\n",
    "for post in ask_posts:\n",
    "    comments = int(post[4])\n",
    "    total_ask_comments += comments\n",
    "\n",
    "avg_ask_comments = total_ask_comments / len(ask_posts)\n",
    "print(avg_ask_comments)\n",
    "\n",
    "\n",
    "total_show_comments = 0\n",
    "\n",
    "for post in show_posts:\n",
    "    comments = int(post[4])\n",
    "    total_show_comments += comments\n",
    "\n",
    "avg_show_comments = total_show_comments / len(show_posts)\n",
    "print(avg_show_comments)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`Ask` posts receive on average approx. 14 comments, whereas `Show` comments receive approx. 10."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since people respond more actively to `Ask` posts, one can continue to analyze at which time of the day most `ask` posts are received and comments added:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[['9/26/2016 2:53', 7], ['9/26/2016 1:17', 3], ['9/25/2016 22:48', 3], ['9/25/2016 21:50', 2], ['9/25/2016 19:30', 1]]\n",
      "{2: 2996, 1: 2089, 22: 3372, 21: 4500, 19: 3954, 17: 5547, 15: 18525, 14: 4972, 13: 7245, 11: 2797, 10: 3013, 9: 1477, 7: 1585, 3: 2154, 16: 4466, 8: 2362, 0: 2277, 23: 2297, 20: 4462, 18: 4877, 12: 4234, 4: 2360, 6: 1587, 5: 1838}\n",
      "[[2, 13.198237885462555], [1, 9.367713004484305], [22, 11.749128919860627], [21, 11.056511056511056], [19, 9.414285714285715], [17, 13.73019801980198], [15, 39.66809421841542], [14, 13.153439153439153], [13, 22.2239263803681], [11, 11.143426294820717], [10, 13.757990867579908], [9, 8.392045454545455], [7, 10.095541401273886], [3, 10.160377358490566], [16, 10.76144578313253], [8, 12.43157894736842], [0, 9.857142857142858], [23, 8.322463768115941], [20, 11.38265306122449], [18, 10.789823008849558], [12, 15.452554744525548], [4, 12.688172043010752], [6, 9.017045454545455], [5, 11.139393939393939]]\n"
     ]
    }
   ],
   "source": [
    "import datetime as dt\n",
    "\n",
    "result_list = []\n",
    "\n",
    "for post in ask_posts:\n",
    "    created_at = post[6]\n",
    "    comments = int(post[4])\n",
    "    result_list.append([created_at, comments])\n",
    "\n",
    "print(result_list[:5])\n",
    "\n",
    "counts_by_hour = {}\n",
    "comments_by_hour = {}\n",
    "\n",
    "for result in result_list:\n",
    "    time = result[0]\n",
    "    time_dt = dt.datetime.strptime(time, \"%m/%d/%Y %H:%M\")\n",
    "    hour = time_dt.hour\n",
    "    if hour not in counts_by_hour:\n",
    "        counts_by_hour[hour] = 1\n",
    "        comments_by_hour[hour] = result[1]\n",
    "    else:\n",
    "        counts_by_hour[hour] += 1\n",
    "        comments_by_hour[hour] += result[1]\n",
    "\n",
    "print(comments_by_hour)\n",
    "\n",
    "avg_by_hour = []\n",
    "for hour in counts_by_hour:\n",
    "    avg_by_hour.append([hour, (comments_by_hour[hour] / counts_by_hour[hour])])\n",
    "\n",
    "print(avg_by_hour)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's tidy this up a bit:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 5 Hours for Ask Posts Comments\n",
      "Ask posts posted at 15:00 generate 39.67 comments on average.\n",
      "Ask posts posted at 13:00 generate 22.22 comments on average.\n",
      "Ask posts posted at 12:00 generate 15.45 comments on average.\n",
      "Ask posts posted at 10:00 generate 13.76 comments on average.\n",
      "Ask posts posted at 17:00 generate 13.73 comments on average.\n"
     ]
    }
   ],
   "source": [
    "swap_avg_by_hour = []\n",
    "for row in avg_by_hour:\n",
    "    swap_avg_by_hour.append([row[1], row[0]])\n",
    "\n",
    "swap_avg_by_hour = sorted(swap_avg_by_hour, reverse=True)\n",
    "print(\"Top 5 Hours for Ask Posts Comments\")\n",
    "for entry in swap_avg_by_hour[:5]:\n",
    "    avg = entry[0]\n",
    "    hr = dt.time(entry[1], 00)\n",
    "    print(f\"Ask posts posted at {hr.strftime('%H:%M')} generate {avg:.2f} comments on average.\")\n",
    "    \n",
    "\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hour that receives the most comments per post on average is 15:00, with an average of 38.59 comments per post. There's about a 60% increase in the number of comments between the hours with the highest and second highest average number of comments."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "In this project, I analyzed posts from Hacker News to determine which type of post as well as time of post creation receives the most comments on average. I find that in order to maximize the amount of comments a post receives, the post should be written as an `Ask` post and created between 15:00 and 16:00. This finding holds for for posts that receive comments at all."
   ]
  }
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