{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "795921f7",
   "metadata": {},
   "source": [
    "# Tradition vs Digital art on Twitter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c156361",
   "metadata": {},
   "source": [
    " By: Gloria Sladek "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0c546da",
   "metadata": {},
   "source": [
    "\n",
    "I conducted this analysis to compare the interaction of traditional and digital art on twitter. As an artist I was curios to see the difference between the two. Going into this analysis I believed that digital art would get more engagement due to the fact that twitter is a digital platform.\n",
    "\n",
    "Many artist use twitter as a way to interact with their users but I have not seen anyone talk about what type of art is best displayed on this platform. I am a digital artist and mostly use instagram to engage my audience though some people have told me I should try sharing my art on twitter. \n",
    " \n",
    "In order to answer my hypothesis I will be focusing on the _public metrics_ dataset from twitter which includes likes, comments, and retweets."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c3e6b46",
   "metadata": {},
   "source": [
    "### Hypothesis:\n",
    "Digital artists recieve more interaction on digital art posts rather than traditional. I predict that digital art recieves 10% more interaction then traditional art on twitter."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "618ba4e3",
   "metadata": {},
   "source": [
    "### Connecting to the Twitter API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f4d85fc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import pandas as pd\n",
    "import json\n",
    "import urllib\n",
    "import io\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7e338896",
   "metadata": {},
   "outputs": [],
   "source": [
    "bearer_token = pd.read_csv(\"twitterApp.txt\", header = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "171da77a",
   "metadata": {},
   "outputs": [],
   "source": [
    "token = bearer_token['Bearer Token'].iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bde5a965",
   "metadata": {},
   "outputs": [],
   "source": [
    "header = {'Authorization' : 'Bearer {}'.format(token)}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d90f5318",
   "metadata": {},
   "outputs": [],
   "source": [
    "endpoint_url = 'https://api.twitter.com/2/tweets/search/recent'"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02fefc24",
   "metadata": {},
   "source": [
    "### Creating Queries"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d2e79542",
   "metadata": {},
   "source": [
    "I created two queries to display posts using the hashtags of digital and traditional art to create queries and 2 data sets. By splitting the hashtags I can rate the engage meant in each catagory and compare it. One issue this may cause is overlap between the two types of data so I used the NOT parameter to ensure there was no overlap between the two datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "5ea0f8a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "query = urllib.parse.quote('(#digitalart OR #digital #art NOT (#traditionalart OR #traditional)) -is:retweet')\n",
    "query2 = urllib.parse.quote('(#traditionalart OR #traditional #art NOT (#digitalart OR #digital)) -is:retweet ') "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bb8b5e7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "tweet_fields = 'author_id,id,text,public_metrics,created_at,entities'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ab916bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "url = endpoint_url + '?query={}&tweet.fields={}&user.fields={}&media.fields={}&max_results=100'.format(query,tweet_fields,\"username\",\"preview_image_url\")\n",
    "url2 = endpoint_url + '?query={}&tweet.fields={}&user.fields={}&max_results=100'.format(query2,tweet_fields,\"username\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6405e7ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_1 = requests.request(\"GET\", url, headers=header, stream = True)\n",
    "traditional_1 = requests.request(\"GET\", url2, headers=header)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ed713ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_dict = json.loads(digital_1.text)\n",
    "traditional_dict = json.loads(traditional_1.text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f3a1344",
   "metadata": {},
   "source": [
    "### Creating the first page for 2 Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "184d8915",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_page1 = pd.DataFrame(digital_dict['data'])\n",
    "traditional_page1 = pd.DataFrame(traditional_dict['data'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "23ad851e",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_metrics1 = pd.DataFrame(list(digital_page1['public_metrics']))\n",
    "traditional_metrics1 = pd.DataFrame(list(traditional_page1['public_metrics']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "15056b62",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_page1['likes'] = digital_metrics1['like_count']\n",
    "digital_page1['retweets'] = digital_metrics1['retweet_count']\n",
    "digital_page1['comments'] = digital_metrics1['reply_count']\n",
    "\n",
    "traditional_page1['likes'] = traditional_metrics1['like_count']\n",
    "traditional_page1['retweets'] = traditional_metrics1['retweet_count']\n",
    "traditional_page1['comments'] = traditional_metrics1['reply_count']\n",
    "\n",
    "del digital_page1['public_metrics']\n",
    "del traditional_page1['public_metrics']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8576192",
   "metadata": {},
   "source": [
    "### Creating pages 2-4 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "026ba77b",
   "metadata": {},
   "source": [
    "I created a function to make this process less messy. This function takes an input of a dictionary, and uses that data to create a new \"page\" of datapoints. This way I can quickly collect 400 points of data from each query."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0e24248f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def next_page(dictionary):\n",
    "    next_pg = pd.DataFrame(dictionary['data'])\n",
    "    next_metric = pd.DataFrame(list(next_pg['public_metrics']))\n",
    "    ##combining the dataframes\n",
    "    next_pg['likes'] = next_metric['like_count']\n",
    "    next_pg['retweets'] = next_metric['retweet_count']\n",
    "    next_pg['comments'] = next_metric['reply_count']\n",
    "    del next_pg['public_metrics']\n",
    "    \n",
    "    return next_pg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "24c16ca7",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_token2 = url +'&next_token={}'.format(digital_dict['meta']['next_token'])\n",
    "digital_2 = requests.request(\"GET\",url = digital_token2, headers=header)\n",
    "digital_dict2 = json.loads(digital_2.text)\n",
    "\n",
    "digital_page2 = next_page(digital_dict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "21dc6a73",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_token3 = url +'&next_token={}'.format(digital_dict2['meta']['next_token'])\n",
    "digital_3 = requests.request(\"GET\",url = digital_token3, headers=header)\n",
    "digital_dict3 = json.loads(digital_3.text)\n",
    "\n",
    "digital_page3 = next_page(digital_dict3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "808cb5e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital_token4 = url +'&next_token={}'.format(digital_dict3['meta']['next_token'])\n",
    "digital_4 = requests.request(\"GET\",url = digital_token4, headers=header)\n",
    "digital_dict4 = json.loads(digital_4.text)\n",
    "\n",
    "digital_page4 = next_page(digital_dict3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2661ad12",
   "metadata": {},
   "outputs": [],
   "source": [
    "tradition_token2 = url +'&next_token={}'.format(traditional_dict['meta']['next_token'])\n",
    "traditional_2 = requests.request(\"GET\",url = tradition_token2, headers=header)\n",
    "traditional_dict2 = json.loads(traditional_2.text)\n",
    "\n",
    "traditional_page2 = next_page(traditional_dict2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d31a61e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "tradition_token3 = url +'&next_token={}'.format(traditional_dict2['meta']['next_token'])\n",
    "traditional_3 = requests.request(\"GET\",url = tradition_token3, headers=header)\n",
    "traditional_dict3 = json.loads(traditional_3.text)\n",
    "\n",
    "traditional_page3 = next_page(traditional_dict3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9189995f",
   "metadata": {},
   "outputs": [],
   "source": [
    "tradition_token4 = url +'&next_token={}'.format(traditional_dict3['meta']['next_token'])\n",
    "traditional_4 = requests.request(\"GET\",url = tradition_token4, headers=header)\n",
    "traditional_dict4 = json.loads(traditional_4.text)\n",
    "\n",
    "traditional_page4 = next_page(traditional_dict4)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b91c9c56",
   "metadata": {},
   "source": [
    "### Combine Into 2 data frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "54805e23",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital = pd.concat([digital_page1, digital_page2, digital_page3, digital_page4],ignore_index=True, sort=False)\n",
    "traditional = pd.concat([traditional_page1, traditional_page2, traditional_page3, traditional_page4], ignore_index=True, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17efdf9c",
   "metadata": {},
   "outputs": [
    {
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     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "digital.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "34798553",
   "metadata": {},
   "outputs": [
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       "397  2021-12-08T21:44:11.000Z   \n",
       "398  2021-12-08T21:44:09.000Z   \n",
       "399  2021-12-08T21:44:06.000Z   \n",
       "\n",
       "                                                  text                   id  \\\n",
       "395  commissi0n for @datcats (reddit)💚\\n#furry #fur...  1468697965044723712   \n",
       "396  Check this out SimbaChicks Nft 🔥🔥\\njoin our di...  1468697956056502280   \n",
       "397  Cappufuckinccino. https://t.co/LiffX0VsII #art...  1468697942349561861   \n",
       "398  Made a bit of progress on my draw this in your...  1468697934405550080   \n",
       "399  big uncomfortable smile\\n-\\n#furryoc #original...  1468697922887897099   \n",
       "\n",
       "               author_id  likes  retweets  comments  \n",
       "395  1154088388426752001      4         2         1  \n",
       "396  1468685639034015746      0         0         0  \n",
       "397            292619181      0         0         0  \n",
       "398            198144736      3         0         0  \n",
       "399  1282023970498318337     60        18         3  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traditional.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ebe40406",
   "metadata": {},
   "source": [
    "### Saving as a CSV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9ebf6b6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital.to_csv(r'C:\\Users\\glori\\Data in EMAT\\digital.csv')\n",
    "traditional.to_csv(r'C:\\Users\\glori\\Data in EMAT\\traditional.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c144064e",
   "metadata": {},
   "outputs": [],
   "source": [
    "digital = pd.read_csv ('digital.csv')\n",
    "traditional = pd.read_csv ('traditional.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "01dfcf9d",
   "metadata": {},
   "source": [
    "### Charting the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "184e817c",
   "metadata": {},
   "source": [
    "Once I had the data collected and organized I created bar charts to have a visual representation of each comparison. The _red_ bar in each chart indecates __traditional art__ and the _blue_ bar indicates __digital art__."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "1ef78134",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig0, ax0 = plt.subplots(2, 2, figsize = (10,10))\n",
    "ax0[0, 0].hist(digital['likes'], bins = 10,color = 'blue',alpha = .5)\n",
    "ax0[0, 0].hist(traditional['likes'], bins = 10, color = 'red', alpha = .5)\n",
    "ax0[0, 0].set_xlabel(\"Likes\")\n",
    "\n",
    "ax0[0, 1].hist(digital['comments'], bins = 5,color = 'blue',alpha = .5)\n",
    "ax0[0, 1].hist(traditional['comments'], bins = 5, color = 'red', alpha = .5)\n",
    "ax0[0, 1].set_xlabel(\"comments\")\n",
    "\n",
    "ax0[1, 0].hist(digital['retweets'], bins = 15,color = 'blue',alpha = .5)\n",
    "ax0[1, 0].hist(traditional['retweets'], bins = 15, color = 'red', alpha = .5)\n",
    "ax0[1, 0].set_xlabel(\"retweets\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "655ba5fd",
   "metadata": {},
   "source": [
    "__After reviewing the bar charts I chose to try looking at another type of chart to get a better visual of the difference between the two datasets__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d2820c56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig2, ax2 =plt.subplots(1,1, figsize = (10,10))\n",
    "\n",
    "#create a jitter in order to show if any overlap\n",
    "d_offset = np.random.randn(len(digital.index)) * .006\n",
    "t_offset = np.random.randn(len(traditional.index)) * .006\n",
    "d_x = [-.02] * len(digital.index)\n",
    "t_x = [.02] * len(traditional.index)\n",
    "d_jitter = d_x + d_offset\n",
    "t_jitter = t_x + t_offset\n",
    "\n",
    "ax2.scatter(d_jitter, digital['likes'], color = \"g\", alpha = .5)\n",
    "ax2.scatter(t_jitter, traditional['likes'], color = \"k\", alpha = .5)\n",
    "ax2.set_xlim(-.05,.05)\n",
    "\n",
    "ax2.set_xticks([-.02,.02])\n",
    "ax2.set_xticklabels(['Digital','Traditional'])\n",
    "ax2.set_ylabel(\"Likes\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "a6c50dc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig4, ax4 =plt.subplots(1,1, figsize = (10,10))\n",
    "\n",
    "#create a jitter in order to show if any overlap\n",
    "d_offset = np.random.randn(len(digital.index)) * .006\n",
    "t_offset = np.random.randn(len(traditional.index)) * .006\n",
    "d_x = [-.02] * len(digital.index)\n",
    "t_x = [.02] * len(traditional.index)\n",
    "d_jitter = d_x + d_offset\n",
    "t_jitter = t_x + t_offset\n",
    "\n",
    "ax4.scatter(d_jitter, digital['retweets'], color = \"g\", alpha = .5)\n",
    "ax4.scatter(t_jitter, traditional['retweets'], color = \"k\", alpha = .5)\n",
    "ax4.set_xlim(-.05,.05)\n",
    "\n",
    "ax4.set_xticks([-.02,.02])\n",
    "ax4.set_xticklabels(['Digital','Traditional'])\n",
    "ax4.set_ylabel(\"retweets\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "69132e49",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig5, ax5 =plt.subplots(1,1, figsize = (10,10))\n",
    "\n",
    "#create a jitter in order to show if any overlap\n",
    "d_offset = np.random.randn(len(digital.index)) * .006\n",
    "t_offset = np.random.randn(len(traditional.index)) * .006\n",
    "d_x = [-.02] * len(digital.index)\n",
    "t_x = [.02] * len(traditional.index)\n",
    "d_jitter = d_x + d_offset\n",
    "t_jitter = t_x + t_offset\n",
    "\n",
    "ax5.scatter(d_jitter, digital['comments'], color = \"g\", alpha = .5)\n",
    "ax5.scatter(t_jitter, traditional['comments'], color = \"k\", alpha = .5)\n",
    "ax5.set_xlim(-.05,.05)\n",
    "\n",
    "ax5.set_xticks([-.02,.02])\n",
    "ax5.set_xticklabels(['Digital','Traditional'])\n",
    "ax5.set_ylabel(\"comments\")\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5e154479",
   "metadata": {},
   "source": [
    "### Reviewing p-value of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6e51544",
   "metadata": {},
   "source": [
    "For all three p-values I can reject the null hypothesis. these values are all significant. However the p-value for the likes data is the most significant as it is the furthest from 1. \n",
    "\n",
    "It is also important to note that the outliers such as the one point with over 60 likes on the first dot plot can change the p-value."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7f57c24a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-6.4292170615685995, pvalue=2.2064201285047585e-10)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(digital['likes'],traditional['likes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "348231ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-2.0995690800418703, pvalue=0.03608026207692304)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(digital['comments'],traditional['comments'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b0b000e1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ttest_indResult(statistic=-3.693348916970628, pvalue=0.00023640052400642012)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.ttest_ind(digital['retweets'],traditional['retweets'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "096539c5",
   "metadata": {},
   "source": [
    "### Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd793ec8",
   "metadata": {},
   "source": [
    "After completing my analysis I found that my hypothesis was incorrect. I found this surprising and intriguing. Traditional art, at least from the dataset I collected, gets more interactions then digital art on twitter. \n",
    "\n",
    "There are a few reasons this may be the case. The biggest variable I can deduce from what I collected is the amount of time between the posts being published and the data being pulled. All the data I pulled was moments after the posts were published. So while the verdict for this data set is that traditional art gets more interaction on twitter, this may change the longer the posts are on the platform. In other words just because the posts get more interaction at first does not mean they get the most interaction over all.\n",
    "\n",
    "Another thing to consider is the use of hashtags. Although the majority of posts do use hashtags to properly describe their post this is not always the case, I have personally seen posts tagged as \"traditional art\" that were in fact mixed media, or that had a traditional feel on a digital piece.\n",
    "\n",
    "Lastly I want to reiterate that this analysis was done with only 400 lines of data per query. This is a major limitation that should not be overlooked. With that being said for the purposes of this analysis we can deduce that traditional art has more of a initial reaction on twitter in comparison the digital art."
   ]
  }
 ],
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