act_report.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# WeRateDogs is in the Twitter Doghouse\n",
    "\n",
    "WeRateDogs (@dog_rates) boasts 8.9 million followers, a hefty number indeed. Here, we examine the account activity of WeRateDogs for the period of the dataset, and examine the relationship between retweets and favorites."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://pbs.twimg.com/media/CT4udn0WwAA0aMy.jpg\" width=\"600\" height=\"400\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url=image_pred_df.jpg_url[0], width=600, height=400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "<div>Image(url=image_pred_df.jpg_url[0], width=600, height=400, align)</div"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from IPython.display import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section we will visualize the cleaned dataset and examine the following:\n",
    "1. How did the monthly number of tweets on WeRateDogs change over time?\n",
    "2. What is the relationship between retweets and favorites?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 2356 entries, 2017-08-01 16:23:56+00:00 to 2015-11-15 22:32:08+00:00\n",
      "Data columns (total 9 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   tweet_id            2356 non-null   int64  \n",
      " 1   source              2356 non-null   object \n",
      " 2   text                2356 non-null   object \n",
      " 3   rating_numerator    2356 non-null   int64  \n",
      " 4   rating_denominator  2356 non-null   int64  \n",
      " 5   name                2286 non-null   object \n",
      " 6   retweet_count       877 non-null    float64\n",
      " 7   favorite_count      877 non-null    float64\n",
      " 8   dogtionary          399 non-null    object \n",
      "dtypes: float64(2), int64(3), object(4)\n",
      "memory usage: 184.1+ KB\n"
     ]
    }
   ],
   "source": [
    "# import archive master as df\n",
    "df = pd.read_csv('twitter_archive_master.csv')\n",
    "df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
    "df.set_index('timestamp', inplace=True)\n",
    "df.info()\n",
    "\n",
    "# import image predictions\n",
    "image_pred_df = pd.read_csv('image_predictions.tsv', sep = '\\t')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tweets Over Time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "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>monthly_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>timestamp</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-11-30 00:00:00+00:00</th>\n",
       "      <td>302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-31 00:00:00+00:00</th>\n",
       "      <td>388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-31 00:00:00+00:00</th>\n",
       "      <td>194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-29 00:00:00+00:00</th>\n",
       "      <td>125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-31 00:00:00+00:00</th>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           monthly_count\n",
       "timestamp                               \n",
       "2015-11-30 00:00:00+00:00            302\n",
       "2015-12-31 00:00:00+00:00            388\n",
       "2016-01-31 00:00:00+00:00            194\n",
       "2016-02-29 00:00:00+00:00            125\n",
       "2016-03-31 00:00:00+00:00            137"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create copy of df\n",
    "monthly_tweets = df.groupby(pd.Grouper(level='timestamp', freq=\"M\")).count().reset_index()\n",
    "monthly_tweets = monthly_tweets[['timestamp','tweet_id']]\n",
    "monthly_tweets.rename(columns={'tweet_id':'monthly_count'},inplace=True)\n",
    "monthly_tweets.set_index('timestamp',inplace=True)\n",
    "monthly_tweets.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot tweets by month\n",
    "monthly_tweets.plot(figsize=(10,8), \n",
    "                   title='@WeRateDogs Tweet Count by Month',\n",
    "                   xlabel='Date',\n",
    "                   ylabel='Count',\n",
    "                   legend=False);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "#T_5e73b_row21_col0{\n",
       "            background-color:  yellow;\n",
       "        }</style><table id=\"T_5e73b_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >monthly_count</th>    </tr>    <tr>        <th class=\"index_name level0\" >timestamp</th>        <th class=\"blank\" ></th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_5e73b_level0_row0\" class=\"row_heading level0 row0\" >2015-11-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row0_col0\" class=\"data row0 col0\" >nan%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row1\" class=\"row_heading level0 row1\" >2015-12-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row1_col0\" class=\"data row1 col0\" >28.48%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row2\" class=\"row_heading level0 row2\" >2016-01-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row2_col0\" class=\"data row2 col0\" >-50.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row3\" class=\"row_heading level0 row3\" >2016-02-29 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row3_col0\" class=\"data row3 col0\" >-35.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row4\" class=\"row_heading level0 row4\" >2016-03-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row4_col0\" class=\"data row4 col0\" >9.60%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row5\" class=\"row_heading level0 row5\" >2016-04-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row5_col0\" class=\"data row5 col0\" >-56.20%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row6\" class=\"row_heading level0 row6\" >2016-05-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row7\" class=\"row_heading level0 row7\" >2016-06-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row7_col0\" class=\"data row7 col0\" >61.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row8\" class=\"row_heading level0 row8\" >2016-07-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row8_col0\" class=\"data row8 col0\" >8.25%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row9\" class=\"row_heading level0 row9\" >2016-08-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row9_col0\" class=\"data row9 col0\" >-28.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row10\" class=\"row_heading level0 row10\" >2016-09-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row10_col0\" class=\"data row10 col0\" >12.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row11\" class=\"row_heading level0 row11\" >2016-10-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row11_col0\" class=\"data row11 col0\" >4.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row12\" class=\"row_heading level0 row12\" >2016-11-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row12_col0\" class=\"data row12 col0\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row13\" class=\"row_heading level0 row13\" >2016-12-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row13_col0\" class=\"data row13 col0\" >-20.45%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row14\" class=\"row_heading level0 row14\" >2017-01-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row14_col0\" class=\"data row14 col0\" >34.29%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row15\" class=\"row_heading level0 row15\" >2017-02-28 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row15_col0\" class=\"data row15 col0\" >-6.38%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row16\" class=\"row_heading level0 row16\" >2017-03-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row16_col0\" class=\"data row16 col0\" >-22.73%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row17\" class=\"row_heading level0 row17\" >2017-04-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row17_col0\" class=\"data row17 col0\" >-11.76%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row18\" class=\"row_heading level0 row18\" >2017-05-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row18_col0\" class=\"data row18 col0\" >-6.67%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row19\" class=\"row_heading level0 row19\" >2017-06-30 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row19_col0\" class=\"data row19 col0\" >5.36%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row20\" class=\"row_heading level0 row20\" >2017-07-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row20_col0\" class=\"data row20 col0\" >-5.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_5e73b_level0_row21\" class=\"row_heading level0 row21\" >2017-08-31 00:00:00+00:00</th>\n",
       "                        <td id=\"T_5e73b_row21_col0\" class=\"data row21 col0\" >-96.43%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x120ad7a7b80>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pct_changes = monthly_tweets.pct_change()\n",
    "pct_changes.style.format(\"{:.2%}\").highlight_min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Over the course of the period, the tweet count for the @WeRateDogs account decliined significantly over the period of 2015 to 2017. In the last two months of 2015, the account's tweet count was on the rise, achieving an all time high for the period in December with 388 tweets. However, this sharply contrasts with the account's activity in the following months.\n",
    "\n",
    "The account had two consecutive months of decreased activity, falling 50% and ~36% in January and February of 2016 respectively. This was followed by an increase in activity in March, but the account would come nowhere near the all-time highs it achieved again. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Retweet and Favorites"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "On Twitter, favorites serve as an effective proxy for user engagement and preferences. However, the reach of online content is ultimately more enhanced by retweets. With retweets, an account's activity can increase the impressions a tweet has by several orders of magnitude. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Groupby name and select records with RTs and favorite count information\n",
    "RT_Fav = df[df[['retweet_count','favorite_count']].isna().any(1) == False] # select records where RTs and favorites not empty\n",
    "RT_Fav = RT_Fav[RT_Fav['favorite_count'] != 0] # remove retweets only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "y=2.582831x+5246.972541\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = RT_Fav.plot.scatter(x='retweet_count', \n",
    "                                y='favorite_count',\n",
    "                                title='Retweets and Favorites',\n",
    "                                xlabel='Retweets',\n",
    "                                ylabel='Favorites',\n",
    "                                xlim=(0,60000),\n",
    "                               ylim=(0,200000),\n",
    "                               figsize=(10,8))\n",
    "\n",
    "ax.ticklabel_format(style='plain') # change scientific formatting to plain\n",
    "# ax.get_yaxis().set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) # format y-axis labels\n",
    "# ax.get_xaxis().set_major_formatter(mpl.ticker.StrMethodFormatter('{x:,.0f}')) # format x-axis labels\n",
    "\n",
    "RTs = RT_Fav['retweet_count']\n",
    "fav = RT_Fav['favorite_count']\n",
    "plt.plot(RTs,fav,'o')\n",
    "\n",
    "# calc the trendline\n",
    "z = numpy.polyfit(RTs, fav, 1)\n",
    "p = numpy.poly1d(z)\n",
    "plt.plot(RTs,p(RTs),\"r--\")\n",
    "# the line equation:\n",
    "print(\"y={:.6f}x+{:.6f}\".format(z[0],z[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There is a clear positive relationshup between retweets and favorites. If a WeRateDogs tweet were to get no retweets, it could be expected that it would get about 5,200 likes based on the fitted linear relationship. For each additional retweet, the number of favorites is increased by 2.58. Each retweet nearly triples the number of likes that a post gets!\n",
    "\n",
    "WeRateDogs may not be stuck in the dog house after all. If the page focuses on gaining retweets of its content, it can expect more user engagement and impressions to continue to grow and share cute dog photos to brighten everyone's day."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "DLP (venv)",
   "language": "python",
   "name": "dlp"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}