Claims Engagement and Validity 10.3.21.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Claims Data: Relationships to Twitter Engagement, and Potential Indicators of Validity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import SMOTE\n",
    "from sklearn import metrics\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "import imblearn\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import recall_score\n",
    "from sklearn.metrics import precision_score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('/Users/mfeene/Desktop/hackathon_2021/claims_topics_eng.xlsx')\n",
    "\n",
    "# fill missing values\n",
    "df = df.fillna(0)\n",
    "\n",
    "\n",
    "df = df[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, 'eval', 'tweet_counts', 'replies']]\n",
    "\n",
    "df.columns = ['topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', \n",
    "             'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'topic_15', 'topic_16', 'topic_17',\n",
    "             'topic_18', 'topic_19', 'topic_20', 'eval', 'tweet_counts', 'replies']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
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       "    topic_1   topic_2   topic_3   topic_4   topic_5   topic_6   topic_7  \\\n",
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       "\n",
       "   topic_8   topic_9  topic_10  ...  topic_14  topic_15  topic_16  topic_17  \\\n",
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       "4  0.00106  0.002529  0.000719  ...   0.00138  0.000641  0.001823  0.002395   \n",
       "\n",
       "   topic_18  topic_19  topic_20  eval  tweet_counts  replies  \n",
       "0  0.002773  0.175692  0.169631  real           1.0      2.0  \n",
       "1  0.002773  0.173437  0.169430  real          10.0     14.0  \n",
       "2  0.002773  0.175692  0.170225  real           0.0      0.0  \n",
       "3  0.002773  0.175692  0.169681  real           0.0      0.0  \n",
       "4  0.002773  0.174114  0.169673  real           0.0      0.0  \n",
       "\n",
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     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topic and Twitter Engagement Correlations "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Correlations with topic and engagement\n",
    "# The more likely that a claim was to belong to a certain topic, the higher engagement"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
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       "                     0         1         2        3         4         5  \\\n",
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       "\n",
       "                     6         7         8         9  ...        11        12  \\\n",
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       "\n",
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       "\n",
       "                    19  tweet_counts  \n",
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     "execution_count": 166,
     "metadata": {},
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   "source": [
    "df = pd.read_excel('/Users/mfeene/Desktop/hackathon_2021/claims_topics_eng.xlsx')\n",
    "\n",
    "# fill missing values\n",
    "df = df.fillna(0)\n",
    "\n",
    "tweet_eng = df[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, 'tweet_counts']].corr().iloc[-1:]\n",
    "#tweet_eng.to_csv('tweet_eng.csv')\n",
    "\n",
    "tweet_eng\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
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       "                0         1         2         3         4         5         6  \\\n",
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       "\n",
       "                7         8         9  ...        11        12        13  \\\n",
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       "\n",
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       "replies -0.020968  0.024363 -0.016541 -0.008914  0.093077  0.035675      1.0  \n",
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     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df = pd.read_excel('/Users/mfeene/Desktop/hackathon_2021/claims_topics_eng.xlsx')\n",
    "\n",
    "# fill missing values\n",
    "df = df.fillna(0)\n",
    "\n",
    "rep_eng = df[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, 'replies']].corr().iloc[-1:]\n",
    "#rep_eng.to_csv('rep_eng.csv')\n",
    "\n",
    "rep_eng\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Topics as Potential Indicators of Claim Validity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('/Users/mfeene/Desktop/hackathon_2021/claims_topics_eng.xlsx')\n",
    "\n",
    "# fill missing values\n",
    "df = df.fillna(0)\n",
    "\n",
    "\n",
    "df = df[[0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19, 'eval', 'tweet_counts', 'replies']]\n",
    "\n",
    "df.columns = ['topic_1', 'topic_2', 'topic_3', 'topic_4', 'topic_5', 'topic_6', 'topic_7', 'topic_8', 'topic_9', \n",
    "             'topic_10', 'topic_11', 'topic_12', 'topic_13', 'topic_14', 'topic_15', 'topic_16', 'topic_17',\n",
    "             'topic_18', 'topic_19', 'topic_20', 'eval', 'tweet_counts', 'tweet_replies']\n",
    "\n",
    "\n",
    "# Also encode the target variable\n",
    "lb_make = LabelEncoder()\n",
    "df['eval_encoded'] = lb_make.fit_transform(df['eval']) # 0 = fake, 1 = real\n",
    "df = df.drop('eval', axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    490\n",
       "0     28\n",
       "Name: eval_encoded, dtype: int64"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Serious class imbalance- way more real than fake claims in the data\n",
    "df['eval_encoded'].value_counts()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train test split\n",
    "x = df.drop('eval_encoded', axis = 1)\n",
    "y = df['eval_encoded']\n",
    "\n",
    "x_train, x_test, y_train, y_test = train_test_split(x, y, stratify = y, test_size = 0.3, random_state = 123)\n",
    "\n",
    "# Define oversampling strategy- SMOTE\n",
    "sm = SMOTE(random_state = 1)\n",
    "\n",
    "# Fit and apply the over sampling to the training data\n",
    "x_train_res, y_train_res = sm.fit_sample(x_train, y_train)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.9358974358974359\n",
      "\n",
      "AUC: 0.6706081081081081\n",
      "\n",
      "Confusion Matrix: [[  3   5]\n",
      " [  5 143]]\n"
     ]
    }
   ],
   "source": [
    "# Random Forest\n",
    "rf = RandomForestClassifier(random_state = 123, \n",
    "                            n_jobs = -1, \n",
    "                            class_weight = 'balanced', \n",
    "                            n_estimators = 1000)\n",
    "    \n",
    "rf.fit(x_train_res, y_train_res)\n",
    "\n",
    "y_pred = rf.predict(x_test)\n",
    "\n",
    "## Accuracy score\n",
    "print('Accuracy:', metrics.accuracy_score(y_test, y_pred))\n",
    "print('')\n",
    "\n",
    "\n",
    "# ROC AUC \n",
    "print('AUC:', metrics.roc_auc_score(y_test, y_pred))\n",
    "print('')\n",
    "\n",
    "\n",
    "# Confusion matrix\n",
    "print('Confusion Matrix:', metrics.confusion_matrix(y_test, y_pred))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Random Forest Feature Importance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "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>Features</th>\n",
       "      <th>Importance Score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>tweet_counts</td>\n",
       "      <td>0.192403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>topic_14</td>\n",
       "      <td>0.127279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>tweet_replies</td>\n",
       "      <td>0.121006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>topic_7</td>\n",
       "      <td>0.081235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>topic_13</td>\n",
       "      <td>0.066782</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>topic_1</td>\n",
       "      <td>0.062765</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>topic_3</td>\n",
       "      <td>0.056334</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>topic_6</td>\n",
       "      <td>0.055789</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>topic_5</td>\n",
       "      <td>0.047987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>topic_12</td>\n",
       "      <td>0.038198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>topic_20</td>\n",
       "      <td>0.027854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>topic_4</td>\n",
       "      <td>0.027688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>topic_8</td>\n",
       "      <td>0.022315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>topic_2</td>\n",
       "      <td>0.015451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>topic_9</td>\n",
       "      <td>0.012243</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>topic_19</td>\n",
       "      <td>0.010560</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>topic_15</td>\n",
       "      <td>0.008969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>topic_10</td>\n",
       "      <td>0.008625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>topic_11</td>\n",
       "      <td>0.006849</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>topic_16</td>\n",
       "      <td>0.005209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>topic_17</td>\n",
       "      <td>0.002277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>topic_18</td>\n",
       "      <td>0.002182</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Features  Importance Score\n",
       "20   tweet_counts          0.192403\n",
       "13       topic_14          0.127279\n",
       "21  tweet_replies          0.121006\n",
       "6         topic_7          0.081235\n",
       "12       topic_13          0.066782\n",
       "0         topic_1          0.062765\n",
       "2         topic_3          0.056334\n",
       "5         topic_6          0.055789\n",
       "4         topic_5          0.047987\n",
       "11       topic_12          0.038198\n",
       "19       topic_20          0.027854\n",
       "3         topic_4          0.027688\n",
       "7         topic_8          0.022315\n",
       "1         topic_2          0.015451\n",
       "8         topic_9          0.012243\n",
       "18       topic_19          0.010560\n",
       "14       topic_15          0.008969\n",
       "9        topic_10          0.008625\n",
       "10       topic_11          0.006849\n",
       "15       topic_16          0.005209\n",
       "16       topic_17          0.002277\n",
       "17       topic_18          0.002182"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set features variable with the names of the features in X\n",
    "features = x.columns\n",
    "\n",
    "feature_importances = rf.feature_importances_\n",
    "\n",
    "features_df = pd.DataFrame({'Features': features, 'Importance Score': feature_importances})\n",
    "\n",
    "features_df.sort_values('Importance Score', inplace = True, ascending = False)\n",
    "\n",
    "features_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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