Psychology_Data_Analysis.ipynb

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    "colab": {
      "name": "Psychology Data Analysis.ipynb",
      "provenance": [],
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/egorssed/Fake_News_Detection/blob/main/Psychology_Data_Analysis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIyFYZ7bSdgX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b165fcb4-f074-4e3c-9346-19feb834aa50"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount= True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9XiECxIjXmd"
      },
      "source": [
        "# Hypothesis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fIunyzv8kCsw"
      },
      "source": [
        "Given previous research on similar topics, we can pose several hypotheses.   \n",
        "\n",
        "1. Personality traits vs Fake News:   \n",
        "**Originality and Resiliency are positively related to the ability to detect Fake**. Originality and Emotional Stability appear to be positively and strongly associated with interest in and knowledge of politics (Chirumbolo et al. 2010). We suppose that interest and knowledge of politics improve fake news detection performance.  \n",
        "\n",
        "2. Political leaning vs Fake News:   \n",
        "**Fake news detection capabilities are not influenced by (moderate) Political leaning**. It has been found by Van Bavel and Pereira (Van_Bavel & Pereira 2018)\n",
        "\n",
        "3. Personality traits vs Political leaning:   \n",
        "**Conscientiousness, agreeableness, and originality are typically found to predict Political criteria**. It has been found by Chirumbolo et al. (Chirumbolo et al. 2010; Alan et al. 2016).\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "48TgTjffjZVx"
      },
      "source": [
        "# Load Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0rna3hlFNQN"
      },
      "source": [
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from os.path import join\n",
        "import scipy\n",
        "import statsmodels.regression.linear_model as LM\n",
        "from matplotlib.ticker import MaxNLocator\n",
        "\n",
        "plt.rcParams['figure.figsize'] = [7, 5]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q9gGSrkSE90h",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "outputId": "75cc3885-9973-43fb-f755-f1fdbd9fc0ec"
      },
      "source": [
        "#Folder_path='drive/MyDrive/epfl/Fakenews/StatAnalysis/'\n",
        "Folder_path='/content/drive/My Drive/Psychology/Fake News Detection/'\n",
        "\n",
        "Data_path='Data_anonymized.csv'\n",
        "df=pd.read_csv(join(Folder_path,Data_path),index_col=0)\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>2021-04-27-22-04</td>\n",
              "      <td>2021-04-27-22-19</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>s.efdf6281-1c94-493f-8e5c-6a2e55dcd6bf.txt</td>\n",
              "      <td>10.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>Switzerland</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>3.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>8.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>5.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>4.2</td>\n",
              "      <td>3.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>2021-04-21-15-54</td>\n",
              "      <td>2021-04-21-16-23</td>\n",
              "      <td>29.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 92 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                  participant  ...  TIME_total\n",
              "0  s.383a7ac8-51de-4be8-82f9-0a5adfab7f2f.txt  ...         NaN\n",
              "1  s.a88c0a70-878f-4148-a335-ef8e351deee1.txt  ...         7.0\n",
              "2  s.5eaa59ba-1dde-41c2-a4dd-9f7f8c0bd77a.txt  ...        13.0\n",
              "3  s.59f16b8a-4207-4825-9342-43d56a9dc114.txt  ...        15.0\n",
              "4  s.efdf6281-1c94-493f-8e5c-6a2e55dcd6bf.txt  ...        29.0\n",
              "\n",
              "[5 rows x 92 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XPVse1rrFksh"
      },
      "source": [
        "# Data cleaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c9kRQXcVFn7V"
      },
      "source": [
        "Apparently there are no multi-gender people there, hence this column is completely useless"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6eGpNYhnFjcD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dfc318a9-1648-4985-91b8-325de24277cc"
      },
      "source": [
        "print(np.isnan(df['gender:other']).all())\n",
        "df.drop('gender:other',axis=1,inplace=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "naTopb5DFsIo"
      },
      "source": [
        "These people didn't manage to reach feeds, so there is no use in them either"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1wQY7YAeFnNW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "400b1f72-1e6e-4bac-9022-a262fc65082e"
      },
      "source": [
        "print('people who did not reach feeds',df[np.isnan(df['feed1_1:1'])].index.to_numpy())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "people who did not reach feeds [ 0 71]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bVy7NOYMFnP4"
      },
      "source": [
        "df=df[np.logical_not(np.isnan(df['feed1_1:1']))]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QUcHszPlFwuR"
      },
      "source": [
        "Nan emails only. Seems sensible. I guess we can work with this dataset now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9k8pFZaFnSu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c469edab-57c1-41ac-ca01-edf515747dc1"
      },
      "source": [
        "print('Columns with Nans',df.columns[df.isna().any()].to_numpy())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Columns with Nans []\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cIPd_bLUlc-6"
      },
      "source": [
        "# Results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "21AzVMIcF0SH"
      },
      "source": [
        "## Data exploration"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uX_4hMBPBZu6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "11cadb4e-739f-4b75-8eb6-12019bc13006"
      },
      "source": [
        "ax = plt.figure(dpi = 150).gca()\n",
        "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "sns.histplot(df['age:1'])\n",
        "plt.title('Age of partecipants')\n",
        "plt.xlabel('')\n",
        "plt.show()\n",
        "\n",
        "\n",
        "age = df['age:1']\n",
        "print(np.mean(age))\n",
        "print(np.std(age))\n",
        "\n",
        "edu = df['education:1']\n",
        "print(len(edu[edu<4]) / len(edu))\n",
        "\n",
        "ax = plt.figure(dpi = 150).gca()\n",
        "sns.countplot(df['education:1'])\n",
        "plt.title('Education Level')\n",
        "ax.set_xticklabels(('Primary school','GCSEs or equivalent','A-Levels or equivalent','Undergraduate', 'University post-graduate program'), rotation=40, ha=\"right\")\n",
        "plt.xlabel('')\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1050x750 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "22.228571428571428\n",
            "5.598906598795947\n",
            "0.45714285714285713\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1050x750 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k5HienN7PhFo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8af54a2b-0752-40de-8fcd-f245fe144703"
      },
      "source": [
        "df['age:1'].describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    70.000000\n",
              "mean     22.228571\n",
              "std       5.639332\n",
              "min      18.000000\n",
              "25%      20.000000\n",
              "50%      21.000000\n",
              "75%      23.000000\n",
              "max      54.000000\n",
              "Name: age:1, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xQ8Fuj76O7Tf",
        "outputId": "f1ba7d47-0bfc-469b-d77c-1a5df774159b"
      },
      "source": [
        "print(\"Percentage of women in the dataset {:.0f}%\".format((df['gender:1']==2).sum()/len(df)*100))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Percentage of women in the dataset 67%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hlov1D6MFnXR"
      },
      "source": [
        "Fake_news_raw=df[['Abortion FN','Climate TN','Domestic violence FN','Homophobia FN','Weed TN']]\n",
        "Political_leaning=df[['Refugees', 'Healthcare', 'Basic quality of living', 'Same sex couples',\n",
        "       'Abortion', 'National defense', 'Death penalty', 'Light drugs',\n",
        "       'Climate change', 'Minorities', 'Self-identify']]\n",
        "\n",
        "Personality_traits=df[['Agreeability','Consciousness','Extroversion','Honesty','Originality','Resiliency']]\n",
        "Personality_traits.columns=['Agreeableness','Conscientiousness','Extraversion','Honesty','Originality','Resiliency']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "O9M0uu0lg_x5",
        "outputId": "547d0ecc-56b0-4d7f-9b25-49c8af69bba0"
      },
      "source": [
        "#Desctiption of normalised Hexaco dataset\n",
        "Personality_traits_norm=(Personality_traits-Personality_traits.min(axis=0))\n",
        "Personality_traits_norm/=Personality_traits_norm.max(axis=0)\n",
        "Personality_traits_norm.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Agreeableness</th>\n",
              "      <th>Conscientiousness</th>\n",
              "      <th>Extraversion</th>\n",
              "      <th>Honesty</th>\n",
              "      <th>Originality</th>\n",
              "      <th>Resiliency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.531746</td>\n",
              "      <td>0.510317</td>\n",
              "      <td>0.601587</td>\n",
              "      <td>0.601020</td>\n",
              "      <td>0.562857</td>\n",
              "      <td>0.447619</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.253544</td>\n",
              "      <td>0.213033</td>\n",
              "      <td>0.226602</td>\n",
              "      <td>0.220407</td>\n",
              "      <td>0.192496</td>\n",
              "      <td>0.245169</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.428571</td>\n",
              "      <td>0.466667</td>\n",
              "      <td>0.222222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.527778</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.638889</td>\n",
              "      <td>0.571429</td>\n",
              "      <td>0.566667</td>\n",
              "      <td>0.444444</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.722222</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>0.785714</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.611111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Agreeableness  Conscientiousness  ...  Originality  Resiliency\n",
              "count      70.000000          70.000000  ...    70.000000   70.000000\n",
              "mean        0.531746           0.510317  ...     0.562857    0.447619\n",
              "std         0.253544           0.213033  ...     0.192496    0.245169\n",
              "min         0.000000           0.000000  ...     0.000000    0.000000\n",
              "25%         0.333333           0.333333  ...     0.466667    0.222222\n",
              "50%         0.527778           0.500000  ...     0.566667    0.444444\n",
              "75%         0.722222           0.666667  ...     0.666667    0.611111\n",
              "max         1.000000           1.000000  ...     1.000000    1.000000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5aRTiWlSF9If"
      },
      "source": [
        "### Fake news"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6h0sbmchCUUj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 722
        },
        "outputId": "831cd9e8-1666-4559-e95f-eeb23e80ece1"
      },
      "source": [
        "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n",
        "fig = plt.figure(figsize= (20,10))\n",
        "for i,name in enumerate(Fake_news_raw.columns):\n",
        "  plt.subplot(2,3,i+1)\n",
        "  \n",
        "  ax = sns.countplot(Fake_news_raw.iloc[:,i],color = colors[i])\n",
        "  ax.set_xticklabels( labels = np.arange(0,11))\n",
        "  plt.title(name)\n",
        "  plt.xlabel('')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LDpSB04GVIL"
      },
      "source": [
        "During the questionnaire, participants were asked to estimate their trust to present pieces of news on a scale from 0 to 10 with step 1. In other terms, they were asked to classify the piece as either True or False, by answering with probability of the piece being True. Such a task can be naturally named as a task of binary classification, hence the cost of deviation from correct answer can be measured as informational entropy between distribution of correct answers and distribution of predicted probabilities. Such logic brings us to conclusion that Fake News score detection should be measured as Binary Crossentropy."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Og3CVtolGMXp"
      },
      "source": [
        "#0*np.log(0)=0, whereas numpy thinks that it is nan \n",
        "def entropy_loss(Answers,Probabilities):\n",
        "    Loss=np.log(Probabilities)\n",
        "    Loss[Loss==-np.inf]=0\n",
        "    return Answers*Probabilities"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKPDRt-M2xss"
      },
      "source": [
        "def get_BCE(Answers,Dataset):\n",
        "  #Numpy thinks that nan*0=nan so we turn nan to 0 explicitly\n",
        "  False_loss=entropy_loss(Answers,Dataset)\n",
        "  True_loss=entropy_loss(1-Answers,1-Dataset)\n",
        "\n",
        "  print('Any infs?',np.isinf(False_loss).any().any(),np.isinf(True_loss).any().any())\n",
        "\n",
        "  # That's negative BCE, so 1-guessed everything, 0-guessed nothing\n",
        "  BCE=(False_loss+True_loss)/Fake_news.shape[1]\n",
        "  plt.show()\n",
        "  return BCE"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E2RWUy8O1rfo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 781
        },
        "outputId": "a53f00b7-8541-466d-b33f-28685140cf0c"
      },
      "source": [
        "Fake_news=Fake_news_raw/10\n",
        "Fake_news_answers=np.array([False,True,False,False,True])\n",
        "\n",
        "\n",
        "\n",
        "BCE_original=get_BCE(Fake_news_answers,Fake_news)\n",
        "plt.figure(dpi = 150)\n",
        "BCE_original.sum(axis=1).plot.hist(title='Total Fake News Score',xlim=(0.3,0.9))\n",
        "plt.show()\n",
        "BCE_scores=BCE_original*5\n",
        "BCE_scores['Total']=BCE_scores.mean(axis=1)\n",
        "print(BCE_original.sum(axis=1).std())"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:3: RuntimeWarning: divide by zero encountered in log\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Any infs? False False\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1050x750 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "0.09124898252361066\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "nXDo1dWSWoXo",
        "outputId": "5f055947-8eed-4aeb-ac67-745aa6383f92"
      },
      "source": [
        "BCE_scores.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Abortion FN</th>\n",
              "      <th>Climate TN</th>\n",
              "      <th>Domestic violence FN</th>\n",
              "      <th>Homophobia FN</th>\n",
              "      <th>Weed TN</th>\n",
              "      <th>Total</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>70.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.847143</td>\n",
              "      <td>0.485714</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.742857</td>\n",
              "      <td>0.414286</td>\n",
              "      <td>0.578000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.230765</td>\n",
              "      <td>0.236696</td>\n",
              "      <td>0.243168</td>\n",
              "      <td>0.282110</td>\n",
              "      <td>0.308472</td>\n",
              "      <td>0.091249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.725000</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.125000</td>\n",
              "      <td>0.505000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.400000</td>\n",
              "      <td>0.580000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.675000</td>\n",
              "      <td>0.635000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.840000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Abortion FN  Climate TN  ...    Weed TN      Total\n",
              "count    70.000000   70.000000  ...  70.000000  70.000000\n",
              "mean      0.847143    0.485714  ...   0.414286   0.578000\n",
              "std       0.230765    0.236696  ...   0.308472   0.091249\n",
              "min       0.100000    0.000000  ...   0.000000   0.400000\n",
              "25%       0.725000    0.300000  ...   0.125000   0.505000\n",
              "50%       1.000000    0.500000  ...   0.400000   0.580000\n",
              "75%       1.000000    0.600000  ...   0.675000   0.635000\n",
              "max       1.000000    1.000000  ...   1.000000   0.840000\n",
              "\n",
              "[8 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4G3CQM5W3_x9"
      },
      "source": [
        "def bootstrap_CI_func(data,func, nbr_draws=1000):\n",
        "    res = np.zeros(nbr_draws)\n",
        "    data = np.array(data)\n",
        "\n",
        "    for n in range(nbr_draws):\n",
        "        indices = np.random.randint(0, len(data), len(data))\n",
        "        data_tmp = data[indices] \n",
        "        res[n] = func(data_tmp)\n",
        "\n",
        "    return [np.nanpercentile(res, 2.5),np.nanpercentile(res, 97.5)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hIeSeLNJ4fVK"
      },
      "source": [
        "95 % significance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b6xa0Ox74A5e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fac645e-6108-46e0-905f-0cdf4ecb0e86"
      },
      "source": [
        "low_CI,high_CI=bootstrap_CI_func(BCE_original.sum(axis=1),np.mean)\n",
        "print(\"95% Confidence intervals for mean value of Fake News detection score [{:.2f},{:.2f}]\".format(low_CI,high_CI))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "95% Confidence intervals for mean value of Fake News detection score [0.56,0.60]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsLIhXRhGdQV"
      },
      "source": [
        "### Political Leaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dCj-LInnGcxH"
      },
      "source": [
        "Normalise it in the first place. And make it zero-centered"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wA8q_p2TGMe1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "68899470-6022-4286-8478-fd7233011dcf"
      },
      "source": [
        "Political_leaning=(Political_leaning-5.5)/4.5\n",
        "Political_leaning.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Refugees</th>\n",
              "      <th>Healthcare</th>\n",
              "      <th>Basic quality of living</th>\n",
              "      <th>Same sex couples</th>\n",
              "      <th>Abortion</th>\n",
              "      <th>National defense</th>\n",
              "      <th>Death penalty</th>\n",
              "      <th>Light drugs</th>\n",
              "      <th>Climate change</th>\n",
              "      <th>Minorities</th>\n",
              "      <th>Self-identify</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>-0.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.555556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Refugees  Healthcare  ...  Minorities  Self-identify\n",
              "1 -0.777778        -1.0  ...   -0.555556      -1.000000\n",
              "2 -0.555556        -1.0  ...    0.111111      -0.777778\n",
              "3 -0.777778        -1.0  ...    0.333333      -0.555556\n",
              "4 -1.000000        -1.0  ...   -1.000000      -0.777778\n",
              "5 -1.000000        -1.0  ...   -1.000000      -1.000000\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LEPTN4C6GMhM"
      },
      "source": [
        "ProAuthoritarian_columns=['Refugees', 'Healthcare', 'Basic quality of living', 'Same sex couples','National defense','Climate change', 'Minorities']\n",
        "ProLibertarian_columns=['Abortion','Death penalty', 'Light drugs']\n",
        "Political_leaning['Political_score']=(Political_leaning[ProAuthoritarian_columns].sum(axis=1)\n",
        "                                      -Political_leaning[ProLibertarian_columns].sum(axis=1))/(Political_leaning.shape[1]-1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OkJCsCLEGMjj",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 547
        },
        "outputId": "e4dda8a7-c276-46f0-c556-e2f1825502b6"
      },
      "source": [
        "plt.figure(dpi = 150)\n",
        "g=sns.jointplot(x='Self-identify', y='Political_score', data=Political_leaning,\n",
        "                  kind=\"reg\",height=7,xlim=(-1.1,0.6),ylim=(-1.1,0.4))\n",
        "plt.show()\n",
        "scipy.stats.spearmanr(Political_leaning[['Political_score','Self-identify']])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1050x750 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SpearmanrResult(correlation=0.4314237311600313, pvalue=0.0001924101387843294)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7qSx35JzZO9G"
      },
      "source": [
        "Normalized Polititcal leaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 530
        },
        "id": "RRgvXShjol2t",
        "outputId": "67cd774e-58b4-4dda-efae-f5e9c3ab3588"
      },
      "source": [
        "PL_norm=(Political_leaning+1)\n",
        "PL_norm/=2\n",
        "\n",
        "plt.figure(dpi = 150)\n",
        "g=sns.jointplot(x='Self-identify', y='Political_score', data=PL_norm,\n",
        "                  kind=\"reg\",height=7,xlim=(-0.05,0.8),ylim=(-0.05,0.7))\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1050x750 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BqGzoOOAGp19"
      },
      "source": [
        "Correlation seems to be significant (pvalue<0.05), yet function description reads \"The p-values are not entirely reliable but are probably reasonable for datasets larger than 500 or so.\" Therefore we can not really state anything for 70 instances dataset. I pearsonally see that we have some correlation only starting from -0.25 Self-identify. These are centrist or authoritarian people "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1I0m0ehfeAg"
      },
      "source": [
        "### Personality traits"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cQmpDj6QffuZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "outputId": "fb4f3667-0d0b-4037-b25b-4a2a352d1427"
      },
      "source": [
        "colors = ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf']\n",
        "fig = plt.figure(figsize= (20,10),dpi=150)\n",
        "for i,name in enumerate(Hexaco.columns):\n",
        "  plt.subplot(2,3,i+1)\n",
        "  \n",
        "  ax = sns.histplot(Hexaco.iloc[:,i],color = colors[i])\n",
        "  plt.xlim((1,5))\n",
        "  \n",
        "  plt.title(name)\n",
        "  plt.xlabel('')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 3000x1500 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhIy7cL5G2Rh"
      },
      "source": [
        "## Spearman rank correlations"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttxSi9MiGMyQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "outputId": "b535d476-2d54-4e34-fa1f-e73c2bc1f43e"
      },
      "source": [
        "df_all=BCE_scores.drop('Total',axis=1).copy()\n",
        "df_all['Fake_news_score']=df_all.mean(axis=1)\n",
        "df_all=df_all.merge(Political_leaning,left_index=True,right_index=True)\n",
        "df_all=df_all.merge(Personality_traits,left_index=True,right_index=True)\n",
        "df_all.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Abortion FN</th>\n",
              "      <th>Climate TN</th>\n",
              "      <th>Domestic violence FN</th>\n",
              "      <th>Homophobia FN</th>\n",
              "      <th>Weed TN</th>\n",
              "      <th>Fake_news_score</th>\n",
              "      <th>Refugees</th>\n",
              "      <th>Healthcare</th>\n",
              "      <th>Basic quality of living</th>\n",
              "      <th>Same sex couples</th>\n",
              "      <th>Abortion</th>\n",
              "      <th>National defense</th>\n",
              "      <th>Death penalty</th>\n",
              "      <th>Light drugs</th>\n",
              "      <th>Climate change</th>\n",
              "      <th>Minorities</th>\n",
              "      <th>Self-identify</th>\n",
              "      <th>Political_score</th>\n",
              "      <th>Agreeableness</th>\n",
              "      <th>Conscientiousness</th>\n",
              "      <th>Extraversion</th>\n",
              "      <th>Honesty</th>\n",
              "      <th>Originality</th>\n",
              "      <th>Resiliency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.6</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.711111</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.8</td>\n",
              "      <td>5.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.50</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>1.8</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.4</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.64</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.111111</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>0.555556</td>\n",
              "      <td>-0.333333</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>0.111111</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>0.333333</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-0.177778</td>\n",
              "      <td>2.8</td>\n",
              "      <td>2.2</td>\n",
              "      <td>4.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.0</td>\n",
              "      <td>0.7</td>\n",
              "      <td>0.1</td>\n",
              "      <td>0.9</td>\n",
              "      <td>0.3</td>\n",
              "      <td>0.60</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.555556</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>4.2</td>\n",
              "      <td>1.2</td>\n",
              "      <td>3.8</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.2</td>\n",
              "      <td>3.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.54</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.0</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.777778</td>\n",
              "      <td>0.777778</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>-0.955556</td>\n",
              "      <td>4.2</td>\n",
              "      <td>4.2</td>\n",
              "      <td>3.6</td>\n",
              "      <td>4.2</td>\n",
              "      <td>5.0</td>\n",
              "      <td>2.6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Abortion FN  Climate TN  ...  Originality  Resiliency\n",
              "1          0.6         0.3  ...          4.0         1.6\n",
              "2          1.0         0.2  ...          3.6         1.6\n",
              "3          1.0         0.4  ...          3.2         1.8\n",
              "4          1.0         0.7  ...          4.2         3.6\n",
              "5          0.7         0.8  ...          5.0         2.6\n",
              "\n",
              "[5 rows x 24 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZxCnK3yWGzge"
      },
      "source": [
        "def spearmanr_pval(x,y):\n",
        "    return scipy.stats.spearmanr(x,y)[1]\n",
        "\n",
        "# calculate the correlations\n",
        "corr = df_all.corr(method='spearman')\n",
        "# calculate p-values\n",
        "corr_p = df_all.corr(method=spearmanr_pval)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_7yRR88gt0g"
      },
      "source": [
        "# select correlations and p-values of interest, namely Fake News columns\n",
        "# Also get rid of Fake News indices (iloc[6:]), since they obviously have corr=1\n",
        "corr_selected = corr[df_all.columns[:6]].loc[df_all.columns.values].iloc[6:]\n",
        "corr_p_selected = corr_p[df_all.columns[:6]].loc[df_all.columns.values].iloc[6:]\n",
        "#print(corr[corr_p < 0.051]['Resiliency'])\n",
        "#print(corr_p['Resiliency'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-hSnKgUaGzjM"
      },
      "source": [
        "#Split correlations on Fake News, Political Leaning and Personality traits\n",
        "corr_FN_PolitLeaning = corr[df_all.columns[:6]].loc[df_all.columns.values].iloc[6:-6]\n",
        "corr_p_FN_PolitLeaning = corr_p[df_all.columns[:6]].loc[df_all.columns.values].iloc[6:-6]\n",
        "\n",
        "corr_FN_Personality = corr[df_all.columns[:6]].loc[df_all.columns.values].iloc[-6:]\n",
        "corr_p_FN_Personality = corr_p[df_all.columns[:6]].loc[df_all.columns.values].iloc[-6:]\n",
        "\n",
        "corr_PolitLeaning_Personality = corr[df_all.columns[-6:]].iloc[6:-6]\n",
        "corr_p_PolitLeaning_Personality = corr_p[df_all.columns[-6:]].iloc[6:-6]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OmcQZw4BbAwz"
      },
      "source": [
        "### Political leaning vs Fake News"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kPHy_JVubGAg"
      },
      "source": [
        "mask_Siginficant_rows_cols=np.where((corr_p_FN_PolitLeaning<0.05).any(axis=1))[0],np.where((corr_p_FN_PolitLeaning<0.05).any(axis=0))[0]\n",
        "\n",
        "corr_p_FN_PolitLeaning=corr_p_FN_PolitLeaning.iloc[mask_Siginficant_rows_cols]\n",
        "corr_FN_PolitLeaning=corr_FN_PolitLeaning.iloc[mask_Siginficant_rows_cols]\n",
        "corr_FN_PolitLeaning[corr_p_FN_PolitLeaning>0.05]=np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWebX4JhbxFU",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 634
        },
        "outputId": "1a271ea8-5fec-4dee-ef1e-2182bb5f6539"
      },
      "source": [
        "FN_columns=np.array(corr_FN_PolitLeaning.columns)\n",
        "FN_columns[:-1]=[x[:-2] for x in corr_FN_PolitLeaning.columns[:-1]]\n",
        "\n",
        "plt.figure(figsize=(11,9))\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(250, 15, s=75, l=40,n=9,as_cmap=True,center='dark')\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "b = sns.heatmap(corr_FN_PolitLeaning, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
        "b.set_title('Significant Spearman correlation')\n",
        "b.set_xlabel('Fake News')\n",
        "b.set_ylabel('Political leaning questions')\n",
        "b.set_yticklabels(b.get_yticklabels(), size = 9)\n",
        "b.set_xticklabels(FN_columns, size = 9)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wq-dBFGhcFsO"
      },
      "source": [
        "We do not see a significant correlation between overall Political leaning and overall Fake News score. However, we find a lot of correlations between specific topics of Political leaning and specific Fake News.  Moreover, we find significant correlation for two Political leaning features with overall Fake News score and for two specific Fake News scores with overall Political leaning.\n",
        "Yet specific features concerned in these correlations appear to have mostly disbalanced distribution, since most of participants voted for one border value. \n",
        "To test whether significance is caused by disbalance we excluded people answered border values and estimated correlations again."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZI9bh7zkdozb",
        "outputId": "ebd4f467-eba3-4048-84d5-e2bd3faa2be9"
      },
      "source": [
        "df_unbiased=df_all[df_all['Abortion FN']!=df_all['Abortion FN'].min()]\n",
        "scipy.stats.spearmanr(df_unbiased['Abortion FN'],df_unbiased['Political_score'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SpearmanrResult(correlation=-0.29791375138891946, pvalue=0.012912189262002645)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K8eRe-uMejpg",
        "outputId": "3603db9d-a7e8-49aa-a7a0-5627ccc21f34"
      },
      "source": [
        "df_unbiased=df_all[df_all['Homophobia FN']!=df_all['Homophobia FN'].min()]\n",
        "scipy.stats.spearmanr(df_unbiased['Homophobia FN'],df_unbiased['Political_score'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SpearmanrResult(correlation=-0.35063221325405525, pvalue=0.0033737070877100117)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hlR4FIH_hUxZ",
        "outputId": "57b2b5a2-8138-40f6-9be6-eb22345e2b0d"
      },
      "source": [
        "#It was marginally insignificant, let's take a look at the change anyway\n",
        "print(\"Disbalanced p value\",corr_p_FN_PolitLeaning.loc['Healthcare','Fake_news_score'])\n",
        "df_unbiased=df_all[df_all['Healthcare']!=df_all['Healthcare'].min()]\n",
        "print(\"Balanced\",scipy.stats.spearmanr(df_unbiased['Healthcare'],df_unbiased['Fake_news_score']))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Disbalanced p value 0.050835851261154585\n",
            "Balanced SpearmanrResult(correlation=-0.27628890687125324, pvalue=0.1324378246617971)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KhTMIbiHgGFu",
        "outputId": "033a822c-d482-431f-c59c-3e51ce6855bd"
      },
      "source": [
        "df_unbiased=df_all[df_all['Same sex couples']!=df_all['Same sex couples'].min()]\n",
        "scipy.stats.spearmanr(df_unbiased['Same sex couples'],df_unbiased['Fake_news_score'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "SpearmanrResult(correlation=-0.3361143175561732, pvalue=0.12616900770295142)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vmOrgwL5d4pI"
      },
      "source": [
        "For balanced dataset both overall Fake news score-related correlations became insignificant, therefore we can conclude that they are due to disbalance, rather than natural impact of specific political leaning aspects."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cLtIi6RzeCRf"
      },
      "source": [
        "### Personality traits vs Fake News"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C5d0K484mbt6"
      },
      "source": [
        "corr_FN_Personality[corr_p_FN_Personality>0.05]=np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "id": "-BMMY_o-mZ0w",
        "outputId": "6280d5ae-cb02-451b-b454-1f11d0bd88c7"
      },
      "source": [
        "FN_columns=np.array(corr_FN_Personality.columns)\n",
        "FN_columns[:-1]=[x[:-2] for x in corr_FN_Personality.columns[:-1]]\n",
        "\n",
        "plt.figure(figsize=(11,9))\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(250, 15, s=75, l=40,n=9,as_cmap=True,center='dark')\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "b = sns.heatmap(corr_FN_Personality, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
        "b.set_title('Significant Spearman correlation')\n",
        "b.set_xlabel('Fake News')\n",
        "b.set_ylabel('Personality traits')\n",
        "b.set_yticklabels(b.get_yticklabels(), size = 9)\n",
        "b.set_xticklabels(FN_columns, size = 9)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qfqtq-WYeXmp"
      },
      "source": [
        "There is only one Personality trait significantly (p=.03) correlating with only one specific Fake News piece detection"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RlUbkoqleYwH"
      },
      "source": [
        "### Personality traits vs Political leaning"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VjYeJ_8wRBtE"
      },
      "source": [
        "mask_Siginficant_rows_cols=np.where((corr_p_PolitLeaning_Personality<0.05).any(axis=1))[0],np.where((corr_p_PolitLeaning_Personality<0.05).any(axis=0))[0]\n",
        "\n",
        "corr_p_PolitLeaning_Personality=corr_p_PolitLeaning_Personality.iloc[mask_Siginficant_rows_cols]\n",
        "corr_PolitLeaning_Personality=corr_PolitLeaning_Personality.iloc[mask_Siginficant_rows_cols]\n",
        "corr_PolitLeaning_Personality[corr_p_PolitLeaning_Personality>0.05]=np.nan"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "id": "wgFn53UARKPo",
        "outputId": "aa73bf54-4e58-4467-a85c-fe5d0a06cfab"
      },
      "source": [
        "FN_columns=np.array(corr_PolitLeaning_Personality.columns)\n",
        "FN_columns[:-1]=[x[:-2] for x in corr_PolitLeaning_Personality.columns[:-1]]\n",
        "\n",
        "plt.figure(figsize=(11,9))\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(250, 15, s=75, l=40,n=9,as_cmap=True,center='dark')\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "b = sns.heatmap(corr_PolitLeaning_Personality, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
        "b.set_title('Significant Spearman correlation')\n",
        "b.set_xlabel('Personality traits')\n",
        "b.set_ylabel('Political leaning questions')\n",
        "b.set_yticklabels(b.get_yticklabels(), size = 9)\n",
        "#b.set_xticklabels(FN_columns, size = 9)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6Ldb34_WfSaq"
      },
      "source": [
        "In this section, we got several significant and logically sensible correlations\n",
        "For instance, extroverts empathize more to mothers who don’t want to have a child, hence extroverts approve abortion."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x_dfyGO8fwIK"
      },
      "source": [
        "## Linear regressions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IBUrMynwf1UG"
      },
      "source": [
        "from statsmodels.regression.linear_model import OLS\n",
        "\n",
        "def get_regression_summary(X,y):\n",
        "    #envoke a model that predicts label by all the demographic features\n",
        "    model = LM.OLS(y, X)\n",
        "    res = model.fit()\n",
        "    #Transform results with LR coefficients to DataFrame\n",
        "    results_as_html = res.summary().tables[1].as_html()\n",
        "    df_summary=pd.read_html(results_as_html, header=0, index_col=0)[0]\n",
        "    \n",
        "    #Consider only significant coefficients\n",
        "    print(\"F-statistic p_value {:.3f}\".format(res.f_pvalue))\n",
        "    return df_summary[df_summary['P>|t|']<0.05][['coef', 'std err', 'P>|t|','[0.025','0.975]']],res"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M_Hu7PUwgw8A"
      },
      "source": [
        "In this method we normalize all features to have M=0,SD=1, so that they all have the same initial impact. Next we try to predict target parameter using linear regression over considered features. In this case linear coefficients mark an impact of a given feature on target excluding confounding impact of the other features."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSf_fLivgv_U"
      },
      "source": [
        "Features=df_all.copy()\n",
        "Features-=Features.mean(axis=0)\n",
        "Features/=Features.std(axis=0)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhpunqXxgSMi"
      },
      "source": [
        "### Political score vs Fake News score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p_NDe1legS5k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "c97d7400-6e4d-4e6c-e410-3318bb10a69b"
      },
      "source": [
        "X=Features['Political_score']\n",
        "y=Features['Fake_news_score']\n",
        "df_coefs,model=get_regression_summary(X,y)\n",
        "sns.jointplot(x=X,y=y,kind=\"reg\")\n",
        "df_coefs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F-statistic p_value 0.035\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>coef</th>\n",
              "      <th>std err</th>\n",
              "      <th>P&gt;|t|</th>\n",
              "      <th>[0.025</th>\n",
              "      <th>0.975]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Political_score</th>\n",
              "      <td>-0.2503</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.035</td>\n",
              "      <td>-0.483</td>\n",
              "      <td>-0.018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   coef  std err  P>|t|  [0.025  0.975]\n",
              "Political_score -0.2503    0.117  0.035  -0.483  -0.018"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NtEdlFRwiKDD"
      },
      "source": [
        "### Personality traits vs Fake News score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CvIFDz5biIYC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "outputId": "19b58726-dac2-4845-9586-2259f1a786db"
      },
      "source": [
        "X=Features.loc[:,Personality_traits.columns]\n",
        "y=Features['Fake_news_score']\n",
        "df_coefs,model=get_regression_summary(X,y)\n",
        "df_coefs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F-statistic p_value 0.388\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>coef</th>\n",
              "      <th>std err</th>\n",
              "      <th>P&gt;|t|</th>\n",
              "      <th>[0.025</th>\n",
              "      <th>0.975]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [coef, std err, P>|t|, [0.025, 0.975]]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "khl1ZbWCi6iK"
      },
      "source": [
        "No Personality features features have a significant impact on the Fake News score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rjhzA-X3iYTi"
      },
      "source": [
        "### Personality traits vs Political leaning score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "au8oQQBfi-IJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "a65c7abb-d8cc-42bb-882e-a4e92b237b39"
      },
      "source": [
        "X=Features.loc[:,Personality_traits.columns]\n",
        "y=Features['Political_score']\n",
        "df_coefs,model=get_regression_summary(X,y)\n",
        "sns.jointplot(x=X[df_coefs.index[0]],y=y,kind=\"reg\")\n",
        "df_coefs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F-statistic p_value 0.105\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>coef</th>\n",
              "      <th>std err</th>\n",
              "      <th>P&gt;|t|</th>\n",
              "      <th>[0.025</th>\n",
              "      <th>0.975]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Extraversion</th>\n",
              "      <td>-0.3075</td>\n",
              "      <td>0.119</td>\n",
              "      <td>0.012</td>\n",
              "      <td>-0.545</td>\n",
              "      <td>-0.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                coef  std err  P>|t|  [0.025  0.975]\n",
              "Extraversion -0.3075    0.119  0.012  -0.545   -0.07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RWLuYw_wll4c"
      },
      "source": [
        "# Discussion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uivNR_-lsYi"
      },
      "source": [
        "## Personality traits vs Fake News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-VDt_qP6mMJW"
      },
      "source": [
        "In this section the hypothesis was **“Originality and Resiliency are positively related to the ability to detect fake news”**.  \n",
        "\n",
        "However, in practice, no strong correlation for either Originality (P=.31 for Spearman correlation and P=.5 for Linear regression) or Resiliency (P=.66 for Spearman correlation and P=.9 for Linear regression) were found. None of Big 6 features showed significance in either correlation or linear impact. Therefore, we can conclude that on the given data we find our first hypothesis disproved.Moreover, we can suggest that Personality traits may not affect Fake News detection at all.   \n",
        "\n",
        "Our hypothesis was drawn from a diverse political spectrum research whereas our sample of participants has political views from liberal to centrists (M=0.2, SD=0.14 with 0 for Liberal and 1 for Conservative), moreover, average age was 22.23 years (SD=5.64). The fact that our sample was disbalanced in these features implies that our results may be applied only on these people subclasses, yet they can not be certainly extrapolated on any given sample of people."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5042vUEsm2q2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 644
        },
        "outputId": "e94e4858-a962-434d-be49-d3b35b4e6971"
      },
      "source": [
        "FN_columns=np.array(corr_FN_Personality.columns)\n",
        "FN_columns[:-1]=[x[:-2] for x in corr_FN_Personality.columns[:-1]]\n",
        "\n",
        "plt.figure(figsize=(11,9))\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(250, 15, s=75, l=40,n=9,as_cmap=True,center='dark')\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "b = sns.heatmap(corr_FN_Personality, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
        "b.set_title('Significant Spearman correlation')\n",
        "b.set_xlabel('Fake News')\n",
        "b.set_ylabel('Personality traits')\n",
        "b.set_yticklabels(b.get_yticklabels(), size = 9)\n",
        "b.set_xticklabels(FN_columns, size = 9)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rDd6GNGKnIEw",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "outputId": "7a7fc914-49fb-4d36-ef47-c46793a962d4"
      },
      "source": [
        "X=Features.loc[:,Personality_traits.columns]\n",
        "y=Features['Fake_news_score']\n",
        "df_coefs,model=get_regression_summary(X,y)\n",
        "df_coefs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F-statistic p_value 0.388\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>coef</th>\n",
              "      <th>std err</th>\n",
              "      <th>P&gt;|t|</th>\n",
              "      <th>[0.025</th>\n",
              "      <th>0.975]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [coef, std err, P>|t|, [0.025, 0.975]]\n",
              "Index: []"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 155
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HlBbWHLwltry"
      },
      "source": [
        "## Political leaning vs Fake News"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qaBXV50AnNgU"
      },
      "source": [
        "In this section out hypothesis was **“Fake news detection capabilities are not influenced by (moderate) political leaning”**.\n",
        " However, using method based on linear regression we significantly (P=.04) find that Liberals are better in resolving Fake News then Centrists  (M=-0.25, SD=0.12, in the table and figure)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TZsmH_AUlxRT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "0ccbd650-a683-41ca-baf6-b369648a062b"
      },
      "source": [
        "X=Features['Political_score']\n",
        "y=Features['Fake_news_score']\n",
        "df_coefs,model=get_regression_summary(X,y)\n",
        "sns.jointplot(x=X,y=y,kind=\"reg\")\n",
        "df_coefs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F-statistic p_value 0.035\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "        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>coef</th>\n",
              "      <th>std err</th>\n",
              "      <th>P&gt;|t|</th>\n",
              "      <th>[0.025</th>\n",
              "      <th>0.975]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Political_score</th>\n",
              "      <td>-0.2503</td>\n",
              "      <td>0.117</td>\n",
              "      <td>0.035</td>\n",
              "      <td>-0.483</td>\n",
              "      <td>-0.018</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   coef  std err  P>|t|  [0.025  0.975]\n",
              "Political_score -0.2503    0.117  0.035  -0.483  -0.018"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 156
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-T2cPcBAnfox"
      },
      "source": [
        "Sindermann et al. 2020 showed that Right-Wing Authoritarianism is associated with selective interest in information fitting with one’s attitudes.  \n",
        "\n",
        "From our results, we may conclude that this effect may propagate on centrists resulting in worse performance in Fake News detection. On the other hand, in same article, there was small evidence on the fact that liberals tend to consume a higher number of news sources. It may improve their ability to resolve Fake News due to considering information coming from different sources. Both of these explanations may work as they coincide with the findings of our study."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xevM9qTblxg9"
      },
      "source": [
        "## Personality traits vs Political leaning"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fyZINSU9ns8B"
      },
      "source": [
        "In this section the hypothesis drawn from Chirumbolo et al 2010  was **“Conscientiousness, agreeableness, and originality are typically found to predict political criteria”**. Unlikely, analysis of Spearman correlations confirmed only agreeableness to predict political criteria (R=-0.28, P=.02)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HDcqMinWlnNo",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 471
        },
        "outputId": "70612dc1-a7c6-41e1-ac11-ca29fec12b46"
      },
      "source": [
        "FN_columns=np.array(corr_PolitLeaning_Personality.columns)\n",
        "FN_columns[:-1]=[x[:-2] for x in corr_PolitLeaning_Personality.columns[:-1]]\n",
        "\n",
        "plt.figure(figsize=(11,9))\n",
        "# Generate a custom diverging colormap\n",
        "cmap = sns.diverging_palette(250, 15, s=75, l=40,n=9,as_cmap=True,center='dark')\n",
        "# Draw the heatmap with the mask and correct aspect ratio\n",
        "b = sns.heatmap(corr_PolitLeaning_Personality, cmap=cmap, center=0,\n",
        "            square=True, linewidths=.5, cbar_kws={\"shrink\": .5})\n",
        "b.set_title('Significant Spearman correlation')\n",
        "b.set_xlabel('Personality traits')\n",
        "b.set_ylabel('Political leaning questions')\n",
        "b.set_yticklabels(b.get_yticklabels(), size = 9)\n",
        "#b.set_xticklabels(FN_columns, size = 9)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 792x648 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hsR1-H4eoFy5"
      },
      "source": [
        "The other method rejects this hypothesis altogether. The analysis of linear regression has shown only Extraversion to be a predictive factor for Political leaning (M=-0.3, SD=0.12, P=.02 for linear coefficient)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lccwKjCXoHl3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "outputId": "e655cc4f-ac75-472c-ad99-e150ddb68b16"
      },
      "source": [
        "X=Features.loc[:,Personality_traits.columns]\n",
        "y=Features['Political_score']\n",
        "df_coefs,model=get_regression_summary(X,y)\n",
        "sns.jointplot(x=X[df_coefs.index[0]],y=y,kind=\"reg\")\n",
        "df_coefs"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "F-statistic p_value 0.105\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "\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>coef</th>\n",
              "      <th>std err</th>\n",
              "      <th>P&gt;|t|</th>\n",
              "      <th>[0.025</th>\n",
              "      <th>0.975]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Extraversion</th>\n",
              "      <td>-0.3075</td>\n",
              "      <td>0.119</td>\n",
              "      <td>0.012</td>\n",
              "      <td>-0.545</td>\n",
              "      <td>-0.07</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                coef  std err  P>|t|  [0.025  0.975]\n",
              "Extraversion -0.3075    0.119  0.012  -0.545   -0.07"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 158
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TivmRKOGoPVE"
      },
      "source": [
        "In the article Gerber et al. 2011 it was shown that extraverts are likely to report that they are interested in politics, watching political talk shows and network news.   \n",
        "\n",
        "In a turn, Sindermann et al. 2020 showed a small evidence on the fact that liberals tend to consume a higher number of news sources.   \n",
        "\n",
        "From this, we conclude that for Extraversion and Political leaning number of news usually consumed is a correlating trait and may work as a confounding factor for relation between these features.   \n",
        "\n",
        "In following work, given more data the information may be propagated through correlating features in order to reduce the confounding variables for considered features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "umYWH1LZmGZC"
      },
      "source": [
        "# Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LP1ukSKUokEq"
      },
      "source": [
        "In this work, we have studied a sample of mostly undergraduate young people with Liberal to Centrist political views. We found that for this subgroup Personality traits do not impact Fake News detection performance, in contrast to Political Leaning, for which Liberals resolve fakes better than Centrists. The relations between Political leaning and Personality traits were found only for Agreeableness and Extraversion. The presence of relation for Agreeableness was already shown in previous articles, whereas for Extraversion we suggest this relation might be present due to a confounding factor, namely that Extraverts and Liberals consume news from more sources than the others. \n",
        "\n",
        "The findings of this research may help social networks to understand how susceptible to fake news their audience is. Given this data, social networks will gain possibility to distribute resources of news validation more efficiently. Moreover, during election campaigns, it is always important to understand the electorate’s susceptibility to unconfirmed statements to build a sensitive strategy of opposing accusations of the other political side and creating their announcements. \n",
        "\n",
        "We also find that the correlation is present between performance of detection specific topics and specific Political related questions or specific Personality traits. The findings seem sensible, however, they need to be tested on a higher number of Fake News questions and a more vast sample of participants. Furthermore, variety of age, education level, and political leaning should also be extended to validate possibility to extrapolate results found in this study on a more general sample of participants."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-_TsFMNhokaB"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}