Political Spectrum Detection in Media News using with Deep-Learning and Shap Python.

The repository contains the data files and scripts corresponding to the paper "Neural Media Bias Detection Using Distant Supervision

Data

• The data is from Media Bias Group.
• "final_labels_MBIC.xlsx": MBIC's aggregated labels over all annotators based on majority vote (1700 sentences).

Content

Columns:

• "text": sentences extracted from news articles and labeled in terms of bias and opinion.
• "news_link": url to the news article from which the sentence is extracted.
• "outlet": news platform publishing the news article.
• "topic": news topic.
• "type": political orientation of news platform according to mediacloud.org.
• "label_bias": bias label for the sentence ("Biased" or "Non-biased").
• "label_opinion": opinion label for the sentence ("Expresses writer's opinion" or "Somewhat factual but also opinionated" or "Entirely factual".
• "biased_words": words marked as biased by the annotators.

Requirements

    numpy>=1.9.2

    scipy>=0.15.1

    scikit-learn>=0.18

    matplotlib>=1.4.3

    pandas

    tensorflow

    nfx

    keras
    
    shap

Installation

Clone this repository and unzip it.
- After downloading, cd into the notebook directory.
- Begin a new virtual environment with Python 3 and activate it.
- Install the required packages using pip install -r requirements.txt
- Execute it in Jupyter Notebook