Skip to content

Latest commit

 

History

History
52 lines (33 loc) · 1.51 KB

README.md

File metadata and controls

52 lines (33 loc) · 1.51 KB

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