Data and analysis supporting the BuzzFeed News article, "These Are 50 Of The Biggest Fake News Hits On Facebook In 2017," published on Dec. 28, 2017
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README.md

Analysis of fake news sites and viral posts, 2016 vs. 2017

This repository contains data and analysis supporting the BuzzFeed News article, "These Are 50 Of The Biggest Fake News Hits On Facebook In 2017", published Thursday, December 28, 2017. Please read that article, which contains important context and methodological details, before proceeding.

Data

The data in this repository was compiled by BuzzFeed News using BuzzSumo, our own data collection, and this PolitiFact list. For additional details, please see the main BuzzFeed News article referenced above.

  • fact_check.csv: Titles and URLs of the top 50 fake news articles of 2017 along with associated fact-checking articles and their engagement numbers
  • sites_2016.csv: All URLs in our 2016 collection of sites that regularly publish completely fabricated articles
  • sites_2017.csv: All URLs in our 2017 collection of sites that regularly publish completely fabricated articles
  • top_2016.csv: The top 50 fake news articles of 2016 (by Facebook engagement) published by our 2016 list of fake news sites
  • top_2017.csv: The top 50 fake news articles of 2017 (by Facebook engagement) published by our 2017 list of fake news sites

Analysis

The analysis is contained within this notebook. The Python code in that notebook also produces one output file. This file, output/top_domains_comparison.csv, compares the count of unique domains from the list of top 50 fake news articles in 2016 with the equivalent list from 2017.

Reproducibility

To reproduce the calculations and produce the output file, you will need to do the following:

  • Ensure that you have installed Python and the Python libraries listed in requirements.txt.
  • Run jupyter notebook from either the root or the notebooks directory.
  • Open notebooks/fake-news-analysis.ipynb in Jupyter and run all the notebook’s cells.

Note: The Makefile and cleaning scripts are contained for reference but are not necessary to reproduce this analysis.

Feedback/Questions?

Contact Scott Pham at scott.pham@buzzfeed.com.

Looking for more from BuzzFeed News? Click here for a list of our open-sourced projects, data, and code.