文件列表:

Makefile (80, 2017-12-29)
data (0, 2017-12-29)
data\fact_check.csv (11138, 2017-12-29)
data\sites_2016.csv (1521, 2017-12-29)
data\sites_2017.csv (3919, 2017-12-29)
data\top_2016.csv (10787, 2017-12-29)
data\top_2017.csv (10426, 2017-12-29)
notebooks (0, 2017-12-29)
notebooks\fake-news-analysis.ipynb (76889, 2017-12-29)
output (0, 2017-12-29)
output\top_domains_comparison.csv (1037, 2017-12-29)
requirements.txt (61, 2017-12-29)
scripts (0, 2017-12-29)
scripts\clean-raw-data.py (2763, 2017-12-29)

# 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](https://www.buzzfeed.com/craigsilverman/these-are-50-of-the-biggest-fake-news-hits-on-facebook-in)", 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](https://buzzsumo.com/), our own data collection, and [this PolitiFact list](http://www.politifact.com/punditfact/article/2017/apr/20/politifacts-guide-fake-news-websites-and-what-they/). 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](notebooks/fake-news-analysis.ipynb). The Python code in that notebook also produces one output file. This file, [`output/top_domains_comparison.csv`](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](https://www.python.org/) 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 notebooks 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](scott.pham@buzzfeed.com). Looking for more from BuzzFeed News? [Click here for a list of our open-sourced projects, data, and code](https://github.com/BuzzFeedNews/everything).

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