MIDS Capstone Project Spring 2017

Make News Credible Again

Problem Statement:

We aim to build a model that is capable of discerning whether an article is credible or not based on features derived solely from its text (i.e. word choice, writing style, title, etc.).


The widespread propagation of false information online (“fake news”) is not a recent phenomenon but its perceived impact in the 2016 U.S. presidential election has thrust the issue into the spotlight. In this project, we explore a number of machine learning-based approaches for solving the problem. Our first step was to identify the various forms of “fake news”.

Four Common Forms of “Fake News”:

  1. Clickbait — Shocking headlines meant to generate clicks to increase ad revenue. Oftentimes these stories are highly exaggerated or totally false.
  2. Propaganda — Intentionally misleading or deceptive articles meant to promote the author’s agenda. Oftentimes the rhetoric is hateful and incendiary.
  3. Commentary/Opinion — Biased reactions to current events. These articles oftentimes tell the reader how to perceive recent events.
  4. Humor/Satire — Articles written for entertainment. These stories are not meant to be taken seriously.

In this project, we focused on developing a classifier that was able to detect clickbait articles and propaganda articles.  


To acquire a sufficiently large labeled corpus of articles to train on, we scraped the websites of both credible and non-credible sources listed in the OpenSources (http://www.opensources.co/) database for new articles daily. Articles were given the same label as their source.


  1. Scrape source websites for new article context (text and title) daily and store on cloud server.
  2. Preprocess articles for content-based classification using various widely used techniques in NLP.
  3. Train different machine learning models to classify the news articles
  4. Create a web application (using Falsk API) to serve as the front-end for our classifier that returns a classification, a confidence metric and few important features in the model.
  5. A more detailed description of our approach can be found here 

( www.classify.news )*

*Please note that during user testing, certain computer models and/or web browsers had difficulty loading the banner video. This video demonstrates how the page should be loading: https://streamable.com/xn5zl . If you face issues, please try another laptop or web browser (preferably with ad-blockers temporarily disabled)

Model Classification Results
Model Classification Results
Model Accuracy
Model Accuracy

Last updated:

April 27, 2017