Inspiration:

There’s no denial that data manipulation on social media play has a huge impact on the society. The ability to target a group of people, to generate information so easily, and to know what those people want to hear is a dangerous tool that should be kept in check. Fake news are so wide spread today and people perception could be easily affected by it. Take this headline from The Hill as an example

_“Researchers say fake news had ‘substantial impact’ on 2016 election” _ - The Hill

This project aims to tackle this data using a data-driven approach, particularly we hope to: 1. Detect patterns of how fake news spread on Twitter. 2. Build classification models to detect fake tweets.

To do this, we employed a labeled data from expert available here. Note that due to privacy policy of Twitter. The dataset only provide

Results:

An in detail analysis and methods can be found in the manuscript here You can see the graphical results here.

Stacks used: Python, sklearn, keras, tweepy