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Candidate solution for Facebook's fake news problem using machine learning and crowd-sourcing. Takes form of a Chrome extension. Developed in under 24 hours at 2017 Crimson Code hackathon at Washington State University.

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# FakeFews
**Facebook Fake News Classifier!** (Currently experiencing 95% testing accuracy using k-fold cross validation)

### Summary

Candidate solution for Facebook's [fake news problem](http://www.newyorker.com/news/news-desk/solving-the-problem-of-fake-news) using machine learning and crowd-sourcing. Takes form of a Chrome extension. Developed in under 24 hours at 2017 [Crimson Code](http://hackathon.eecs.wsu.edu/) hackathon at Washington State University.

Some notes:
* Python secure server (with classifier and training data) lies in *./server*
* Chrome extension lies in *./client*
* Server, along with training data, is currently stored locally. Plans are to host these on external server soon.
* Chrome extension is not yet available on Chrome App Store - this is in the works!

### Example

Here's an example classification of fake news, as would appear in your Facebook feed:
<img src="/res/real_classification.png" width="1000"> 
<hr />

### Contributions

This was a great and challenging project, and we plan on taking this further. Of course, this couldn't have been completed without the rest of team _SlickBits_: 
* **Shyam** ([@gopshyam](https://github.com/gopshyam))
* **Anand** ([@Andyrocks143](https://github.com/Andyrocks143))
* **Giridhar** ([@mgiridhar](https://github.com/mgiridhar)).

These guys have talent and persistence.
<hr />
This work is under a Non-commercial Creative Commons license under the group SlickBits. If you use any part of this code, please add attribution to our team.

[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](http://creativecommons.org/licenses/by-nc/4.0/)

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