Please use this identifier to cite or link to this item:

Creating Task-Generic Features for Fake News Detection

File Size Format  
0516.pdf 319.92 kB Adobe PDF View/Open

Item Summary

Title:Creating Task-Generic Features for Fake News Detection
Authors:Olivieri, Alex
Shabani, Shaban
Sokhn, Maria
Cudré-Mauroux, Philippe
Keywords:Truth and Lies: Deception and Cognition on the Internet
Internet and the Digital Economy
Fake News
Google Custom Search
show 2 moreMachine Learning
show less
Date Issued:08 Jan 2019
Abstract:Information spreads at a pace never seen before on online platforms, even when this information is fake. Fake news can have substantial impact, for instance when it concern politics and influences the results of legislations or elections. Finding a methodology to verify if some piece of news is true or false is hence essential. In this work, we propose a methodology to create task-generic features that are paired with textual features in order to detect fake news. Task-generic features are created by elaborating on metadata attached to answers from Google’s search engine, and by using crowdsourcing for missing values. We experimentally validate our method on a dataset for fake news detection based on the PolitiFact website. Our results show an improvement in F1-Score of 3% over the state of the art, which is significant for a 6-class task.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Truth and Lies: Deception and Cognition on the Internet

Please email if you need this content in ADA-compliant format.

This item is licensed under a Creative Commons License Creative Commons