Creating Task-Generic Features for Fake News Detection

Olivieri, Alex
Shabani, Shaban
Sokhn, Maria
Cudré-Mauroux, Philippe
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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.
Truth and Lies: Deception and Cognition on the Internet, Internet and the Digital Economy, Crowdsourcing, Fake News, Google Custom Search, Machine Learning, Politics
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