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An Emotional Analysis of False Information in Social Media and News Articles

2019-08-26 22:49:35
Bilal Ghanem, Paolo Rosso, Francisco Rangel
arXiv_AI
         
Abstract

Fake news is risky since it has been created to manipulate the readers' opinions and beliefs. In this work, we compared the language of false news to the real one of real news from an emotional perspective, considering a set of false information types (propaganda, hoax, clickbait, and satire) from social media and online news articles sources. Our experiments showed that false information has different emotional patterns in each of its types, and emotions play a key role in deceiving the reader. Based on that, we proposed a LSTM neural network model that is emotionally-infused to detect false news.

Abstract (translated)

URL

https://arxiv.org/abs/1908.09951

PDF

https://arxiv.org/pdf/1908.09951.pdf

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