On the Impact of Emotions on the Detection of False Information

Abstract

A great amount of fake news are propagated in online social media, with the aim, usually, to deceive users and formulate specific opinions. The threat is even greater when the purpose is political or ideological and they are used during electoral campaigns. Bots play a key role in disseminating these false claims. False information is intentionally written to trigger emotions to the readers in an attempt to be believed and be disseminated in social media. Therefore, in order to discriminate credible from non credible information, we believe that it is important to take into account these emotional signals. In this paper we describe the way that emotional features have been integrated in deep learning models in order to detect if and when emotions are evoked in fake news.