The growth of social media and the people interconnection led to the digitalization of communication. Nowadays the most influential politicians or scientific communicators use the media to disseminate news or decisions. However, such communications media can be used maliciously to spread the so-called fake news in order to polarise public opinion or to deny scientific theories. It is therefore important to develop intelligent and accurate techniques in order to identify the spreading of fake news. In this paper, we describe the methodology regarding our participation in the PAN@ CLEF Profiling Fake News Spreaders on Twitter competition. We propose a supervised Machine-Learning (ML) based framework to profile fake-news spreaders. Our method relies on the combination of Big Five personality and stylometric features. Finally, we evaluate our framework detection capabilities and performance with different ML models on a tweeter dataset in both English and Spanish languages.

Fake News Spreaders Profiling Through Behavioural Analysis

Matteo Cardaioli;Stefano Cecconello;Mauro Conti;Luca Pajola;Federico Turrin
2020

Abstract

The growth of social media and the people interconnection led to the digitalization of communication. Nowadays the most influential politicians or scientific communicators use the media to disseminate news or decisions. However, such communications media can be used maliciously to spread the so-called fake news in order to polarise public opinion or to deny scientific theories. It is therefore important to develop intelligent and accurate techniques in order to identify the spreading of fake news. In this paper, we describe the methodology regarding our participation in the PAN@ CLEF Profiling Fake News Spreaders on Twitter competition. We propose a supervised Machine-Learning (ML) based framework to profile fake-news spreaders. Our method relies on the combination of Big Five personality and stylometric features. Finally, we evaluate our framework detection capabilities and performance with different ML models on a tweeter dataset in both English and Spanish languages.
CLEF 2020 Conference and Labs of the Evaluation Forum
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11577/3355822
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