Fake News Spreaders Profiling through Behavioural Analysis Notebook for PAN at CLEF 2020

Journal article (2020)

Authors

Matteo Cardaioli Università degli Studi di Padova, GFT Italy
S. Cecconello Università degli Studi di Padova
M. Conti Università degli Studi di Padova
Luca Pajola Università degli Studi di Padova
Federico Turrin Università degli Studi di Padova
Affiliation
External organisation
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Published Date

2020

Language

English

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External organisation

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 describes 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.