Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN, and this is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content reverberates in a way which is hard to distinguish from scientific content: for the classification mechanism we investigated, classification F-score never exceeds 0.7.

It's Always April Fools' Day! On the Difficulty of Social Network Misinformation Classification via Propagation Features / Conti, Mauro; Lain, Daniele; Lazzeretti, Riccardo; Lovisotto, Giulio; Quattrociocchi, Walter. - ELETTRONICO. - (2017), pp. 1-6. (Intervento presentato al convegno IEEE Workshop on Information Forensics and Security (WIFS), 2017 tenutosi a Rennes; France nel 4/12/2017-7/12/2017) [10.1109/WIFS.2017.8267653].

It's Always April Fools' Day! On the Difficulty of Social Network Misinformation Classification via Propagation Features

Riccardo Lazzeretti;Walter Quattrociocchi
2017

Abstract

Given the huge impact that Online Social Networks (OSN) had in the way people get informed and form their opinion, they became an attractive playground for malicious entities that want to spread misinformation, and leverage their effect. In fact, misinformation easily spreads on OSN, and this is a huge threat for modern society, possibly influencing also the outcome of elections, or even putting people's life at risk (e.g., spreading "anti-vaccines" misinformation). Therefore, it is of paramount importance for our society to have some sort of "validation" on information spreading through OSN. The need for a wide-scale validation would greatly benefit from automatic tools. In this paper, we show that it is difficult to carry out an automatic classification of misinformation considering only structural properties of content propagation cascades. We focus on structural properties, because they would be inherently difficult to be manipulated, with the the aim of circumventing classification systems. To support our claim, we carry out an extensive evaluation on Facebook posts belonging to conspiracy theories (representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content reverberates in a way which is hard to distinguish from scientific content: for the classification mechanism we investigated, classification F-score never exceeds 0.7.
2017
IEEE Workshop on Information Forensics and Security (WIFS), 2017
Facebook; Peer-to-peer computing; Voting; Tools; Complexity theory; Google
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
It's Always April Fools' Day! On the Difficulty of Social Network Misinformation Classification via Propagation Features / Conti, Mauro; Lain, Daniele; Lazzeretti, Riccardo; Lovisotto, Giulio; Quattrociocchi, Walter. - ELETTRONICO. - (2017), pp. 1-6. (Intervento presentato al convegno IEEE Workshop on Information Forensics and Security (WIFS), 2017 tenutosi a Rennes; France nel 4/12/2017-7/12/2017) [10.1109/WIFS.2017.8267653].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1073100
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