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

Conti, Mauro and Lain, Daniele and Lazzeretti, Riccardo and Lovisotto, Giulio and Quattrociocchi, Walter It’s Always April Fools’ Day! On the Difficulty of Social Network Misinformation Classification via Propagation Features. Working Paper arXiv (Submitted)

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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 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 dif- ficult 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 (as representative of misinformation), and scientific news (representative of fact-checked content). Our findings show that conspiracy content actually reverberates in a way which is hard to distinguish from the one scientific content does: for the classification mechanisms we investigated, classification F1-score never exceeds 0.65 during content propagation stages, and is still less than 0.7 even after propagation is complete.

Item Type: Working Paper (Working Paper)
Identification Number: arXiv:1701.04221
Subjects: H Social Sciences > H Social Sciences (General)
H Social Sciences > HA Statistics
Research Area: Computer Science and Applications
Depositing User: Caterina Tangheroni
Date Deposited: 18 Apr 2017 08:50
Last Modified: 18 Apr 2017 08:50
URI: http://eprints.imtlucca.it/id/eprint/3688

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