In the last years social networks have emerged as a critical mean for information spreading bringing along several advantages. At the same time, unverified and instrumentally relevant information state- ments in circulation, named as rumours, are becoming a potential threat to the society. For this reason, although the identification in social microblogs of which topic is a rumour has been studied in several works, there is the need to detect if a post is either a rumor or not. In this paper we cope with this last challenge presenting a novel rumour detection system that leverages on newly designed features, includ- ing influence potential and network characteristics measures. We tested our approach on a real dataset composed of health-related posts collected from Twitter microblog. We observe promising results, as the system is able to correctly detect about 90% of rumours, with acceptable levels of precision.

Twitter rumour detection in the health domain

Sicilia R;Soda P
2018-01-01

Abstract

In the last years social networks have emerged as a critical mean for information spreading bringing along several advantages. At the same time, unverified and instrumentally relevant information state- ments in circulation, named as rumours, are becoming a potential threat to the society. For this reason, although the identification in social microblogs of which topic is a rumour has been studied in several works, there is the need to detect if a post is either a rumor or not. In this paper we cope with this last challenge presenting a novel rumour detection system that leverages on newly designed features, includ- ing influence potential and network characteristics measures. We tested our approach on a real dataset composed of health-related posts collected from Twitter microblog. We observe promising results, as the system is able to correctly detect about 90% of rumours, with acceptable levels of precision.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.12610/4402
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