Health-related rumour detection on Twitter

Rosa Sicilia, Stella Lo Giudice, Yulong Pei, Mykola Pechenizkiy, Paolo Soda

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

8 Citaten (Scopus)

Samenvatting

In the last years social networks have emerged as a critical mean for information spreading. In spite of all the positive consequences this phenomenon brings, unverified and instrumentally relevant information statements in circulation, named as rumours, are becoming a potential threat to the society. Recently, there have been several studies on topic-independent rumour detection on Twitter. In this paper we present a novel rumour detection system which focuses on a specific topic, that is health-related rumours on Twitter. To this aim, we constructed a new subset of features including influence potential and network characteristics features. We tested our approach on a real dataset observing promising results, as it is able to correctly detect about 89% of rumours, with acceptable levels of precision.

Originele taal-2Engels
TitelProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
UitgeverijInstitute of Electrical and Electronics Engineers
Pagina's1599-1606
Aantal pagina's8
Volume2017-January
ISBN van elektronische versie9781509030491
DOI's
StatusGepubliceerd - 15 dec 2017
Evenement2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017) - Kansas City, Verenigde Staten van Amerika
Duur: 13 nov 201716 nov 2017

Congres

Congres2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017)
Verkorte titelBIBM 2017
LandVerenigde Staten van Amerika
StadKansas City
Periode13/11/1716/11/17

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