Health-related rumour detection on Twitter

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

5 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationProceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
PublisherInstitute of Electrical and Electronics Engineers
Number of pages8
ISBN (Electronic)9781509030491
Publication statusPublished - 15 Dec 2017
Event2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017) - Kansas City, United States
Duration: 13 Nov 201716 Nov 2017


Conference2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017)
Abbreviated titleBIBM 2017
CountryUnited States
CityKansas City


  • Health rumour detection
  • Network- and User-based Features
  • Social microblog
  • Twitter

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