Health-related rumour detection on Twitter

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

doi:10.1109/BIBM.2017.8217899

Health-related rumour detection on Twitter

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

Data Mining

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

5 Citations (Scopus)

Abstract

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 language	English
Title of host publication	Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017
Publisher	Institute of Electrical and Electronics Engineers
Pages	1599-1606
Number of pages	8
Volume	2017-January
ISBN (Electronic)	9781509030491
DOIs	https://doi.org/10.1109/BIBM.2017.8217899
Publication status	Published - 15 Dec 2017
Event	2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017) - Kansas City, United States Duration: 13 Nov 2017 → 16 Nov 2017

Conference

Conference	2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017)
Abbreviated title	BIBM 2017
Country	United States
City	Kansas City
Period	13/11/17 → 16/11/17

Keywords

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

Access to Document

10.1109/BIBM.2017.8217899

Link to publication in Scopus

Fingerprint Dive into the research topics of 'Health-related rumour detection on Twitter'. Together they form a unique fingerprint.

Cite this

@inproceedings{13ecea80d6944ca680c83c9ac0f31cac,

title = "Health-related rumour detection on Twitter",

abstract = "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.",

keywords = "Health rumour detection, Network- and User-based Features, Social microblog, Twitter",

author = "Rosa Sicilia and Giudice, {Stella Lo} and Yulong Pei and Mykola Pechenizkiy and Paolo Soda",

year = "2017",

month = dec,

day = "15",

doi = "10.1109/BIBM.2017.8217899",

language = "English",

volume = "2017-January",

pages = "1599--1606",

booktitle = "Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017",

publisher = "Institute of Electrical and Electronics Engineers",

address = "United States",

note = "2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017), BIBM 2017 ; Conference date: 13-11-2017 Through 16-11-2017",

}

Sicilia, R, Giudice, SL, Pei, Y , Pechenizkiy, M & Soda, P 2017, Health-related rumour detection on Twitter. in Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. vol. 2017-January, Institute of Electrical and Electronics Engineers, pp. 1599-1606, 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017), Kansas City, United States, 13/11/17. https://doi.org/10.1109/BIBM.2017.8217899

Health-related rumour detection on Twitter. / Sicilia, Rosa; Giudice, Stella Lo; Pei, Yulong ; Pechenizkiy, Mykola; Soda, Paolo.

Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017. Vol. 2017-January Institute of Electrical and Electronics Engineers, 2017. p. 1599-1606.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Health-related rumour detection on Twitter

AU - Sicilia, Rosa

AU - Giudice, Stella Lo

AU - Pei, Yulong

AU - Pechenizkiy, Mykola

AU - Soda, Paolo

PY - 2017/12/15

Y1 - 2017/12/15

N2 - 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.

AB - 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.

KW - Health rumour detection

KW - Network- and User-based Features

KW - Social microblog

KW - Twitter

UR - http://www.scopus.com/inward/record.url?scp=85045975327&partnerID=8YFLogxK

U2 - 10.1109/BIBM.2017.8217899

DO - 10.1109/BIBM.2017.8217899

M3 - Conference contribution

AN - SCOPUS:85045975327

VL - 2017-January

SP - 1599

EP - 1606

BT - Proceedings - 2017 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2017

PB - Institute of Electrical and Electronics Engineers

T2 - 2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM 2017)

Y2 - 13 November 2017 through 16 November 2017

ER -