research-article

Identification of credulous users on Twitter

Authors:

Alessandro Balestrucci,

Rocco De Nicola,

Catia TrubianiAuthors Info & Claims

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

Pages 2096 - 2103

https://doi.org/10.1145/3297280.3297486

Published: 08 April 2019 Publication History

Abstract

Social networks can quickly propagate information to large audiences and can be used to spread fake news or to provide false figures of popularity. Social bots, i.e., software robots that automatically interact with human users and produce content under a fictive identity, are used for such harmful activities. In this paper, we study the relationship between bots and genuine human users with the aim of identifying those "credulous" users who are particularly exposed, and unintentionally contribute, to the activities planned by a network of bots. Spotting credulous users is useful to service providers to display warnings on their dashboards, scan their activities for early signs of attacks, or take more active measures to prevent or limit the negative effects of their activities.

Here we aim at identifying credulous users on Twitter starting from those involved in any social relation with a bot. To that end, we rely on an existing bot detector along with its dataset of genuine users and bots that we extend with additional information about the friends of each genuine user. To single out credulous users out of genuine ones, we study the effectiveness of different metrics or combinations thereof. We see this as a first step towards singling out features that can be used to detect credulous users without resorting to the expensive analysis of the nature of their friends.

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Cited By

Mazoochi MAsadi NRahmani FRabiei L(2023)Identifying Persian bots on Twitter; which feature is more important: Account Information or Tweet Contents?International Journal of Information and Communication Technology Research10.61186/itrc.15.1.3515:1(35-44)Online publication date: 1-Feb-2023
https://doi.org/10.61186/itrc.15.1.35
Echeverría MRodríguez Cano C(2023)¿La alfabetización digital activa la incredulidad en noticias falsas? Eficacia de las actitudes y estrategias contra la desinformación en MéxicoRevista de Comunicación10.26441/RC22.2-2023-3246Online publication date: 14-Aug-2023
https://doi.org/10.26441/RC22.2-2023-3246
Mrs. K. Pazhanivel Ajai Kumar. B Mageshwaran. M Dhivakar. K (2022)A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers ModelInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-4610(395-403)Online publication date: 11-Jun-2022
https://doi.org/10.48175/IJARSCT-4610
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cover image ACM Conferences

SAC '19: Proceedings of the 34th ACM/SIGAPP Symposium on Applied Computing

April 2019

2682 pages

ISBN:9781450359337

DOI:10.1145/3297280

Conference Chairs:
Chih-Cheng Hung
Kennesaw State University, Marietta, Georgia
,
George A. Papadopoulos
University of Cyprus, Nicosia, Cyprus

Copyright © 2019 ACM.

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Published: 08 April 2019

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Cited By

Mazoochi MAsadi NRahmani FRabiei L(2023)Identifying Persian bots on Twitter; which feature is more important: Account Information or Tweet Contents?International Journal of Information and Communication Technology Research10.61186/itrc.15.1.3515:1(35-44)Online publication date: 1-Feb-2023
https://doi.org/10.61186/itrc.15.1.35
Echeverría MRodríguez Cano C(2023)¿La alfabetización digital activa la incredulidad en noticias falsas? Eficacia de las actitudes y estrategias contra la desinformación en MéxicoRevista de Comunicación10.26441/RC22.2-2023-3246Online publication date: 14-Aug-2023
https://doi.org/10.26441/RC22.2-2023-3246
Mrs. K. Pazhanivel Ajai Kumar. B Mageshwaran. M Dhivakar. K (2022)A Deep Learning Approach for Robust Detection of Bots in Twitter Using Transformers ModelInternational Journal of Advanced Research in Science, Communication and Technology10.48175/IJARSCT-4610(395-403)Online publication date: 11-Jun-2022
https://doi.org/10.48175/IJARSCT-4610
de Morais DDigiampietri L(2021)Methods and Challenges in Social Bots Detection: A Systematic ReviewProceedings of the XVII Brazilian Symposium on Information Systems10.1145/3466933.3466973(1-8)Online publication date: 7-Jun-2021
https://dl.acm.org/doi/10.1145/3466933.3466973
Alharbi ADong HYi XTari ZKhalil I(2021)Social Media Identity Deception DetectionACM Computing Surveys10.1145/344637254:3(1-35)Online publication date: 17-Apr-2021
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Martin-Gutierrez DHernandez-Penaloza GHernandez ALozano-Diez AAlvarez F(2021)A Deep Learning Approach for Robust Detection of Bots in Twitter Using TransformersIEEE Access10.1109/ACCESS.2021.30686599(54591-54601)Online publication date: 2021
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Ben Sassi IBen Yahia S(2021)Malicious accounts detection from online social networks: a systematic review of literatureInternational Journal of General Systems10.1080/03081079.2021.197677350:7(741-814)Online publication date: 21-Sep-2021
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Balestrucci ANicola R(2020)Credulous Users and Fake News: a Real Case Study on the Propagation in Twitter2020 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)10.1109/EAIS48028.2020.9122764(1-8)Online publication date: May-2020
https://doi.org/10.1109/EAIS48028.2020.9122764
Kušen EStrembeck M(2020)You talkin’ to me? Exploring Human/Bot Communication Patterns during Riot EventsInformation Processing and Management: an International Journal10.1016/j.ipm.2019.10212657:1Online publication date: 1-Jan-2020
https://dl.acm.org/doi/10.1016/j.ipm.2019.102126
Balestrucci ADe Nicola RPetrocchi MTrubiani C(2019)Do You Really Follow Them? Automatic Detection of Credulous Twitter UsersIntelligent Data Engineering and Automated Learning – IDEAL 201910.1007/978-3-030-33607-3_44(402-410)Online publication date: 14-Nov-2019
https://dl.acm.org/doi/10.1007/978-3-030-33607-3_44

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