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Detecting Aggressors and Bullies on Twitter

Chatzakou, D; Kourtellis, N; Blackburn, J; Cristofaro, ED; Stringhini, G; Vakali, A; (2017) Detecting Aggressors and Bullies on Twitter. In: Barrett, R and Cummings, R and Agichtein, E and Gabrilovich, E, (eds.) Proceedings of the 26th International Conference on World Wide Web Companion. (pp. pp. 767-768). ACM: Perth, Australia. Green open access

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Abstract

Online social networks constitute an integral part of people's every day social activity and the existence of aggressive and bullying phenomena in such spaces is inevitable. In this work, we analyze user behavior on Twitter in an effort to detect cyberbullies and cuber-aggressors by considering specific attributes of their online activity using machine learning classifiers.

Type: Proceedings paper
Title: Detecting Aggressors and Bullies on Twitter
Event: WWW 2017 Companion
ISBN-13: 978-1-4503-4914-7
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3041021.3054211
Publisher version: http://dx.doi.org/10.1145/3041021.3054211
Language: English
Additional information: © 2017 International World Wide Web Conference Committee (IW3C2), published under Creative Commons CC BY 4.0 License
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/10046721
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