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Mean birds: Detecting aggression and bullying on Twitter

Chatzakou, D; Kourtellis, N; Blackburn, J; De Cristofaro, E; Stringhini, G; Vakali, A; (2017) Mean birds: Detecting aggression and bullying on Twitter. In: Proceedings of the 2017 ACM on Web Science Conference. (pp. pp. 13-22). ACM publishing: Troy, NY, USA. Green open access

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Abstract

In recent years, bullying and aggression against social media users have grown significantly, causing serious consequences to victims of all demographics. Nowadays, cyberbullying affects more than half of young social media users worldwide, suffering from prolonged and/or coordinated digital harassment. Also, tools and technologies geared to understand and mitigate it are scarce and mostly ineffective. In this paper, we present a principled and scalable approach to detect bullying and aggressive behavior on Twitter. We propose a robust methodology for extracting text, user, and network-based attributes, studying the properties of bullies and aggressors, and what features distinguish them from regular users. We find that bullies post less, participate in fewer online communities, and are less popular than normal users. Aggressors are relatively popular and tend to include more negativity in their posts. We evaluate our methodology using a corpus of 1.6M tweets posted over 3 months, and show that machine learning classification algorithms can accurately detect users exhibiting bullying and aggressive behavior, with over 90% AUC.

Type: Proceedings paper
Title: Mean birds: Detecting aggression and bullying on Twitter
Event: WebSci '17
ISBN-13: 9781450348966
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/3091478.3091487
Publisher version: http://dx.doi.org/10.1145/3091478.3091487
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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: https://discovery.ucl.ac.uk/id/eprint/1543150
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