The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Mean Birds: Detecting Aggression and Bullying on Twitter
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.
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Cyberbullying: Its nature and impact in secondary school pupils
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Cited by
Social Media, Echo Chambers, and Political Polarization
Barberá, P. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.004 · Cited by 373
Automatic cyberbullying detection: A systematic review
Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P. C., Carvalho, J. P., Oliveira, S., Coheur, L., Paulino, P., Veiga Simão, A. M., & Trancoso, I. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.12.021 · Cited by 307
Misinformation, Disinformation, and Online Propaganda
Guess, A. M., & Lyons, B. A. (2020) · Social Media and Democracy · doi:10.1017/9781108890960.003 · Cited by 284
Understanding the Effect of Deplatforming on Social Networks
Ali, S., Saeed, M. H., Aldreabi, E., Blackburn, J., De Cristofaro, E., Zannettou, S., & Stringhini, G. (2021) · 13th ACM Web Science Conference 2021 · doi:10.1145/3447535.3462637 · Cited by 109
Self-citationTowards Cyberbullying-free social media in smart cities: a unified multi-modal approach
Kumari, K., Singh, J. P., Dwivedi, Y. K., & Rana, N. P. (2020) · Soft Computing · doi:10.1007/s00500-019-04550-x · Cited by 81
Metadata
- Authors
- Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., & Vakali, A.
- Venue
- Proceedings of the 2017 ACM on Web Science Conference
- Publisher
- ACM
- Published
- 2017-06-25
- Pages
- 13-22
- OpenAlex
- W2594902547
- Cited by
- 369
- References
- 40
- FWCI
- 21.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Chatzakou, D., Kourtellis, N., Blackburn, J., De Cristofaro, E., Stringhini, G., & Vakali, A. (2017). Mean Birds: Detecting Aggression and Bullying on Twitter. Proceedings of the 2017 ACM on Web Science Conference. https://doi.org/10.1145/3091478.3091487
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