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Automatic cyberbullying detection: A systematic review
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“These studies do not show publicly available datasets for cyberbullying detection, and a few survey papers address the open issues and challenges [26,27].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [27]
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Metadata
- Authors
- Rosa, H., Pereira, N., Ribeiro, R., Ferreira, P. C., Carvalho, J. P., Oliveira, S., Coheur, L., Paulino, P., Veiga Simão, A. M., & Trancoso, I.
- Affiliations
- Instituto de Engenharia de Sistemas e Computadores Investigação e Desenvolvimento (PT)
- University of Lisbon (PT)
- Iscte – Instituto Universitário de Lisboa (PT)
- Corresponding author
- Pereira, N.
- ORCID
- Venue
- Computers in Human Behavior
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 93
- Pages
- 333-345
- ISSN
- 0747-5632 (ISSN-L)
- OpenAlex
- W2904937466
- Cited by
- 307
- References
- 70
- Citations by year
- 2026: 29 · 2025: 31 · 2024: 47 · 2023: 57 · 2022: 48 · 2021: 55 · 2020: 27 · 2019: 13
- FWCI
- 2.76
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Computers in Human Behavior (publishedVersion)
- Portuguese National Funding Agency for Science, Research and Technology (RCAAP Project by FCT) (submittedVersion)
- Funders
- Foundation for Science and Technology (PTDC/MHC/PED/3297/2014, SFRH/BPD/110695/2015)
- Psychological Science of the Faculty of Psychology of the University of Lisbon (UID/PSI/4527/2016)
- Institute of System and Computer Engineering, Research and Development of Lisbon (UID/CEC/50021/2013, SFRH/BSAB/136312/2018)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Social Sciences › Psychology › Social Psychology › Bullying, Victimization, and Aggression
- Keywords
- cyberbullying detection, automated detection, social networks, adolescents, class imbalance, evaluation methodology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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