The DOI matches its Crossref record; 64 of the 70 references it cites were found in a registry or an index.
Automatic cyberbullying detection: A systematic review
Overview
Integrity signals
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Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“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]
Citations
Cites
Distributed Representations of Words and Phrases and their Compositionality
Mikolov, T., Sutskever, I., Chen, K., Corrado, G., & Dean, J. (2013) · arXiv · doi:10.48550/arxiv.1310.4546 · Cited by 24,533
VADER: a parsimonious rule-based model for sentiment analysis of social media text
Hutto, C., & Gilbert, E. (2014) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v8i1.14550 · Cited by 6,087
Cyberbullying: Its nature and impact in secondary school pupils
Smith, P. K., Mahdavi, J., Carvalho, M., Fisher, S., Russell, S., & Tippett, N. (2008) · Journal of Child Psychology and Psychiatry · doi:10.1111/j.1469-7610.2007.01846.x · Cited by 3,639
A Neural Probabilistic Language Model
Bengio, Y., Ducharme, R., Vincent, P., Jauvin, C., Jaz, K., Hofmann, T., Poggio, T., & Shawe-Taylor, J. S. (2000) · Cited by 3,451
A neural probabilistic language model
BengioYoshua, DucharmeRéjean, VincentPascal, & JanvinChristian (2003) · Journal of Machine Learning Research · doi:10.5555/944919.944966 · Cited by 2,646
Chawla, N. V., Japkowicz, N., & Kotcz, A. (2004) · ACM SIGKDD Explorations Newsletter · doi:10.1145/1007730.1007733 · Cited by 2,245
Data Mining and Knowledge Discovery Handbook
(2010) · doi:10.1007/978-0-387-09823-4 · Cited by 2,190
Norms of valence, arousal, and dominance for 13,915 English lemmas
Warriner, A. B., Kuperman, V., & Brysbaert, M. (2013) · Behavior Research Methods · doi:10.3758/s13428-012-0314-x · Cited by 1,959
Bullying and the peer group: A review
Salmivalli, C. (2009) · Aggression and Violent Behavior · doi:10.1016/j.avb.2009.08.007 · Cited by 1,767
Bullies Move Beyond the Schoolyard: A Preliminary Look at Cyberbullying
Patchin, J. W., & Hinduja, S. (2006) · Youth Violence and Juvenile Justice · doi:10.1177/1541204006286288 · Cited by 1,701
Flagged first, then by citations
6 of the 70 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- Nahar, 2014, An effective approach for cyberbullying detection, Communications in Information Science and Management Engineering, 3, 238
Cited by
Sorted by citations
Natarajan, Y., Srihari, K., Dhiman, G., Somasundaram, K., Sharma, A., Rajeskannan, S., Soni, M., Gaba, G. S., AlZain, M. A., & Masud, M. (2021) · Mathematical Problems in Engineering · doi:10.1155/2021/6644652 · Cited by 218
Cyberbullying detection solutions based on deep learning architectures
Iwendi, C., Srivastava, G., Khan, S., & Maddikunta, P. K. R. (2023) · Multimedia Systems · doi:10.1007/s00530-020-00701-5 · Cited by 205
Cyberbullying detection for low-resource languages and dialects: Review of the state of the art
Mahmud, T., Ptaszynski, M. E., Eronen, J., & Masui, F. (2023) · Information Processing & Management · doi:10.1016/j.ipm.2023.103454 · Cited by 181
Multilingual Offensive Language Identification with Cross-lingual Embeddings
Ranasinghe, T., & Zampieri, M. (2020) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2020.emnlp-main.470 · Cited by 172
Accurate Cyberbullying Detection and Prevention on Social Media
Perera, A., & Fernando, P. (2021) · Procedia Computer Science · doi:10.1016/j.procs.2021.01.207 · Cited by 145
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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