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Cited by 265Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion MiningBullying, Victimization, and Aggression

Approaches to automated detection of cyberbullying: A survey

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Salawu, Semiu, He, Yulan, Lumsden, Joanna·2020·IEEE Transactions on Affective Computing
DOI 10.1109/taffc.2017.2761757
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Abstract

Research into cyberbullying detection has increased in recent years, due in part to the proliferation of cyberbullying across social media and its detrimental effect on young people. A growing body of work is emerging on automated approaches to cyberbullying detection. These approaches utilise machine learning and natural language processing techniques to identify the characteristics of a cyberbullying exchange and automatically detect cyberbullying by matching textual data to the identified traits. In this paper, we present a systematic review of published research (as identified via Scopus, ACM and IEEE Xplore bibliographic databases) on cyberbullying detection approaches. On the basis of our extensive literature review, we categorise existing approaches into 4 main classes, namely supervised learning, lexicon-based, rule-based, and mixed-initiative approaches. Supervised learning-based approaches typically use classifiers such as SVM and Naıve Bayes to develop predictive models for cyberbullying detection. Lexicon-based systems utilise word lists and use the presence of words within the lists to detect cyberbullying. Rule-based approaches match text to predefined rules to identify bullying, and mixed-initiatives approaches combine human-based reasoning with one or more of the aforementioned approaches. We found lack of labelled datasets and non-holistic consideration of cyberbullying by researchers when developing detection systems are two key challenges facing cyberbullying detection research. This paper essentially maps out the state-of-the-art in cyberbullying detection research and serves as a resource for researchers to determine where to best direct their future research efforts in this field.

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  • A few survey papers address the open issues and challenges.

    “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 [26]

  • The sentence 'The image that you have sent so irritated me and I would rather not contact with you any longer!' is not easy to detect as cyberbullying without investigating from a rationale factor, even though a model shows negative sentiment.

    “For example, 'The image that you have sent so irritated me and I would rather not contact with you any longer!' is not easy to detect as cyberbullying without investigating from a rationale factor, albeit that model shows negative sentiment [26].”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [26]

  • Capturing context and analyzing sentiment from different types of sentences is difficult for cyberbullying detection.

    “Language challenge: Capturing context and analyzing the sentiment from different types of sentences is a difficult task and challenging work for cyberbullying detection.”

    From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [26]

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Research works printed but not linked: 7

  1. [28]28. Fanti, K.A., Demetriou, A.G., and Hawa, V.V. (2012). A Longi- tudinal Study of Cyberbullying: Examining Risk and Protective Factors. European Journal of Developmental Psychology, 9(2), p.168-181.
  2. [30]30. Garlan-Garcia, M., Gamon, M., Counts, S. and Horvitz, E. (2013). Predicting Depression via Social Media. (p. 2). IN: International AAAI Conference on Weblogs and Social Media (ICWSM). Bos- ton. July 8-11, 2013
  3. [35]35. Hosseinmardi, H., Mattson, S.A., Rafiq, R., Han, R., Lv, Q. and Mishra, S. (2015). Poster: Detection of Cyberbullying in a Mobile Social Network: Systems Issues. IN: Annual International Con- ference on Mobile Systems, Applications, and Services. 13 th . Florence. May 18 th -22nd, 2015. ACM, 481-481.
  4. [36]36. Huang, Q., Singh, V.K. and Atrey, P.K., (2014). Cyberbullying detection using social and textual analysis. IN: International Workshop on Socially-Aware Multimedia. 3 rd . Orlando. Novem- ber 7, 2014. ACM, 3-6.
  5. [61]61. NaliniPriya. G and Asswini. M. (2015). A dynamic cognitive sys- tem for automatic detection and prevention of cyber-bullying at- tacks. ARPN Journal of Engineering and Applied Science, 10(10), pp.4618-4626.
  6. [71]71. Patchin, J.W. and Hinduja, S. (2012). Preventing and Responding To Cyberbullying: Expert Perspectives. Thousand Oaks: Routledge.
  7. [80]80. Sanchez, H. and Kumar, S. (2011). Twitter Bullying Detection. NSDI, 12, p.15-22.

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Authors
Salawu, S., He, Y., & Lumsden, J.
Affiliations
Aston University (GB)
ORCID
Venue
IEEE Transactions on Affective Computing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2020-01-01
Volume
11
Issue
1
Pages
3-24
ISSN
  • 1949-3045 (ISSN-L)
  • 2371-9850
DOI
10.1109/taffc.2017.2761757
OpenAlex
W2762029282
Cited by
258
References
112
Citations by year
2026: 22 · 2025: 27 · 2024: 33 · 2023: 41 · 2022: 29 · 2021: 44 · 2020: 26 · 2019: 28 · 2018: 8
FWCI
10.11
Type
journal article
Language
en
Open access
Licence
Locations
  • IEEE Transactions on Affective Computing (publishedVersion)
  • Aston Publications Explorer (Aston University) (submittedVersion, public-domain)
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 media, supervised learning, rule-based detection, annotated dataset, text classification, youth

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