Hierarchical Attention Networks for Cyberbullying Detection on the Instagram Social Network
Open accessAbstract
Cyberbullying has become one of the most pressing online risks for young people and has raised serious concerns in society. The emerging literature identifies cyberbullying as repetitive acts that occur over time rather than one-off incidents. Yet, there has been relatively little work to model the hierarchical structure of social media sessions and the temporal dynamics of cyberbullying in online social network sessions. We propose a hierarchical attention network for cyberbullying detection that takes these aspects of cyberbullying into account. The primary distinctive characteristics of our approach include: (i) a hierarchical structure that mirrors the structure of a social media session; (ii) levels of attention mechanisms applied at the word and comment level, thereby enabling the model to pay different amounts of attention to words and comments, depending on the context; and (iii) a cyberbullying detection task that also predicts the interval of time between two adjacent comments. These characteristics allow the model to exploit the commonalities and differences across these two tasks to improve the performance of cyberbullying detection. Experiments on a real-world dataset from Instagram, the social media platform on which the highest percentage of users have reported experiencing cyberbullying, reveal that the proposed architecture outperforms the state-of-the-art method.
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Hierarchical Attention Networks (HAN) have demonstrated promising results at both the document and sentence levels, especially in detecting cyberbullying.
“Hierarchical Attention Networks (HAN) have demonstrated promising results at both the document and sentence levels, especially in detecting cyberbullying.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [118]
The Results section reports that HAN outperformed LSTM and CNN at identifying cyberbullying sessions. However, the model applies attention at the word and comment levels and predicts at the session level; the paper does not demonstrate cyberbullying detection at both document and sentence levels. Page numbers are unavailable in our copy. (LLM, conf=0.86)
Experimental — accuracy not yet measuredHANs have been used to understand the common tone of a social media message and the presence of certain bullying behaviors at the sentence level.
“HANs have been used to understand the common tone of a social media message and the presence of certain bullying behaviors at the sentence level [61,118,119].”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [118]
The Introduction and Conclusions describe a hierarchical attention network that uses words and comments in context to detect cyberbullying in Instagram sessions. The Introduction contrasts an insulting comment with a joking one, which supports the idea of attending to tone. But the paper does not describe identifying specific bullying behaviors at the sentence level; its detection target is the social media session. (LLM, conf=0.85)
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- Authors
- Cheng, L., Guo, R., Silva, Y., Hall, D., & Liu, H.
- Affiliations
- Arizona State University (US)
- Venue
- Proceedings of the 2019 SIAM International Conference on Data Mining
- Published
- 2019-05-06
- Pages
- 235-243
- OpenAlex
- W2944606605
- Cited by
- 95
- References
- 40
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 12 · 2023: 11 · 2022: 16 · 2021: 26 · 2020: 15 · 2019: 4
- FWCI
- 5.83
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Society for Industrial and Applied Mathematics eBooks (publishedVersion)
- Funders
- National Science Foundation (1719722)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- cyberbullying detection, hierarchical attention network, Instagram, attention mechanism, online harassment, youth
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