Deep Learning for Detecting Cyberbullying Across Multiple Social Media Platforms
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Agrawal et al. [68] found that swear words are not sufficient for detecting cyberbullying.
“The study gives some interesting insights on the detection of cyberbullying, such as that swear words are not sufficient for detecting cyberbullying.”
From A Review on Deep-Learning-Based Cyberbullying Detection (2023) · cites this paper as [68]
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“Agrawal et al. [29] also verified the feasibility of neural networks for detecting cyberbullying.”
BackgroundMethodInfluentialCyberbullying detection from tweets using deep learning
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“The authors of [28,29] used twitter data set which contain around 16 k tweets.”
BackgroundMethodDetection of Cyberhectoring on Instagram
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- Authors
- Agrawal, S., & Awekar, A.
- Affiliations
- Adobe Systems (United States) (US)
- Indian Institute of Technology Guwahati (IN)
- Corresponding author
- Awekar, Amit
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2018
- Pages
- 141-153
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2784418822
- Cited by
- 387
- References
- 16
- Citations by year
- 2026: 20 · 2025: 27 · 2024: 52 · 2023: 67 · 2022: 54 · 2021: 60 · 2020: 71 · 2019: 27 · 2018: 9
- FWCI
- 16.83
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Hate Speech and Cyberbullying Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
- Keywords
- cyberbullying detection, social networking sites, transfer learning, cross-platform generalization, deep learning, Twitter, Wikipedia, online harassment
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