Shielding against online harm: A survey on text analysis to prevent cyberbullying
Integrity signals
Registry
Cites
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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?
Various forms of cyberbullying include sarcasm, toxicity, hate speech, and attacks based on ethnicity and religion, which are used to target individuals on social media platforms.
“Additionally, there are various forms of cyberbullying, including sarcasm, toxicity, hate speech, and attacks based on ethnicity and religion, which are used to target individuals on social media platforms [22].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [22]
The citation lists haven’t been fetched yet
References 88 · Cited by 13
- Authors
- Mishra, A., Sinha, S., & George, C. P.
- Affiliations
- Indian Institute of Technology Goa (IN)
- Corresponding author
- Mishra, Akanksha
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2024-07
- Volume
- 133
- Pages
- 108241
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W4393256576
- Semantic Scholar
- da0c2e51d6f49539639f712d1fdcd050c892160d
- Cited by
- 13
- References
- 88
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 3
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion)
- 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, cyberbullying detection, online harms, social networking sites, instant messaging apps, text preprocessing, feature representation, sentiment analysis, word embeddings, text summarization, user data, datasets, machine learning, artificial neural networks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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