Cyber Bullying Detection using Natural Language Processing (NLP) and Text Analytics
Abstract
This final year project is a Twitter dependant application called Cyberbullying Detection using Natural Language Processing and Text Analytics which focused on handling cyberbullying by removing inappropriate tweet content and alert the parents, guardian, and counsellor about the case via email. The application is designed for the student with general IT knowledge. It was fully developed using the Python programming language. Additionally, a feature to update the white-listed username in real-time has been added to the application by the developer based on the feedback from testers. The developer believed that this application will surely help in protecting the mental development of the student by tackling the cyberbullying issues.
Integrity signals
Registry
Cites
Statistics
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?
Imbalance led models to become more motivated to detect the non-cyberbullying than the cyberbullying class and tended to label most instances as non-cyberbullying.
“Firstly, bias in model training: this led models to become more motivated to detect the non-cyberbullying than the cyberbullying class and tended to label most instances as non-cyberbullying [108].”
From Cyberbullying detection: Exploring datasets, technologies, and approaches on social media platforms (2026) · cites this paper as [108]
The citation lists haven’t been fetched yet
References 16 · Cited by 11
- Authors
- Khang Hsien, Y., Arabee Abdul Salam, Z., & Kasinathan, V.
- Affiliations
- Asia Pacific University of Technology & Innovation,Kuala Lumpur,Malaysia
- Asia Pacific University of Technology & Innovation (MY)
- Venue
- 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE)
- Publisher
- IEEE
- Published
- 2022-04-23
- Pages
- 1-4
- OpenAlex
- W4282832237
- Cited by
- 11
- References
- 16
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 3 · 2023: 3
- FWCI
- 1.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE) (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 detection, Twitter, text analysis, natural language processing, student mental health
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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