ArticlePloS one2020
Cyberbullying severity detection: A machine learning approach.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Fusing Transformer-XL with bi-directional recurrent networks for cyberbullying detection.PeerJ. Computer science · 2025Article
- Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques.Scientific reports · 2024Article
- Addressing cyberbullying in Urdu tweets: a comprehensive dataset and detection system.PeerJ. Computer science · 2024Article
- Detecting cyberbullying using deep learning techniques: a pre-trained glove and focal loss technique.PeerJ. Computer science · 2024Article
- Workplace violence in the COVID-19 era: a qualitative analysis of harassment and threats against public health professionals in Canada.BMJ public health · 2023Article
- Site Agnostic Approach to Early Detection of Cyberbullying on Social Media Networks.Sensors (Basel, Switzerland) · 2023Article
- Trajectory tracking of changes digital divide prediction factors in the elderly through machine learning.PloS one · 2023Article
- Nursing Intervention for Preventing Cyberbullying and Reducing Its Negative Impact on Students: A Scoping Review.Journal of multidisciplinary healthcare · 2023Article
- Deep Learning Approaches for Cyberbullying Detection and Classification on Social Media.Computational intelligence and neuroscience · 2022Article
- Artificial Intelligence to Address Cyberbullying, Harassment and Abuse: New Directions in the Midst of Complexity.International journal of bullying prevention : an official publication of the International Bullying Prevention Association · 2022Article
- Trends in US Pediatric Hospital Admissions in 2020 Compared With the Decade Before the COVID-19 Pandemic.JAMA network open · 2021Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
With widespread usage of online social networks and its popularity, social networking platforms have given us incalculable opportunities than ever before, and its benefits are undeniable. Despite benefits, people may be humiliated, insulted, bullied, and harassed by anonymous users, strangers, or peers. In this study, we have proposed a cyberbullying detection framework to generate features from Twitter content by leveraging a pointwise mutual information technique. Based on these features, we developed a supervised machine learning solution for cyberbullying detection and multi-class categorization of its severity in Twitter. In the study we applied Embedding, Sentiment, and Lexicon features along with PMI-semantic orientation. Extracted features were applied with Naïve Bayes, KNN, Decision Tree, Random Forest, and Support Vector Machine algorithms. Results from experiments with our proposed framework in a multi-class setting are promising both with respect to Kappa, classifier accuracy and f-measure metrics, as well as in a binary setting. These results indicate that our proposed framework provides a feasible solution to detect cyberbullying behavior and its severity in online social networks. Finally, we compared the results of proposed and baseline features with other machine learning algorithms. Findings of the comparison indicate the significance of the proposed features in cyberbullying detection.
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