Summary of Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying, by Rohan Biswas et al.
Securing Social Spaces: Harnessing Deep Learning to Eradicate Cyberbullying
by Rohan Biswas, Kasturi Ganguly, Arijit Das, Diganta Saha
First submitted to arxiv on: 1 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed deep learning-based approach utilizes BERT and BiLSTM architectures to effectively address cyberbullying in online spaces. By analyzing large volumes of posts, the approach can predict potential instances of cyberbullying. The hateBERT model, an extension of BERT focused on hate speech detection, achieves an accuracy rate of 89.16%, outperforming four other models. This research contributes to “Computational Intelligence for Social Transformation,” promising a safer and more inclusive digital landscape. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Cyberbullying is a serious problem that affects people’s mental and physical health when they use social media. It’s important to find better ways to detect cyberbullying so online spaces can be safer. To do this, researchers introduced a new approach using deep learning. This approach looks at lots of posts online and predicts if someone might be bullied. The best model, called hateBERT, was able to accurately spot cyberbullying 89% of the time. |
Keywords
* Artificial intelligence * Bert * Deep learning
