Summary of Detecting Lgbtq+ Instances Of Cyberbullying, by Muhammad Arslan et al.
Detecting LGBTQ+ Instances of Cyberbullying
by Muhammad Arslan, Manuel Sandoval Madrigal, Mohammed Abuhamad, Deborah L. Hall, Yasin N. Silva
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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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 paper investigates the effectiveness of various transformer-based machine learning models in detecting cyberbullying incidents targeting LGBTQ+ individuals on social media. The study aims to compare the efficacy of these models, analyzing their strengths and limitations using real-world data. This research is crucial for developing strategies to combat online harassment and support the mental health and well-being of this vulnerable community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper compares different transformer-based machine learning models to detect cyberbullying targeting LGBTQ+ individuals on social media. It aims to determine which model works best by analyzing their effectiveness with real data. |
Keywords
» Artificial intelligence » Machine learning » Transformer
