Summary of Bridging the Gap in Online Hate Speech Detection: a Comparative Analysis Of Bert and Traditional Models For Homophobic Content Identification on X/twitter, by Josh Mcgiff and Nikola S. Nikolov
Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter
by Josh McGiff, Nikola S. Nikolov
First submitted to arxiv on: 15 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 presents a significant advancement in online hate speech detection, specifically focusing on homophobia, an underrepresented area in sentiment analysis research. By utilizing BERT and traditional machine learning methods, the authors developed a nuanced approach to identify homophobic content on X/Twitter. The study highlights the importance of contextual understanding in detecting nuanced hate speech, demonstrating that the choice of validation technique can impact model performance. The authors also release the largest open-source labelled English dataset for homophobia detection, providing insights into the effective detection of homophobic content and laying groundwork for future advancements in hate speech analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about finding a way to identify mean and hurtful comments on social media that are aimed at people who are gay or lesbian. Currently, there isn’t much research done on this topic, so it’s an important step towards making the internet a safer and more inclusive place. The researchers used special computer programs called sentiment analysis models to help them understand what people are saying online. They found that one of these models, called BERT, is particularly good at spotting mean comments. But they also discovered that how you test the model can affect how well it works. To help other researchers and make it easier for them to detect mean comments, the authors created a large collection of examples labeled as homophobic or not homophobic. They hope this will be an important step towards making the internet a kinder place. |
Keywords
» Artificial intelligence » Bert » Machine learning
