{"title":"缩小网络仇恨言论检测的差距:BERT 与传统模式在 X/Twitter 上识别仇视同性恋内容的比较分析","authors":"Josh McGiff, Nikola S. Nikolov","doi":"10.54254/2755-2721/64/20241346","DOIUrl":null,"url":null,"abstract":"Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research. Utilising advanced sentiment analysis models, particularly BERT, and traditional machine learning methods, we developed a nuanced approach to identify homophobic content on X/Twitter. This research is pivotal due to the persistent underrepresentation of homophobia in detection models. Our findings reveal that while BERT outperforms traditional methods, the choice of validation technique can impact model performance. This underscores the importance of contextual understanding in detecting nuanced hate speech. By releasing the largest open-source labelled English dataset for homophobia detection known to us, an analysis of various models' performance and our strongest BERT-based model, we aim to enhance online safety and inclusivity. Future work will extend to broader LGBTQIA+ hate speech detection, addressing the challenges of sourcing diverse datasets. Through this endeavour, we contribute to the larger effort against online hate, advocating for a more inclusive digital landscape. Our study not only offers insights into the effective detection of homophobic content by improving on previous research results, but it also lays groundwork for future advancements in hate speech analysis.","PeriodicalId":350976,"journal":{"name":"Applied and Computational Engineering","volume":"54 8","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bridging the gap in online hate speech detection: A comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter\",\"authors\":\"Josh McGiff, Nikola S. Nikolov\",\"doi\":\"10.54254/2755-2721/64/20241346\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research. Utilising advanced sentiment analysis models, particularly BERT, and traditional machine learning methods, we developed a nuanced approach to identify homophobic content on X/Twitter. This research is pivotal due to the persistent underrepresentation of homophobia in detection models. Our findings reveal that while BERT outperforms traditional methods, the choice of validation technique can impact model performance. This underscores the importance of contextual understanding in detecting nuanced hate speech. By releasing the largest open-source labelled English dataset for homophobia detection known to us, an analysis of various models' performance and our strongest BERT-based model, we aim to enhance online safety and inclusivity. Future work will extend to broader LGBTQIA+ hate speech detection, addressing the challenges of sourcing diverse datasets. Through this endeavour, we contribute to the larger effort against online hate, advocating for a more inclusive digital landscape. Our study not only offers insights into the effective detection of homophobic content by improving on previous research results, but it also lays groundwork for future advancements in hate speech analysis.\",\"PeriodicalId\":350976,\"journal\":{\"name\":\"Applied and Computational Engineering\",\"volume\":\"54 8\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-05-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied and Computational Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.54254/2755-2721/64/20241346\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied and Computational Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.54254/2755-2721/64/20241346","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bridging the gap in online hate speech detection: A comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter
Our study addresses a significant gap in online hate speech detection research by focusing on homophobia, an area often neglected in sentiment analysis research. Utilising advanced sentiment analysis models, particularly BERT, and traditional machine learning methods, we developed a nuanced approach to identify homophobic content on X/Twitter. This research is pivotal due to the persistent underrepresentation of homophobia in detection models. Our findings reveal that while BERT outperforms traditional methods, the choice of validation technique can impact model performance. This underscores the importance of contextual understanding in detecting nuanced hate speech. By releasing the largest open-source labelled English dataset for homophobia detection known to us, an analysis of various models' performance and our strongest BERT-based model, we aim to enhance online safety and inclusivity. Future work will extend to broader LGBTQIA+ hate speech detection, addressing the challenges of sourcing diverse datasets. Through this endeavour, we contribute to the larger effort against online hate, advocating for a more inclusive digital landscape. Our study not only offers insights into the effective detection of homophobic content by improving on previous research results, but it also lays groundwork for future advancements in hate speech analysis.