弥合在线仇恨言论检测的差距：针对X/Twitter上恐同内容识别的BERT与传统模型对比分析
Bridging the gap in online hate speech detection: a comparative analysis of BERT and traditional models for homophobic content identification on X/Twitter
AI总结：
本研究针对在线仇恨言论检测中恐同内容研究不足的问题，对比BERT与传统模型在X/Twitter恐同内容识别的性能，发布最大规模相关开源英文数据集，为提升在线包容性及后续仇恨言论分析研究奠定基础。
摘要：
本研究聚焦于情感分析研究中常被忽视的恐同领域，填补了在线仇恨言论检测研究的一项重要空白。我们运用先进的情感分析模型（尤其是BERT）以及传统机器学习方法，提出了一种精细化方法来识别X/Twitter上的恐同内容。由于检测模型中长期存在恐同内容代表性不足的问题，本研究具有关键意义。我们的研究结果显示，尽管BERT的表现优于传统方法，但验证技术的选择会影响模型性能，这凸显了语境理解在检测细微仇恨言论中的重要性。我们发布了据我们所知规模最大的用于恐同检测的开源标注英文数据集，结合对各类模型性能的分析以及我们性能最优的基于BERT的模型，旨在提升在线安全性与包容性。未来工作将拓展至更广泛的LGBTQIA+仇恨言论检测，应对获取多样化数据集的挑战。通过这项工作，我们为打击在线仇恨的更广泛行动贡献力量，倡导构建更具包容性的数字环境。本研究不仅通过改进既往研究结果，为恐同内容的有效检测提供了洞见，还为仇恨言论分析的未来发展奠定了基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
