The purpose of this paper is to ascertain the influence of sociocultural factors (i.e., social, cultural, and political) in the development of hate speech detection systems. We set out to investigate the suitability of using open-source training data to monitor levels of anti-LGBTQ+ content on social media across different national-varieties of English. Our findings suggests the social and cultural alignment of open-source hate speech data sets influences the predicted outputs. Furthermore, the keyword-search approach of anti-LGBTQ+ slurs in the development of open-source training data encourages detection models to overfit on slurs; therefore, anti-LGBTQ+ content may go undetected. We recommend combining empirical outputs with qualitative insights to ensure these systems are fit for purpose.

检测系统发现，社会文化因素对仇恨言论的发展产生影响。采用开源训练数据在不同的英语国别社交媒体上监测反LGBTQ+内容的合适性进行了调查，结果显示开源仇恨言论数据集的社会和文化一致性影响了预测结果。此外，开源训练数据中针对反LGBTQ+辱骂的关键字搜索方法会导致模型过度拟合，从而可能未能检测到反LGBTQ+内容。我们建议结合经验输出和定性洞察，以确保这些系统具有合适的功能。

社会文化因素对社交媒体上反LGBTQ+内容的监控