The detection of hate speech in political discourse is a critical issue, and this becomes even more challenging in low-resource languages. To address this issue, we introduce a new dataset named IEHate, which contains 11,457 manually annotated Hindi tweets related to the Indian Assembly Election Campaign from November 1, 2021, to March 9, 2022. We performed a detailed analysis of the dataset, focusing on the prevalence of hate speech in political communication and the different forms of hateful language used. Additionally, we benchmark the dataset using a range of machine learning, deep learning, and transformer-based algorithms. Our experiments reveal that the performance of these models can be further improved, highlighting the need for more advanced techniques for hate speech detection in low-resource languages. In particular, the relatively higher score of human evaluation over algorithms emphasizes the importance of utilizing both human and automated approaches for effective hate speech moderation. Our IEHate dataset can serve as a valuable resource for researchers and practitioners working on developing and evaluating hate speech detection techniques in low-resource languages. Overall, our work underscores the importance of addressing the challenges of identifying and mitigating hate speech in political discourse, particularly in the context of low-resource languages. The dataset and resources for this work are made available at https://github.com/Farhan-jafri/Indian-Election.

翻译：政治话语中的仇恨言辞检测是一项关键挑战，尤其在低资源语言中更为棘手。为解决此问题，我们提出了名为IEHate的新数据集，包含11,457条手工标注的印地语推文，时间范围为2021年11月1日至2022年3月9日，涉及印度议会选举活动。我们对数据集进行了详细分析，重点关注政治沟通中仇恨言论的普遍性及其不同形式。此外，我们采用机器学习、深度学习及基于Transformer的多种算法对数据集进行了基准测试。实验结果表明，这些模型的性能仍有提升空间，凸显了低资源语言中仇恨言论检测更先进技术的需求。特别地，人工评估的相对较高评分强调了融合人工与自动化方法进行有效仇恨言论审核的重要性。IEHate数据集可为致力于低资源语言仇恨言论检测技术开发与评估的研究人员和实践者提供宝贵资源。总体而言，本研究强调了应对政治话语中仇恨言论识别与缓解挑战的重要意义，尤其在低资源语言背景下。本工作的数据集与资源已公开于https://github.com/Farhan-jafri/Indian-Election。