TITLE: Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines 

ABSTRACT: 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.
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.
Uncovering Political Hate Speech During Indian Election Campaign: A New Low-Resource Dataset and Baselines
http://arxiv.org/abs/2306.14764v2