Thapa, Surendrabikram, Rauniyar, Kritesh, Shiwakoti, Shuvam, Poudel, Sweta, Naseem, Usman, and Nasim, Mehwish (2023) NEHATE: Large-Scale Annotated Data Shedding Light on Hate Speech in Nepali Local Election Discourse. In: Frontiers in Artificial Intelligence and Applications (372) pp. 2346-2353. From: ECAI 2020: 26th European Conference on Artificial Intelligence, 30 September - 4 October 2023, Kraków, Poland.

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Abstract

The use of social media during election campaigns has become increasingly popular. However, the unbridled nature of online discourse can lead to the propagation of hate speech, which has far-reaching implications for the democratic process. Natural Language Processing (NLP) techniques are being used to counteract the spread of hate speech and promote healthy online discourse. Despite the increasing need for NLP techniques to combat hate speech, research on low-resource languages such as Nepali is limited, posing a challenge to the realization of the United Nations' Leave No One Behind principle, which calls for inclusive development that benefits all individuals and communities, regardless of their backgrounds or circumstances. To bridge this gap, we introduce NEHATE, a large-scale manually annotated dataset of hate speech and its targets in Nepali local election discourse. The dataset comprises 13,505 tweets, annotated for hate speech with further sub-categorization of hate speech into targets such as community, individual, and organization. Benchmarking of the dataset with various algorithms has shown potential for performance improvement. We have made the dataset publicly available at https://github.com/shucoll/NEHate to promote further research and development, while also contributing to the UN SDGs aimed at fostering peaceful, inclusive societies, and justice and strong institutions.

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