CHUNAV: Analyzing Hindi Hate Speech and Targeted Groups in Indian Election Discourse

Abstract

In the ever-evolving landscape of online discourse and political dialogue, the rise of hate speech poses a signiicant challenge to maintaining a respectful and inclusive digital environment. he context becomes particularly complex when considering the Hindi language—a low-resource language with limited available data. To address this pressing concern, we introduce the CHUNAV dataset—a collection of 11,457 Hindi tweets gathered during assembly elections in various states. CHUNAV is purpose-built for hate speech categorization and the identiication of target groups. he dataset is a valuable resource for exploring hate speech within the distinctive socio-political context of Indian elections. he tweets within CHUNAV have been meticulously categorized into “Hate” and “Non-Hate” labels, and further subdivided to pinpoint the speciic targets of hate speech, including “Individual”, “Organization”, and “Community” labels (as shown in Figure 1). Furthermore, this paper presents multiple benchmark models for hate speech detection, along with an innovative ensemble and oversampling-based method. he paper also delves into the results of topic modeling, all aimed at efectively addressing hate speech and target identiication in the Hindi language. his contribution seeks to advance the ield of hate speech analysis and foster a safer and more inclusive online space within the distinctive realm of Indian Assembly Elections.