Many populous countries including India are burdened with a considerable backlog of legal cases. Development of automated systems that could process legal documents and augment legal practitioners can mitigate this. However, there is a dearth of high-quality corpora that is needed to develop such data-driven systems. The problem gets even more pronounced in the case of low resource languages such as Hindi. In this resource paper, we introduce the Hindi Legal Documents Corpus (HLDC), a corpus of more than 900K legal documents in Hindi. Documents are cleaned and structured to enable the development of downstream applications. Further, as a use-case for the corpus, we introduce the task of bail prediction. We experiment with a battery of models and propose a Multi-Task Learning (MTL) based model for the same. MTL models use summarization as an auxiliary task along with bail prediction as the main task. Experiments with different models are indicative of the need for further research in this area.
In recent years, social media, such as Twitter, has become a central point for organizing and developing online protests worldwide. Although protest and mass mobilization are scarce, they may lead to dramatic developments when they occur. Different narratives shared during a protest serve as strategic tools for building and advancing collective opinions. Hence, it becomes crucial to decipher various narratives shared during an online protest. In this work, we aim to investigate the shared narratives and examine the evolution and communication around the narratives during the protest. To this end, we propose an unsupervised clustering-based framework to understand the various narratives in an online protest. We contribute novel insights about narratives shared during an online protest by analyzing 4 protests under study. Next, we investigate the evolution of identified converging narratives across the protests. We further identify the most influential participants in a protest and study their contribution to spreading various narratives. Our results suggest that clusters with call-to-action tweets and on-ground activity reporting tweets are common narratives across all protests. The analysis of the evolution of narrative suggests that the call-to-action narrative is the most consistent during the protest. The community detection over the retweet network across protests suggests narrative-centric community formation. Our research on narrative analysis during a protest can help provide crucial intelligence and situational awareness to make informed decisions.
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