Papers by Kritesh Rauniyar
POLAR: A Benchmark for Multilingual, Multicultural, and Multi-Event Online Polarization (2026.findings-acl)
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Usman Naseem, Robert Geislinger, Juan Ren, Sarah Kohail, Rudy Alexandro Garrido Veliz, P Sam Sahil, Yiran Zhang, Idris Abdulmumin, Marco Antonio Stranisci, Özge Alacam, Cengiz Acarturk, Aisha Jabr, Saba Anwar, Abinew Ali Ayele, Simona Frenda, Alessandra Teresa Cignarella, Elena Tutubalina, Oleg Rogov, Aung Kyaw Htet, Xintong Wang, Surendrabikram Thapa, Kritesh Rauniyar, Tanmoy Chakraborty, MD Arfeen Zeeshan, Dheeraj Kodati, Satya Keerthi, Sahar Moradizeyveh, Firoj Alam, Md Arid Hasan, Syed Ishtiaque Ahmed, Ye Kyaw Thu, Shantipriya Parida, Ihsan Ayyub Qazi, Lilian Diana Awuor Wanzare, Nelson Odhiambo Onyango, Clemencia Siro, Jane Wanjiru Kimani, Ibrahim Said Ahmad, Adem Chanie Ali, Martin Semmann, Chris Biemann, Shamsuddeen Hassan Muhammad, Seid Muhie Yimam
| Challenge: | polarization is a pervasive threat to democratic institutions, civil discourse, and social cohesion worldwide . most existing datasets focus on English or high-resource languages, reflecting a widespread trend across NLP tasks . |
| Approach: | They propose a multilingual, multicultural, and multi-event dataset with over 110K instances in 22 languages drawn from diverse online platforms and real-world events. |
| Outcome: | The proposed dataset analyzes polarization detection, type, and manifestation using a variety of annotation platforms adapted to each cultural context. |
Analyzing the Dynamics of Climate Change Discourse on Twitter: A New Annotated Corpus and Multi-Aspect Classification (2024.lrec-main)
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Shuvam Shiwakoti, Surendrabikram Thapa, Kritesh Rauniyar, Akshyat Shah, Aashish Bhandari, Usman Naseem
| Challenge: | a lack of data on climate change discourse has highlighted the need for further advancement . a new study examines the discourse on social media platforms that ignores climate change . |
| Approach: | They analyze climate change discourse on Twitter using a meticulously annotated dataset . they find relevance, stance, hate speech, direction of hate, humor and humor are key aspects . |
| Outcome: | The proposed method combines annotated tweets with a dataset of 15,309 tweets . it reveals tweet distribution patterns, stance prevalence, and hate speech trends . |