Papers by Sharath Chandra Guntuku
Building Knowledge-Guided Lexica to Model Cultural Variation (2024.naacl-long)
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| Challenge: | Cultural variation exists between nations, but also within regions . Historically, it has been difficult to computationally model cultural variation due to a lack of training data and scalability constraints. |
| Approach: | They propose a method to measure cultural variation using a knowledge-guided lexical model using geolocated tweets. |
| Outcome: | The proposed method could help us better understand the way people communicate and build more culturally-aware NLP systems. |
Did that happen? Predicting Social Media Posts that are Indicative of what happened in a scene: A case study of a TV show (2022.lrec-1)
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| Challenge: | Prior work identified and summarized scenes associated with a TV show by selecting a few representative social media posts (5 posts) that were published during the timeline of the scenes. |
| Approach: | They propose a method to predict social media posts associated with a TV show from those that are not-indicative. |
| Outcome: | The proposed method can predict posts indicative of what happened in a scene from those that are not-indicative based on high AUC's on social media posts associated with a popular TV show . |
Social Norms in Cinema: A Cross-Cultural Analysis of Shame, Pride and Prejudice (2025.naacl-long)
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| Challenge: | We examine *how* and *why* shame and pride are expressed across cultures using a blend of psychology-informed language analysis combined with large language models. |
| Approach: | They introduce a cross-cultural dataset of over 10k shame/pride-related expressions with underlying social expectations from 5.4K Bollywood and Hollywood movies. |
| Outcome: | The results show that women are more sanctioned across cultures and for violating similar social expectations. |
Language-based Valence and Arousal Expressions between the United States and China: a Cross-Cultural Examination (2025.findings-naacl)
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Young Min Cho, Dandan Pang, Stuti Thapa, Garrick Sherman, Lyle Ungar, Louis Tay, Sharath Chandra Guntuku
| Challenge: | valence and arousal are functionally equivalent across social media platforms . americans display higher emotional intensity than Chinese users . |
| Approach: | They compare valence and arousal on Twitter/X and Sina Weibo in China . they use the NRC-VAD lexicon to measure valance and valency . |
| Outcome: | The results show that the valence and arousal of the two platforms differ across cultures . the analysis also shows that the US users display higher emotional intensity than Chinese users . |