COLING 2025, 31st International Conference on Computational Linguistics, 19-24 January 2025, Abu Dhabi, UAE
Tropes — recurring narrative elements like the "smoking gun" or the "veil of secrecy"—are often used in movies to convey familiar patterns. However, they also play a significant role in online communication about societal issues, where they can oversimplify complex matters and deteriorate public discourse. Recognizing these tropes can offer insights into the emotional manipulation and potential bias present in online discussions. This paper addresses the challenge of automatically detecting tropes in social media posts. We define the task, distinguish it from previous work, and create a ground-truth dataset of social media posts related to vaccines and immigration, manually labeled with tropes. Using this dataset, we develop a supervised machine learning technique for multi-label classification, fine-tune a model, and demonstrate its effectiveness experimentally. Our results show that tropes are common across domains and that fine-tuned models can detect them with high accuracy.
Type:
Conference
City:
Abu Dhabi
Date:
2025-01-19
Department:
Data Science
Eurecom Ref:
8028
Copyright:
Copyright ACL. Personal use of this material is permitted. The definitive version of this paper was published in COLING 2025, 31st International Conference on Computational Linguistics, 19-24 January 2025, Abu Dhabi, UAE and is available at :
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