Automated Detection of Tropes In Short Texts

Abstract

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.