Analysis of Style-Shifting on Social Media: Using Neural Language Model Conditioned by Social Meanings
Seiya Kawano, Shota Kanezaki, Angel Fernando Garcia Contreras, Akishige Yuguchi, Marie Katsurai, Koichiro Yoshino
Abstract
In this paper, we propose a novel framework for evaluating style-shifting in social media conversations. Our proposed framework captures changes in an individual’s conversational style based on surprisals predicted by a personalized neural language model for individuals. Our personalized language model integrates not only the linguistic contents of conversations but also non-linguistic factors, such as social meanings, including group membership, personal attributes, and individual beliefs. We incorporate these factors directly or implicitly into our model, leveraging large, pre-trained language models and feature vectors derived from a relationship graph on social media. Compared to existing models, our personalized language model demonstrated superior performance in predicting an individual’s language in a test set. Furthermore, an analysis of style-shifting utilizing our proposed metric based on our personalized neural language model reveals a correlation between our metric and various conversation factors as well as human evaluation of style-shifting.- Anthology ID:
- 2023.findings-emnlp.531
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2023
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 7911–7921
- Language:
- URL:
- https://aclanthology.org/2023.findings-emnlp.531
- DOI:
- 10.18653/v1/2023.findings-emnlp.531
- Cite (ACL):
- Seiya Kawano, Shota Kanezaki, Angel Fernando Garcia Contreras, Akishige Yuguchi, Marie Katsurai, and Koichiro Yoshino. 2023. Analysis of Style-Shifting on Social Media: Using Neural Language Model Conditioned by Social Meanings. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7911–7921, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Analysis of Style-Shifting on Social Media: Using Neural Language Model Conditioned by Social Meanings (Kawano et al., Findings 2023)
- PDF:
- https://preview.aclanthology.org/proper-vol2-ingestion/2023.findings-emnlp.531.pdf