You Are What You Read: Inferring Personality From Consumed Textual Content
Adam Sutton, Almog Simchon, Matthew Edwards, Stephan Lewandowsky
Abstract
In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our model on two datasets, sampled from participants who consumed either fiction content (N = 913) or news content (N = 213). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of r=.06 between the model’s predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as r=.34 when predicting personality from the text being read.- Anthology ID:
- 2023.wassa-1.4
- Volume:
- Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Jeremy Barnes, Orphée De Clercq, Roman Klinger
- Venue:
- WASSA
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 28–38
- Language:
- URL:
- https://aclanthology.org/2023.wassa-1.4
- DOI:
- 10.18653/v1/2023.wassa-1.4
- Cite (ACL):
- Adam Sutton, Almog Simchon, Matthew Edwards, and Stephan Lewandowsky. 2023. You Are What You Read: Inferring Personality From Consumed Textual Content. In Proceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis, pages 28–38, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- You Are What You Read: Inferring Personality From Consumed Textual Content (Sutton et al., WASSA 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.wassa-1.4.pdf