@inproceedings{ca6df6887a474109a8e61d6fa8a95588,
title = "You Are What You Read: Inferring Personality From Consumed Textual Content",
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 models 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{\textquoteright}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.",
author = "Adam Sutton and Almog Simchon and Matthew Edwards and Stephan Lewandowsky",
note = "Publisher Copyright: {\textcopyright} 2023 Association for Computational Linguistics.; 13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2023 ; Conference date: 14-07-2023",
year = "2023",
month = jan,
day = "1",
doi = "10.18653/v1/2023.wassa-1.4",
language = "English",
series = "Proceedings of the Annual Meeting of the Association for Computational Linguistics",
publisher = "Association for Computational Linguistics (ACL)",
pages = "28--38",
editor = "Jeremy Barnes and {De Clercq}, Orphee and Roman Klinger",
booktitle = "WASSA 2023 - 13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, Proceedings of the Workshop",
address = "United States",
}