You Are What You Read: Inferring Personality From Consumed Textual Content

Adam Sutton, Almog Simchon, Matthew Edwards, Stephan Lewandowsky

Research output: Chapter in Book/Conference paperConference paperpeer-review

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’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.

Original languageEnglish
Title of host publicationProceedings of the 13th Workshop on Computational Approaches to Subjectivity, Sentiment, & Social Media Analysis
EditorsJeremy Barnes, Orphee De Clercq, Roman Klinger
PublisherAssociation for Computational Linguistics (ACL)
Pages28-38
Number of pages11
ISBN (Electronic)9781959429876
DOIs
Publication statusPublished - 2023
Externally publishedYes
Event13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2023 - Toronto, Canada
Duration: 14 Jul 202314 Jul 2023

Publication series

NameProceedings of the Annual Meeting of the Association for Computational Linguistics
ISSN (Print)0736-587X

Conference

Conference13th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis, WASSA 2023
Country/TerritoryCanada
CityToronto
Period14/07/2314/07/23

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