Online reading habits can reveal personality traits: Towards detecting psychological microtargeting

Almog Simchon*, Adam J Sutton, Matthew Edwards, Stephan Lewandowsky

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

2 Citations (Scopus)

Abstract

Building on big data from Reddit, we generated two computational text models: (1) Predicting the personality of users from the text they have written and (2) predicting the personality of users based on the text they have consumed. The second model is novel and without precedent in the literature. We recruited active Reddit users (N = 1,105) of fiction-writing communities. The participants completed a Big Five personality questionnaire, and consented for their Reddit activity to be scraped and used to create a machine-learning model. We trained an NLP model (BERT), predicting personality from produced text (average performance: r = 0.33). We then applied this model to a new set of Reddit users (N = 10,050), predicted their personality based on their produced text, and trained a second BERT model to predict their predicted-personality scores based on consumed text (average performance: r = 0.13). By doing so, we provide the first glimpse into the linguistic markers of personality-congruent consumed content.
Original languageEnglish
Article numberpgad191
JournalPNAS Nexus
Volume2
Issue number6
Early online date7 Jun 2023
DOIs
Publication statusPublished - 17 Jun 2023

Bibliographical note

Funding Information:
This research was supported by a large grant from the Volkswagen Foundation (“Reclaiming individual autonomy and democratic discourse online”). S.L. was also supported by funding from the Humboldt Foundation in Germany and by an Advanced Grant (PRODEMINFO). A.Su. was also supported by funding by REPHRAIN: National Research Centre on Privacy, Harm Reduction and Adversarial Influence online (EPSRC Grant: EP/V011189/1).

Funding Information:
The authors thank Danae Ioakeimidou and the anonymous reviewers for their valuable suggestions. This manuscript was posted on a preprint: https://osf.io/wdrus. This research was supported by a large grant from the Volkswagen Foundation (“Reclaiming individual autonomy and democratic discourse online”). S.L. was also supported by funding from the Humboldt Foundation in Germany and by an Advanced Grant (PRODEMINFO). A.Su. was also supported by funding by REPHRAIN: National Research Centre on Privacy, Harm Reduction and Adversarial Influence online (EPSRC Grant: EP/V011189/1).

Publisher Copyright:
© The Author(s) 2023. Published by Oxford University Press on behalf of National Academy of Sciences. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

Structured keywords

  • Cyber Security
  • Cognitive Science
  • TeDCog

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