PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 31, 2024PNAS Nexus83 citationsOpen Access

Large language models can infer psychological dispositions of social media users

View Full Paper
HPHeinrich PetersSMSandra Matz

Key Points

Key points are not available for this paper at this time.

Abstract

Large language models (LLMs) demonstrate increasingly human-like abilities across a wide variety of tasks. In this paper, we investigate whether LLMs like ChatGPT can accurately infer the psychological dispositions of social media users and whether their ability to do so varies across socio-demographic groups. Specifically, we test whether GPT-3.5 and GPT-4 can derive the Big Five personality traits from users' Facebook status updates in a zero-shot learning scenario. Our results show an average correlation of

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Peters et al. (2024) studied this question.

synapsesocial.com/papers/68e67617b6db6435875fff4ehttps://doi.org/10.1093/pnasnexus/pgae231
Ask AI
Helpful
Bookmark
Share
View Full Paper

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Large Language Models Can Infer Personality from Free-Form User Interactions2024 · 4 citations
  2. 2Large Language Models Can Infer Personality from Free-Form User Interactions2024 · 13 citations
  3. 3Artificial Intelligence and Personality: Large Language Models’ Ability to Predict Personality Type2024 · 8 citations
  4. 4Inferring Personality From Social Media Activity Using Large Language Models: Cross‐Model Agreement, Temporal Stability, and Convergent Validity With Self‐Reports2025
  5. 5Can large language models help predict results from a complex behavioural science study?2024 · 17 citations