ArticleFrontiers in psychology2022
Mining Digital Traces of Facebook Activity for the Prediction of Individual Differences in Tendencies Toward Social Networks Use Disorder: A Machine Learning Approach.
Article in Frontiers in psychology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
5 citing papers in PubMed.
- Inferring Personality From Social Media Activity Using Large Language Models: Cross-Model Agreement, Temporal Stability, and Convergent Validity With Self-Reports.Journal of personality · 2026Article
- Developing SHAP interpretable machine learning models for assessing biopsychosocial risk in female drug users: a small sample study.Frontiers in psychiatry · 2026Article
- The role of artificial intelligence in general, and large language models specifically, for understanding addictive behaviors.Annals of the New York Academy of Sciences · 2025Review
- Article
- Dataset on individual differences in self-reported personality and inferred emotional expression in profile pictures of Italian Facebook users.Data in brief · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
More than three billion users are currently on one of Meta's online platforms with Facebook being still their most prominent social media service. It is well known that Facebook has designed a highly immersive social media service with the aim to prolong online time of its users, as this results in more digital footprints to be studied and monetized (
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.