ArticleJournal of personality2021
Differential ability of network and natural language information on social media to predict interpersonal and mental health traits.
Article in Journal of personality, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 2 of them syntheses that pooled it.
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.
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Who cites it
9 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Methodologies for Monitoring Mental Health on Twitter: Systematic Review.Journal of medical Internet research · 2023Pooled it
- Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.Journal of medical Internet research · 2022Pooled it
- A scoping review of social media in child, adolescents and young adults: research findings in depression, anxiety and other clinical challenges.BJPsych open · 2023Article
- Relationship between university students' emotional expression on tweets and subjective well-being: Considering the effects of their self-presentation and online communication skills.BMC public health · 2023Article
- Natural Language Content Mediates the Association Between Active Interactions on Social Network Services and Subjective Well-Being.Cyberpsychology, behavior and social networking · 2022Article
- Predicting verbal reasoning from virtual community membership in a sample of Russian young adults.Heliyon · 2022Article
- An hybrid deep learning approach for depression prediction from user tweets using feature-rich CNN and bi-directional LSTM.Multimedia tools and applications · 2022Article
- Differential ability of network and natural language information on social media to predict interpersonal and mental health traits.Journal of personality · 2021Article
- Decoding anxiety: A scoping review of observable cues.Digital healthArticle
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivePrevious studies have shown that digital footprints (mainly Social Networking Services, or SNS) can predict personality traits centered on the Big Five. The present study investigates to what extent different types of SNS information predicts wider traits and attributes.
methodWe collected an intensive set of 24 (52 subscales) personality traits and attributes (N = 239) and examined whether machine learning models trained on four different types of SNS (i.e., Twitter) information (network, time, word statistics, and bag of words) predict the traits and attributes.
resultsWe found that four types of SNS information can predict 23 subscales collectively. Furthermore, we validated our hypothesis that the network and word statistics information, respectively, exhibit unique strengths for the prediction of inter-personal traits such as autism and mental health traits such as schizophrenia and anxiety. We also found that intelligence is predicted by all four types of SNS information.
conclusionsThese results reveal that the different types of SNS information can collectivity predict wider human traits and attributes than previously recognized, and also that each information type has unique predictive strengths for specific traits and attributes, suggesting that personality prediction from SNS is a powerful tool for both personality psychology and information technology.
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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.