Automatic personality assessment through social media language.

By Gregory J. Park, H. A. Schwartz, Johannes C. Eichstaedt, Margaret L. Kern, Michal Kosinski, David J. Stillwell, Lyle Ungar, Martin E. P. Seligman. 2015

763 citations recorded by the source corpus.

Subfields: Clinical Psychology, Social Psychology, Natural Language Processing

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Citations in the corpus

Ethical and social risks of harm from Language Models (2021, background): “Leveraging language processing tools and large public datasets to infer private traits is an active area of research (Kosinski et al., 2013; Park et al., 2015; Quercia et al., 2011; Youyou et al., 2015).”
Taxonomy of Risks posed by Language Models (2022, background): “Leveraging language processing tools and large public datasets to infer protected and other personal traits is an active area of research [107, 140, 146, 200], despite serious ethical concerns [6, 185].”
The role of personality, age, and gender in tweeting about mental illness (2015, methodology): “The model was trained on a large sample of around 70,000 Facebook users who have taken Big Five personality tests and shared their posts using a model using 1-3 grams and topics as features (Park et al., 2014; Schwartz et al., 2013).”
Extracting Human Temporal Orientation from Facebook Language (2015, background): “Others, such as the bottom three examples, might reference multiple temporal classes or not include clear verb tense and thus rely on the raters’ judgements for what is most dominant.”
Returning the N to NLP: Towards Contextually Personalized Classification Models (2020, background): “, 2014), and a range of psychological traits and issues (Schwartz et al., 2013; Park et al., 2015; Sumner et al., 2012; Guntuku et al., 2017; Coppersmith et al., 2014).”
Text Preprocessing for Text Mining in Organizational Research: Review and Recommendations (2020, methodology): “…that text mining is used in social psychology is to infer traits and trait-like individual differences from text, such as social media posts (e.g., Park et al., 2015), which has been used to distinguish the average personality traits of workers in a given occupation (Kern et al., 2019) and to…”
Multitask Learning for Mental Health Conditions with Limited Social Media Data (2017, background): “…et al., 2014), gender (Sarawgi et al., 2011; Ciot et al., 2013; Liu and Ruths, 2013; Volkova et al., 2015; Hovy, 2015), personality (Schwartz et al., 2013; Volkova et al., 2014; Plank and Hovy, 2015; Park et al., 2015; Preot¸iuc-Pietro et al., 2015), and occupation (Preotiuc-Pietro et al., 2015).”
Big Data and Data Science Methods for Management Research (2016, background): “As another example, researchers have begun to analyze large samples of language (e.g., individuals’ posts on social media) as a non-obtrusive way to assess personality (Park et al., 2015).”
The potential of generative AI for personalized persuasion at scale (2024, background): “For example, while some psychological traits produced consistent and relatively pronounced matching effects (e.g., Openness and Extra-version), others failed to produce robust effects or reach statistical levels of significance (e.g., Agreeableness).”
Using a Personality-Profiling Algorithm to Investigate Political Microtargeting: Assessing the Persuasion Effects of Personality-Tailored Ads on Social Media (2020, neutral): “Park et al. (2015) tested this computerized personality profiling against a self-administered personality assessment, and found this technique to be a valid way of personality recognition.”
Third wave positive psychology: broadening towards complexity (2020, background): “…of power differentials on community engagement (Danescu-Niculescu-Mizil et al., 2013); personal values (Boyd et al., 2015); emotions (Bollen et al., 2011; Strapparava & Mihalcea, 2008); and psychological traits (Guntuku et al., 2017; Mitchell et al., 2015; Park et al., 2015; Sagi & Dehghani, 2014).”
Does ‘well-being’ translate on Twitter? (2016, neutral): “(Mohammad and Kiritchenko, 2015; Park et al., 2014).”
Personal Sensing: Understanding Mental Health Using Ubiquitous Sensors and Machine Learning (2017, background): “…geographical, demographic, and seasonal patterns reported by the US Centers for Disease Control and Prevention (CDC) (De Choudhury et al. For example, language features from Facebook posts have shown modest but consistent correlations correlations with Big 5 personality factors (Park et al. 2015).”