Automatic personality assessment through social media language.

Abstract

Language use is a psychologically rich, stable individual difference with well-established correlations to personality. We describe a method for assessing personality using an open-vocabulary analysis of language from social media. We compiled the written language from 66,732 Facebook users and their questionnaire-based self-reported Big Five personality traits, and then we built a predictive model of personality based on their language. We used this model to predict the 5 personality factors in a separate sample of 4,824 Facebook users, examining (a) convergence with self-reports of personality at the domain- and facet-level; (b) discriminant validity between predictions of distinct traits; (c) agreement with informant reports of personality; (d) patterns of correlations with external criteria (e.g., number of friends, political attitudes, impulsiveness); and (e) test-retest reliability over 6-month intervals. Results indicated that language-based assessments can constitute valid personality measures: they agreed with self-reports and informant reports of personality, added incremental validity over informant reports, adequately discriminated between traits, exhibited patterns of correlations with external criteria similar to those found with self-reported personality, and were stable over 6-month intervals. Analysis of predictive language can provide rich portraits of the mental life associated with traits. This approach can complement and extend traditional methods, providing researchers with an additional measure that can quickly and cheaply assess large groups of participants with minimal burden.

CEO Personality and Firm Policies

“…This research finds that language is not only predictive of personality (e.g., Mairesse et al, 2007, Schwartz et al, 2013, Park et al, 2015, but that language-based personality scores are also stable over time (Park et al, 2015). Our findings are consistent with these studies.…”

“…Similar to this paper, a number of recent studies (e.g., Mairesse et al, 2007, Schwartz et al, 2013, Park et al, 2015 used linguistic features to predict continuous Big Five scores in different contexts. Schwartz et al (2013) analyzed Facebook messages of 75,000 volunteers to predict their Big Five traits.…”

Estimating affective word covariates using word association data

“…The extrapolation methods we describe would conceivably work on other psychologically relevant dimensions as well, as long as these dimensions are captured by the associative technique, that is, as long as the associations people give to a certain word are in some way related to the cue's or association's value on that dimension. Existing research suggests that other examples of word covariates that could likely be predicted based on association data may include concreteness (the extent to which words refer to something perceptible; see Mandera et al, 2015, or Van Rensbergen, Storms, & De Deyne, 2015, age of acquisition (the age at which a word was learned; see Mandera et al, 2015), or dimensions relevant to personality profiles (e.g., openness, conscientiousness, extraversion, agreeableness, or neuroticism; see Yarkoni, 2010, or Park et al, 2015.…”

Extracting Human Temporal Orientation from Facebook Language

“…Toward attribute prediction, a large proportion of works have focused on demographics (Argamon et al, 2009;Goswami et al, 2009;Burger et al, 2011;Al Zamal et al, 2012;Bergsma et al, 2013;Sap et al, 2014). and personality prediction (Mairesse et al, 2007;Iacobelli et al, 2011;Schwartz et al, 2013;Park et al, 2015).…”