Personality in Its Natural Habitat Mehl, Matthias R; Gosling, Samuel D; Pennebaker, James W
Journal of personality and social psychology,
05/2006, Volume:
90, Issue:
5
Journal Article
Peer reviewed
To examine the expression of personality in its natural habitat, the authors tracked 96 participants over 2 days using the Electronically Activated Recorder (EAR), which samples snippets of ambient ...sounds in participants' immediate environments. Participants' Big Five scores were correlated with EAR-derived information on their daily social interactions, locations, activities, moods, and language use; these quotidian manifestations were generally consistent with the trait definitions and (except for Openness) often gender specific. To identify implicit folk theories about daily manifestations of personality, the authors correlated the EAR-derived information with impressions of participants based on their EAR sounds; judges' implicit folk theories were generally accurate (especially for Extraversion) and also partially gender specific. The findings point to the importance of naturalistic observation studies on how personality is expressed and perceived in the natural stream of everyday behavior.
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12.
Language Style Matching in Writing Ireland, Molly E; Pennebaker, James W
Journal of personality and social psychology,
09/2010, Volume:
99, Issue:
3
Journal Article
Peer reviewed
Each relationship has its own personality. Almost immediately after a social interaction begins, verbal and nonverbal behaviors become synchronized. Even in asocial contexts, individuals tend to ...produce utterances that match the grammatical structure of sentences they have recently heard or read. Three projects explore language style matching (LSM) in everyday writing tasks and professional writing. LSM is the relative use of 9 function word categories (e.g., articles, personal pronouns) between any 2 texts. In the first project, 2 samples totaling 1,744 college students answered 4 essay questions written in very different styles. Students automatically matched the language style of the target questions. Overall, the LSM metric was internally consistent and reliable across writing tasks. Women, participants of higher socioeconomic status, and students who earned higher test grades matched with targets more than others did. In the second project, 74 participants completed cliffhanger excerpts from popular fiction. Judges' ratings of excerpt-response similarity were related to content matching but not function word matching, as indexed by LSM. Further, participants were not able to intentionally increase style or content matching. In the final project, an archival study tracked the professional writing and personal correspondence of 3 pairs of famous writers across their relationships. Language matching in poetry and letters reflected fluctuations in the relationships of 3 couples: Sigmund Freud and Carl Jung, Elizabeth Barrett and Robert Browning, and Sylvia Plath and Ted Hughes. Implications for using LSM as an implicit marker of social engagement and influence are discussed.
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The mental health of college students is a growing concern, and gauging the mental health needs of college students is difficult to assess in real-time and in scale. To address this gap, researchers ...and practitioners have encouraged the use of passive technologies. Social media is one such "passive sensor" that has shown potential as a viable "passive sensor" of mental health. However, the construct validity and in-practice reliability of computational assessments of mental health constructs with social media data remain largely unexplored. Towards this goal, we study how assessing the mental health of college students using social media data correspond with ground-truth data of on-campus mental health consultations. For a large U.S. public university, we obtained ground-truth data of on-campus mental health consultations between 2011-2016, and collected 66,000 posts from the university's Reddit community. We adopted machine learning and natural language methodologies to measure symptomatic mental health expressions of depression, anxiety, stress, suicidal ideation, and psychosis on the social media data. Seasonal auto-regressive integrated moving average (SARIMA) models of forecasting on-campus mental health consultations showed that incorporating social media data led to predictions with r = 0.86 and SMAPE = 13.30, outperforming models without social media data by 41%. Our language analyses revealed that social media discussions during high mental health consultations months consisted of discussions on academics and career, whereas months of low mental health consultations saliently show expressions of positive affect, collective identity, and socialization. This study reveals that social media data can improve our understanding of college students' mental health, particularly their mental health treatment needs.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
College students' study strategies were explored by tracking the ways they navigated the websites of two large (Ns of 1384 and 671) online introductory psychology courses. Students' study patterns ...were measured analyzing the ways they clicked outside of the regularly scheduled class on study materials within the online Learning Management System. Three main effects emerged: studying course content materials (as opposed to course logistics materials) outside of class and higher grades are consistently correlated; studying at any time except in the late night/early morning hours was strongly correlated with grades; students with higher Scholastic Aptitude Test (SAT) scores made higher grades but accessed course materials at lower rates that those with lower SATs. Multiple regressions predicting grades using just SATs and click rates accounted for almost 43 and 36 percent of the grade variance for the Fall and Spring classes respectively. Implications for using click patterns to understand and shape student learning are discussed.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Previous relationship research has largely ignored the importance of similarity in how people talk with one another. Using natural language samples, we investigated whether similarity in dyads' use ...of function words, called language style matching (LSM), predicts outcomes for romantic relationships. In Study I, greater LSM in transcripts of 40 speed dates predicted increased likelihood of mutual romantic interest (odds ratio = 3.05). Overall, 33.3% of pairs with LSM above the median mutually desired future contact, compared with 9.1% of pairs with LSM at or below the median. In Study 2, LSM in 86 couples' instant messages positively predicted relationship stability at a 3-month follow-up (odds ratio = 1.95). Specifically, 76.7% of couples with LSM greater than the median were still dating at the follow-up, compared with 53.5% of couples with LSM at or below the median. LSM appears to reflect implicit interpersonal processes central to romantic relationships.
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The current research chronicles the unfolding of the early psychological impacts of coronavirus disease 2019 (COVID-19) by analyzing Reddit language from 18 U.S. cities (200,000+ people) and ...large-scale survey data (11,000+ people). Large psychological shifts were found reflecting three distinct phases. When COVID-19 warnings first emerged (“warning phase”), people’s attentional focus switched to the impending threat. Anxiety levels surged, and positive emotion and anger dropped. In parallel, people’s thinking became more intuitive rather than analytic. When lockdowns began (“isolation phase”), analytic thinking dropped further. People became sadder, and their thinking reflected attempts to process the uncertainty. Familial ties strengthened, but ties to broader social groups weakened. Six weeks after COVID-19’s onset (“normalization phase”), people’s psychological states stabilized but remained elevated. Most psychological shifts were stronger when the threat of COVID-19 was greater. The magnitude of the observed shifts dwarfed responses to other events that occurred in the previous decade.
The words people use in their daily lives can reveal important aspects of their social and psychological worlds. With advances in computer technology, text analysis allows researchers to reliably and ...quickly assess features of what people say as well as subtleties in their linguistic styles. Following a brief review of several text analysis programs, we summarize some of the evidence that links natural word use to personality, social and situational fluctuations, and psychological interventions. Of particular interest are findings that point to the psychological value of studying particles-parts of speech that include pronouns, articles, prepositions, conjunctives, and auxiliary verbs. Particles, which serve as the glue that holds nouns and regular verbs together, can serve as markers of emotional state, social identity, and cognitive styles.
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The diaries of 1,084 U.S. users of an on-line journaling service were downloaded for a period of 4 months spanning the 2 months prior to and after the September 11 attacks. Linguistic analyses of the ...journal entries revealed pronounced psychological changes in response to the attacks. In the short term, participants expressed more negative emotions, were more cognitively and socially engaged, and wrote with greater psychological distance. After 2 weeks, their moods and social referencing returned to baseline, and their use of cognitive-analytic words dropped below baseline. Over the next 6 weeks, social referencing decreased, and psychological distancing remained elevated relative to baseline. Although the effects were generally stronger for individuals highly preoccupied with September 11, even participants who hardly wrote about the events showed comparable language changes. This study bypasses many of the methodological obstacles of trauma research and provides a finegrained analysis of the time line of human coping with upheaval.
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BFBNIB, DOBA, FSPLJ, IZUM, KILJ, NMLJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Depressive symptomatology is manifested in greater first-person singular pronoun use (i.e., I-talk), but when and for whom this effect is most apparent, and the extent to which it is specific to ...depression or part of a broader association between negative emotionality and I-talk, remains unclear. Using pooled data from N = 4,754 participants from 6 labs across 2 countries, we examined, in a preregistered analysis, how the depression-I-talk effect varied by (a) first-person singular pronoun type (i.e., subjective, objective, and possessive), (b) the communication context in which language was generated (i.e., personal, momentary thought, identity-related, and impersonal), and (c) gender. Overall, there was a small but reliable positive correlation between depression and I-talk (r = .10, 95% CI .07, .13). The effect was present for all first-person singular pronouns except the possessive type, in all communication contexts except the impersonal one, and for both females and males with little evidence of gender differences. Importantly, a similar pattern of results emerged for negative emotionality. Further, the depression-I-talk effect was substantially reduced when controlled for negative emotionality but this was not the case when the negative emotionality-I-talk effect was controlled for depression. These results suggest that the robust empirical link between depression and I-talk largely reflects a broader association between negative emotionality and I-talk. Self-referential language using first-person singular pronouns may therefore be better construed as a linguistic marker of general distress proneness or negative emotionality rather than as a specific marker of depression.
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Most scientists agree that climate change is the largest existential threat of our time. Despite the magnitude of the threat, surprisingly few climate-related discussions take place on social media. ...What factors drive online discussions about climate change? In this study, we examined the occurrence of Reddit discussions around three types of climate-related events: natural disasters (e.g., hurricanes, wildfires), political events (i.e., 2016 United States Presidential election), and policy events (i.e., United States’ withdrawal from Paris Climate Agreement, release of IPCC report). The objective was to understand how different types of events influence collective action as measured by discussions of climate change. Six large US cities were selected based on the occurrence of at least one locally-relevant natural disaster since 2014. Posts (
N
= 4.4 million) from subreddits of the selected cities were collected to obtain a six-month period before and after local natural disasters as well as climate-related political and policy events (which applied equally to all cities). Climate change discussions increased significantly for all three types of events, with the highest discussion during the 2016 elections. Further, discussions returned to baseline levels within 2 months following natural disasters and policy events but continued at elevated rates for up to 4 months following the 2016 elections. The findings suggest that collective discussions on climate change are driven more by political leaders’ controversial positions than life-threatening local natural disasters themselves. Implications for collective action are discussed.
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