A meta-analysis of linguistic markers of extraversion: Positive emotion and social process words

How Users' Personality Traits Predict Sentiment Tendencies of User-Generated Content in Social Media: A Mixed Method of Configuration Analysis and Machine Learning

OBJECTIVE

Social media content created by users with different personality traits presents various sentiment tendencies, easily leading to irrational public opinion. This study aims to explore the relationships between users' personality traits and sentiment tendencies of user-generated content (UGC).

METHOD

We crawled 18,686 tweets of 1, 215 users from Twitter to figure out the relationships between personality traits and sentiment tendencies. This study utilizes Essays and Sentiment datasets to train machine learning models for the identification of personality traits and sentiment tendencies and then explores the configuration effect of personality traits on sentiment tendency via crisp-set Qualitative Comparative Analysis (csQCA).

RESULT

The findings suggest that (1) one-dimensional personality trait is not a necessary condition for the sentiment tendencies of UGC. (2) There are multiple equivalent configurations that lead to the sentiment tendencies of UGC.

CONCLUSION

The study suggests that the sentiment tendencies pattern of UGC can be discovered via the configurations of various dimensions of personality traits.

The role of individual differences in emotional word recognition: Insights from a large-scale lexical decision study

This work presents a large lexical decision mega-study in Spanish, with 918 participants and 7500 words, focusing on emotional content and individual differences. The main objective was to investigate how emotional valence and arousal influence word recognition, controlling for a large number of confounding variables. In addition, as a unique contribution, the study examined the modulation of these effects by individual differences. Results indicated a significant effect of valence and arousal on lexical decision times, with an interaction between these variables. A linear effect of valence was observed, with slower recognition times for negative words and faster recognition times for positive words. In addition, arousal showed opposite effects in positive and negative words. Importantly, the effect of emotional variables was affected by personality traits (extroversion, conscientiousness and openness to experience), age and gender, challenging the 'one-size-fits-all' interpretation of emotional word processing. All data collected in the study is available to the research community: https://osf.io/cbtqy . This includes data from each participant (RTs, errors and individual differences scores), as well as values of concreteness (n = 1690), familiarity (n = 1693) and age of acquisition (n = 2171) of the words collected exclusively for this study. This is a useful resource for researchers interested not only in emotional word processing, but also in lexical processing in general and the influence of individual differences.

Robust language-based mental health assessments in time and space through social media

In the most comprehensive population surveys, mental health is only broadly captured through questionnaires asking about "mentally unhealthy days" or feelings of "sadness." Further, population mental health estimates are predominantly consolidated to yearly estimates at the state level, which is considerably coarser than the best estimates of physical health. Through the large-scale analysis of social media, robust estimation of population mental health is feasible at finer resolutions. In this study, we created a pipeline that used ~1 billion Tweets from 2 million geo-located users to estimate mental health levels and changes for depression and anxiety, the two leading mental health conditions. Language-based mental health assessments (LBMHAs) had substantially higher levels of reliability across space and time than available survey measures. This work presents reliable assessments of depression and anxiety down to the county-weeks level. Where surveys were available, we found moderate to strong associations between the LBMHAs and survey scores for multiple levels of granularity, from the national level down to weekly county measurements (fixed effects β = 0.34 to 1.82; p < 0.001). LBMHAs demonstrated temporal validity, showing clear absolute increases after a list of major societal events (+23% absolute change for depression assessments). LBMHAs showed improved external validity, evidenced by stronger correlations with measures of health and socioeconomic status than population surveys. This study shows that the careful aggregation of social media data yields spatiotemporal estimates of population mental health that exceed the granularity achievable by existing population surveys, and does so with generally greater reliability and validity.

Gender difference in the relationship between personality traits and changes in depressive symptoms before and after the COVID-19 outbreak: A follow-up study among Chinese adults

OBJECTIVES

Increasing depressive symptoms have become an urgent public health concern worldwide. This study aims to explore the correlation between personality traits and changes in depressive symptoms before and after the COVID-19 outbreak and to examine the gender difference in this association further.

METHODS

Data were obtained from the China Family Panel Studies (CFPS, wave in 2018 and 2020). A total of 16,369 residents aged 18 and above were included in this study. Multinomial logistic regression analysis was used to examine whether personality traits were associated with changes in depressive symptoms. We also analyzed whether there was an interaction effect of gender and personality traits on depressive symptoms.

RESULTS

Conscientiousness, extroversion, and agreeableness are negatively associated with depressive symptoms, while neuroticism and openness are positively related. Gender moderates the relationship between personality traits and depressive symptoms. Compared to men, women have demonstrated a stronger association between neuroticism (OR = 0.79; 95 % CI = 0.66, 0.94), conscientiousness (OR = 1.40; 95 % CI = 1.15, 1.69), and persistent depressive symptoms.

LIMITATIONS

Given its longitudinal study design, it is insufficient to draw a causal inference between personality traits and depressive symptoms.

CONCLUSION

Personality traits and their various dimensions are correlated with changes in depressive symptoms. Persistent depressive symptoms are positively related to neuroticism and negatively associated with conscientiousness. Women demonstrate a stronger association between personality traits and persistent depressive symptoms. Thus, in Chinese adults' mental health intervention and prevention programs, personality and gender-specific strategies should be considered, especially in the context of the COVID-19 pandemic.

Personality and Word Use: Study on Czech Language and the Big Five

The study is a follow-up to three published anglophone researches examining the relation between the use of linguistic categories and personality characteristics as outlined in the Big Five model, with the purpose of replicating these and elaborating for the Czech language. The comparative research study in Czech focuses on analysis of both grammatical and semantic variables in six types of text (written and oral), produced by N = 200 participants. Within the study, there were six confirmed relations, however, these appear only in certain types of text. The results show not only an essential role of the text register, but they also allow us to evaluate the universality of findings of studies in English in comparison with other, especially Slavic, languages.

Linguistic measures of personality in group discussions

This investigation sought to find the relationships among multiple dimensions of personality and multiple features of language style. Unlike previous investigations, after controlling for such other moderators as culture and socio-demographics, the current investigation explored those dimensions of naturalistic spoken language that most closely align with communication. In groups of five to eight players, participants (N = 340) from eight international locales completed hour-long competitive games consisting of a series of ostensible missions. Composite measures of quantity, lexical diversity, sentiment, immediacy and negations were measured with an automated tool called SPLICE and with Linguistic Inquiry and Word Count. We also investigated style dynamics over the course of an interaction. We found predictors of extraversion, agreeableness, and neuroticism, but overall fewer significant associations than prior studies, suggesting greater heterogeneity in language style in contexts entailing interactivity, conversation rather than solitary message production, oral rather than written discourse, and groups rather than dyads. Extraverts were found to maintain greater linguistic style consistency over the course of an interaction. The discussion addresses the potential for Type I error when studying the relationship between language and personality.

The dashang feature in social media: a personality and justice theory perspective

Purpose

Dashang refers to a reward given voluntarily to street performers in return for their performance. Some social media platforms have created a way to integrate this as a function, referred to as the dashang feature, to allow users to reward live performers online as well. Over the last few years, this function has become extremely popular among social media users, as it recreates the nostalgic experience of watching street performances. Platforms now consider it indispensable, as it has become a source of substantial revenue (commission on rewards earned by performers). However, not all users reward performers. For each user who pays, there are many more who lurk on the platform. This study examines the reasons for these differences using the Big Five personality perspective and justice theory.

Design/methodology/approach

We develop an empirical model using the Big Five theory and justice theory and test it using empirical data collected through a survey of WeChat users.

Findings

The results indicate that distributive justice, interpersonal justice and informational justice are essential factors in relation to social media users' use of the dashang feature. It is also found that personality type affects these three factors.

Originality/value

This study makes three key contributions. First, it examines the factors that influence users' voluntary use of the dashang feature using the lenses of the Big Five theory and justice theory. Second, this study extends previous results on perceived justice to examine use of the dashang feature in social media. Third, this study applies these theories to the study of consumer behavior by exploring the role of user characteristics in social media use.

Beyond English: Considering Language and Culture in Psychological Text Analysis

The paper discusses the role of language and culture in the context of quantitative text analysis in psychological research. It reviews current automatic text analysis methods and approaches from the perspective of the unique challenges that can arise when going beyond the default English language. Special attention is paid to closed-vocabulary approaches and related methods (and Linguistic Inquiry and Word Count in particular), both from the perspective of cross-cultural research where the analytic process inherently consists of comparing phenomena across cultures and languages and the perspective of generalizability beyond the language and the cultural focus of the original investigation. We highlight the need for a more universal and flexible theoretical and methodological grounding of current research, which includes the linguistic, cultural, and situational specifics of communication, and we provide suggestions for procedures that can be implemented in future studies and facilitate psychological text analysis across languages and cultures.