Political Attitudes and Disease Threat: Regional Pathogen Stress Is Associated With Conservative Ideology Only for Older Individuals.

What environmental factors are associated with individual differences in political ideology, and do such associations change over time? We examine whether reductions in pathogen prevalence in U.S. states over the past 60 years are associated with reduced associations between parasite stress and conservatism. We report a positive association between infection levels and conservative ideology in the United States during the 1960s and 1970s. However, this correlation reduces from the 1980s onwards. These results suggest that the ecological influence of infectious diseases may be larger for older people who grew up (or whose parents grew up) during earlier time periods. We test this hypothesis by analyzing the political affiliation of 45,000 Facebook users, and find a positive association between self-reported political affiliation and regional pathogen stress for older (>40 years) but not younger individuals. It is concluded that the influence of environmental pathogen stress on ideology may have reduced over time.

Developing a sentence level fairness metric using word embeddings.

Fairness is a principal social value that is observable in civilisations around the world. Yet, a fairness metric for digital texts that describe even a simple social interaction, e.g., 'The boy hurt the girl' has not been developed. We address this by employing word embeddings that use factors found in a new social psychology literature review on the topic. We use these factors to build fairness vectors. These vectors are used as sentence level measures, whereby each dimension reflects a fairness component. The approach is employed to approximate human perceptions of fairness. The method leverages a pro-social bias within word embeddings, for which we obtain an F1 = 79.8 on a list of sentences using the Universal Sentence Encoder (USE). A second approach, using principal component analysis (PCA) and machine learning (ML), produces an F1 = 86.2. Repeating these tests using Sentence Bidirectional Encoder Representations from Transformers (SBERT) produces an F1 = 96.9 and F1 = 100 respectively. Improvements using subspace representations are further suggested. By proposing a first-principles approach, the paper contributes to the analysis of digital texts along an ethical dimension.

Two is better than one: Using a single emotion lexicon can lead to unreliable conclusions.

Emotion lexicons became a popular method for quantifying affect in large amounts of textual data (e.g., social media posts). There are multiple independently developed emotion lexicons which tend to correlate positively with one another but not entirely. Such differences between lexicons may not matter if they are just unsystematic noise, but if there are systematic differences this could affect conclusions of a study. The goal of this paper is to examine whether two extensively used, apparently domain-independent lexicons for emotion analysis would give the same answer to a theory-driven research question. Specifically, we use the Linguistic Inquiry and Word Count (LIWC) and NRC Word-Emotion Association Lexicon (NRC). As an example, we investigate whether older people have more positive expression through their language use. We examined nearly 5 million tweets created by 3,573 people between 18 to 78 years old and found that both methods show an increase in positive affect until age 50. After that age, however, according to LIWC, positive affect drops sharply, whereas according to NRC, the growth of positive affect increases steadily until age 65 and then levels off. Thus, using one or the other method would lead researchers to drastically different theoretical conclusions regarding affect in older age. We unpack why the two methods give inconsistent conclusions and show this was mostly due to a particular class of words: those related to politics. We conclude that using a single lexicon might lead to unreliable conclusions, so we suggest that researchers should routinely use at least two lexicons. If both lexicons come to the same conclusion then the research evidence is reliable, but if not then researchers should further examine the lexicons to find out what difference might be causing inconclusive result.

Individual-Level Analyses of the Impact of Parasite Stress on Personality: Reduced Openness Only for Older Individuals.

The parasite stress hypothesis predicts that individuals living in regions with higher infectious disease rates will show lower openness, agreeableness, and extraversion, but higher conscientiousness. This article, using data from more than 250,000 U.S. Facebook users, reports tests of these predictions at the level of both U.S. states and individuals and evaluates criticisms of previous findings. State-level results for agreeableness and conscientiousness are consistent with previously reported cross-national findings, but others (a significant positive correlation with extraversion and no correlation with openness) are not. However, effects of parasite stress on conscientiousness and agreeableness are not found when analyses account for the data's hierarchical structure and include controls. We find that only openness is robustly related to parasite stress in these analyses, and we also find a significant interaction with age: Older, but not younger, inhabitants of areas of high parasite stress show lower openness. Interpretations of the findings are discussed.

Predicting individual-level income from Facebook profiles.

Information about a person's income can be useful in several business-related contexts, such as personalized advertising or salary negotiations. However, many people consider this information private and are reluctant to share it. In this paper, we show that income is predictable from the digital footprints people leave on Facebook. Applying an established machine learning method to an income-representative sample of 2,623 U.S. Americans, we found that (i) Facebook Likes and Status Updates alone predicted a person's income with an accuracy of up to r = 0.43, and (ii) Facebook Likes and Status Updates added incremental predictive power above and beyond a range of socio-demographic variables (ΔR2 = 6-16%, with a correlation of up to r = 0.49). Our findings highlight both opportunities for businesses and legitimate privacy concerns that such prediction models pose to individuals and society when applied without individual consent.

Latent human traits in the language of social media: An open-vocabulary approach.

Over the past century, personality theory and research has successfully identified core sets of characteristics that consistently describe and explain fundamental differences in the way people think, feel and behave. Such characteristics were derived through theory, dictionary analyses, and survey research using explicit self-reports. The availability of social media data spanning millions of users now makes it possible to automatically derive characteristics from behavioral data-language use-at large scale. Taking advantage of linguistic information available through Facebook, we study the process of inferring a new set of potential human traits based on unprompted language use. We subject these new traits to a comprehensive set of evaluations and compare them with a popular five factor model of personality. We find that our language-based trait construct is often more generalizable in that it often predicts non-questionnaire-based outcomes better than questionnaire-based traits (e.g. entities someone likes, income and intelligence quotient), while the factors remain nearly as stable as traditional factors. Our approach suggests a value in new constructs of personality derived from everyday human language use.

Musical Preferences Predict Personality: Evidence From Active Listening and Facebook Likes.

Research over the past decade has shown that various personality traits are communicated through musical preferences. One limitation of that research is external validity, as most studies have assessed individual differences in musical preferences using self-reports of music-genre preferences. Are personality traits communicated through behavioral manifestations of musical preferences? We addressed this question in two large-scale online studies with demographically diverse populations. Study 1 ( N = 22,252) shows that reactions to unfamiliar musical excerpts predicted individual differences in personality-most notably, openness and extraversion-above and beyond demographic characteristics. Moreover, these personality traits were differentially associated with particular music-preference dimensions. The results from Study 2 ( N = 21,929) replicated and extended these findings by showing that an active measure of naturally occurring behavior, Facebook Likes for musical artists, also predicted individual differences in personality. In general, our findings establish the robustness and external validity of the links between musical preferences and personality.

A Computer Adaptive Measure of Delay Discounting.

Delay discounting has been linked to important behavioral, health, and social outcomes, including academic achievement, social functioning and substance use, but thoroughly measuring delay discounting is tedious and time consuming. We develop and consistently validate an efficient and psychometrically sound computer adaptive measure of discounting. First, we develop a binary search-type algorithm to measure discounting using a large international data set of 4,190 participants. Using six independent samples ( N = 1,550), we then present evidence of concurrent validity with two standard measures of discounting and a measure of discounting real rewards, convergent validity with addictive behavior, impulsivity, personality, survival probability; and divergent validity with time perspective, life satisfaction, age and gender. The new measure is considerably shorter than standard questionnaires, includes a range of time delays, can be applied to multiple reward magnitudes, shows excellent concurrent, convergent, divergent, and discriminant validity-by showing more sensitivity to effects of smoking behavior on discounting.

In your eyes only? Discrepancies and agreement between self- and other-reports of personality from age 14 to 29.

Do others perceive the personality changes that take place between the ages of 14 and 29 in a similar fashion as the aging person him- or herself? This cross-sectional study analyzed age trajectories in self- versus other-reported Big Five personality traits and in self-other agreement in a sample of more than 10,000 individuals from the myPersonality Project. Results for self-reported personality showed maturation effects (increases in extraversion, conscientiousness, openness to experience, and emotional stability), and this pattern was generally also reflected in other-reports, albeit with discrepancies regarding timing and magnitude. Age differences found for extraversion were similar between the self- and other-reports, but the increase found in self-reported conscientiousness was delayed in other-reports, and the curvilinear increase found in self-reported openness was slightly steeper in other-reports. Only emotional stability showed a distinct mismatch with an increase in self-reports, but no significant age effect in other-reports. Both the self- and other-reports of agreeableness showed no significant age trends. The trait correlations between the self- and other-reports increased with age for emotional stability, openness, agreeableness, and conscientiousness; by contrast, agreement regarding extraversion remained stable. The profile correlations confirmed increases in self-other agreement with age. We suggest that these gains in agreement are a further manifestation of maturation. Taken together, our analyses generally show commonalities but also some divergences in age-associated mean level changes between self- and other-reports of the Big Five, as well as an age trend toward increasing self-other agreement. (PsycINFO Database Record

Frankly, We Do Give a Damn: The Relationship Between Profanity and Honesty.

There are two conflicting perspectives regarding the relationship between profanity and dishonesty. These two forms of norm-violating behavior share common causes and are often considered to be positively related. On the other hand, however, profanity is often used to express one's genuine feelings and could therefore be negatively related to dishonesty. In three studies, we explored the relationship between profanity and honesty. We examined profanity and honesty first with profanity behavior and lying on a scale in the lab (Study 1; N = 276), then with a linguistic analysis of real-life social interactions on Facebook (Study 2; N = 73,789), and finally with profanity and integrity indexes for the aggregate level of U.S. states (Study 3; N = 50 states). We found a consistent positive relationship between profanity and honesty; profanity was associated with less lying and deception at the individual level and with higher integrity at the society level.

Building a profile of subjective well-being for social media users.

Subjective well-being includes 'affect' and 'satisfaction with life' (SWL). This study proposes a unified approach to construct a profile of subjective well-being based on social media language in Facebook status updates. We apply sentiment analysis to generate users' affect scores, and train a random forest model to predict SWL using affect scores and other language features of the status updates. Results show that: the computer-selected features resemble the key predictors of SWL as identified in early studies; the machine-predicted SWL is moderately correlated with the self-reported SWL (r = 0.36, p < 0.01), indicating that language-based assessment can constitute valid SWL measures; the machine-assessed affect scores resemble those reported in a previous experimental study; and the machine-predicted subjective well-being profile can also reflect other psychological traits like depression (r = 0.24, p < 0.01). This study provides important insights for psychological prediction using multiple, machine-assessed components and longitudinal or dense psychological assessment using social media language.

Computerized Adaptive Testing Provides Reliable and Efficient Depression Measurement Using the CES-D Scale.

BACKGROUND: The Center for Epidemiologic Studies Depression Scale (CES-D) is a measure of depressive symptomatology which is widely used internationally. Though previous attempts were made to shorten the CES-D scale, few have attempted to develop a Computerized Adaptive Test (CAT) version for the CES-D. OBJECTIVE: The aim of this study was to provide evidence on the efficiency and accuracy of the CES-D when administered using CAT using an American sample group. METHODS: We obtained a sample of 2060 responses to the CESD-D from US participants using the myPersonality application. The average age of participants was 26 years (range 19-77). We randomly split the sample into two groups to evaluate and validate the psychometric models. We used evaluation group data (n=1018) to assess dimensionality with both confirmatory factor and Mokken analysis. We conducted further psychometric assessments using item response theory (IRT), including assessments of item and scale fit to Samejima's graded response model (GRM), local dependency and differential item functioning. We subsequently conducted two CAT simulations to evaluate the CES-D CAT using the validation group (n=1042). RESULTS: Initial CFA results indicated a poor fit to the model and Mokken analysis revealed 3 items which did not conform to the same dimension as the rest of the items. We removed the 3 items and fit the remaining 17 items to GRM. We found no evidence of differential item functioning (DIF) between age and gender groups. Estimates of the level of CES-D trait score provided by the simulated CAT algorithm and the original CES-D trait score derived from original scale were correlated highly. The second CAT simulation conducted using real participant data demonstrated higher precision at the higher levels of depression spectrum. CONCLUSIONS: Depression assessments using the CES-D CAT can be more accurate and efficient than those made using the fixed-length assessment.

Birds of a Feather Do Flock Together.

Friends and spouses tend to be similar in a broad range of characteristics, such as age, educational level, race, religion, attitudes, and general intelligence. Surprisingly, little evidence has been found for similarity in personality-one of the most fundamental psychological constructs. We argue that the lack of evidence for personality similarity stems from the tendency of individuals to make personality judgments relative to a salient comparison group, rather than in absolute terms (i.e., the reference-group effect), when responding to the self-report and peer-report questionnaires commonly used in personality research. We employed two behavior-based personality measures to circumvent the reference-group effect. The results based on large samples provide evidence for personality similarity between romantic partners ( n = 1,101; rs = .20-.47) and between friends ( n = 46,483; rs = .12-.31). We discuss the practical and methodological implications of the findings.

Living in the Past, Present, and Future: Measuring Temporal Orientation With Language.

Temporal orientation refers to individual differences in the relative emphasis one places on the past, present, or future, and it is related to academic, financial, and health outcomes. We propose and evaluate a method for automatically measuring temporal orientation through language expressed on social media. Judges rated the temporal orientation of 4,302 social media messages. We trained a classifier based on these ratings, which could accurately predict the temporal orientation of new messages in a separate validation set (accuracy/mean sensitivity = .72; mean specificity = .77). We used the classifier to automatically classify 1.3 million messages written by 5,372 participants (50% female; ages 13-48). Finally, we tested whether individual differences in past, present, and future orientation differentially related to gender, age, Big Five personality, satisfaction with life, and depressive symptoms. Temporal orientations exhibit several expected correlations with age, gender, and Big Five personality. More future-oriented people were older, more likely to be female, more conscientious, less impulsive, less depressed, and more satisfied with life; present orientation showed the opposite pattern. Language-based assessments can complement and extend existing measures of temporal orientation, providing an alternative approach and additional insights into language and personality relationships.

Using Item Response Theory to Develop Measures of Acquisitive and Protective Self-Monitoring From the Original Self-Monitoring Scale.

For the past 40 years, the conventional univariate model of self-monitoring has reigned as the dominant interpretative paradigm in the literature. However, recent findings associated with an alternative bivariate model challenge the conventional paradigm. In this study, item response theory is used to develop measures of the bivariate model of acquisitive and protective self-monitoring using original Self-Monitoring Scale (SMS) items, and data from two large, nonstudent samples ( Ns = 13,563 and 709). Results indicate that the new acquisitive (six-item) and protective (seven-item) self-monitoring scales are reliable, unbiased in terms of gender and age, and demonstrate theoretically consistent relations to measures of personality traits and cognitive ability. Additionally, by virtue of using original SMS items, previously collected responses can be reanalyzed in accordance with the alternative bivariate model. Recommendations for the reanalysis of archival SMS data, as well as directions for future research, are provided.

Women are Warmer but No Less Assertive than Men: Gender and Language on Facebook.

Using a large social media dataset and open-vocabulary methods from computational linguistics, we explored differences in language use across gender, affiliation, and assertiveness. In Study 1, we analyzed topics (groups of semantically similar words) across 10 million messages from over 52,000 Facebook users. Most language differed little across gender. However, topics most associated with self-identified female participants included friends, family, and social life, whereas topics most associated with self-identified male participants included swearing, anger, discussion of objects instead of people, and the use of argumentative language. In Study 2, we plotted male- and female-linked language topics along two interpersonal dimensions prevalent in gender research: affiliation and assertiveness. In a sample of over 15,000 Facebook users, we found substantial gender differences in the use of affiliative language and slight differences in assertive language. Language used more by self-identified females was interpersonally warmer, more compassionate, polite, and-contrary to previous findings-slightly more assertive in their language use, whereas language used more by self-identified males was colder, more hostile, and impersonal. Computational linguistic analysis combined with methods to automatically label topics offer means for testing psychological theories unobtrusively at large scale.

Money Buys Happiness When Spending Fits Our Personality.

In contrast to decades of research reporting surprisingly weak relationships between consumption and happiness, recent findings suggest that money can indeed increase happiness if it is spent the "right way" (e.g., on experiences or on other people). Drawing on the concept of psychological fit, we extend this research by arguing that individual differences play a central role in determining the "right" type of spending to increase well-being. In a field study using more than 76,000 bank-transaction records, we found that individuals spend more on products that match their personality, and that people whose purchases better match their personality report higher levels of life satisfaction. This effect of psychological fit on happiness was stronger than the effect of individuals' total income or the effect of their total spending. A follow-up study showed a causal effect: Personality-matched spending increased positive affect. In summary, when spending matches the buyer's personality, it appears that money can indeed buy happiness.

PREDICTING INDIVIDUAL WELL-BEING THROUGH THE LANGUAGE OF SOCIAL MEDIA.

We present the task of predicting individual well-being, as measured by a life satisfaction scale, through the language people use on social media. Well-being, which encompasses much more than emotion and mood, is linked with good mental and physical health. The ability to quickly and accurately assess it can supplement multi-million dollar national surveys as well as promote whole body health. Through crowd-sourced ratings of tweets and Facebook status updates, we create message-level predictive models for multiple components of well-being. However, well-being is ultimately attributed to people, so we perform an additional evaluation at the user-level, finding that a multi-level cascaded model, using both message-level predictions and userlevel features, performs best and outperforms popular lexicon-based happiness models. Finally, we suggest that analyses of language go beyond prediction by identifying the language that characterizes well-being.