Tracking Fluctuations in Psychological States Using Social Media Language: A Case Study of Weekly Emotion

Personality psychologists are increasingly documenting dynamic, within‐person processes. Big data methodologies can augment this endeavour by allowing for the collection of naturalistic and personality‐relevant digital traces from online environments. Whereas big data methods have primarily been used to catalogue static personality dimensions, here we present a case study in how they can be used to track dynamic fluctuations in psychological states. We apply a text‐based, machine learning prediction model to Facebook status updates to compute weekly trajectories of emotional valence and arousal. We train this model on 2895 human‐annotated Facebook statuses and apply the resulting model to 303 575 Facebook statuses posted by 640 US Facebook users who had previously self‐reported their Big Five traits, yielding an average of 28 weekly estimates per user. We examine the correlations between model‐predicted emotion and self‐reported personality, providing a test of the robustness of these links when using weekly aggregated data, rather than momentary data as in prior work. We further present dynamic visualizations of weekly valence and arousal for every user, while making the final data set of 17 937 weeks openly available. We discuss the strengths and drawbacks of this method in the context of personality psychology’s evolution into a dynamic science. © 2020 European Association of Personality PsychologyPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163564/3/per2261-sup-0001-Open_Practices_Disclosure_Form.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163564/2/per2261.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163564/1/per2261_am.pd

What Twitter Profile and Posted Images Reveal About Depression and Anxiety

Previous work has found strong links between the choice of social media images and users' emotions, demographics and personality traits. In this study, we examine which attributes of profile and posted images are associated with depression and anxiety of Twitter users. We used a sample of 28,749 Facebook users to build a language prediction model of survey-reported depression and anxiety, and validated it on Twitter on a sample of 887 users who had taken anxiety and depression surveys. We then applied it to a different set of 4,132 Twitter users to impute language-based depression and anxiety labels, and extracted interpretable features of posted and profile pictures to uncover the associations with users' depression and anxiety, controlling for demographics. For depression, we find that profile pictures suppress positive emotions rather than display more negative emotions, likely because of social media self-presentation biases. They also tend to show the single face of the user (rather than show her in groups of friends), marking increased focus on the self, emblematic for depression. Posted images are dominated by grayscale and low aesthetic cohesion across a variety of image features. Profile images of anxious users are similarly marked by grayscale and low aesthetic cohesion, but less so than those of depressed users. Finally, we show that image features can be used to predict depression and anxiety, and that multitask learning that includes a joint modeling of demographics improves prediction performance. Overall, we find that the image attributes that mark depression and anxiety offer a rich lens into these conditions largely congruent with the psychological literature, and that images on Twitter allow inferences about the mental health status of users.Comment: ICWSM 201

Big data methods, social media, and the psychology of entrepreneurial regions: capturing cross-county personality traits and their impact on entrepreneurship in the USA

There is increasing interest in the potential of artificial intelligence and Big Data (e.g., generated via social media) to help understand economic outcomes. But can artificial intelligence models based on publicly available Big Data identify geographical differences in entrepreneurial personality or culture? We use a machine learning model based on 1.5 billion tweets by 5.25 million users to estimate the Big Five personality traits and an entrepreneurial personality profile for 1,772 U.S. counties. The Twitter-based personality estimates show substantial relationships to county-level entrepreneurship activity, accounting for 20% (entrepreneurial personality profile) and 32% (Big Five traits) of the variance in local entrepreneurship, even when controlling for other factors that affect entrepreneurship. Whereas more research is clearly needed, our findings have initial implications for research and practice concerned with entrepreneurial regions and eco-systems, and regional economic outcomes interacting with local culture. The results suggest, for example, that social media datasets and artificial intelligence methods have the potential to deliver comparable information on the personality and culture of regions than studies based on millions of questionnaire-based personality tests

Lifestyle and wellbeing: Exploring behavioral and demographic covariates in a large US sample

Using data from a nationally representative sample of 46,179 US adults from the Gallup-Healthways Wellbeing Index, we investigate covariates of four subjective mental wellbeing dimensions spanning evaluative (life satisfaction), positive affective (happiness), negative affective (worry), and eudaimonic wellbeing. Negative covariates were generally more strongly correlated with the four dimensions than positive covariates, with depression, poor health, and loneliness being the greatest negative correlates and excellent health and older age being the greatest positive correlates. We reproduce previous evidence for a “midlife crisis” around age 50 across the four wellbeing dimensions. Notably, although salutogenic behaviors (diet, exercise, socializing) correlated with greater wellbeing, there were diminishing benefits beyond thresholds of about four hours a day spent socializing, four days per week of consuming fruits and vegetables, and four days per week of exercising. Findings suggest that wellbeing is easier lost than gained, underscore the influence that relatively malleable lifestyle factors have on wellbeing, and stress the importance of multidimensional measurement for public policy

Unobtrusively Measuring the Well-Being of Entire Nations: The World Well-Being Project

The primary focus of most developed nations is economic development. However, governments around the world are starting to consider the well-being of their citizens as an explicit goal of public policy, and are consolidating the necessary infrastructure to measure it. To contribute to this new generation of policy objectives, the World Well-Being Project (WWBP) at the Positive Psychology Center at Penn is developing a novel method to unobtrusively measure the well-being of large populations. Through analyzing Facebook status updates, blog posts, Twitter updates, and Google search queries for expressions of Positive Emotion, Engagement, Positive Relationships, Meaning, and Accomplishment (PERMA), we seek to provide a new measurement methodology that is easily scalable to entire populations, highly cost-effective, and available essentially in real time with high spatial and temporal resolution. This capstone project outlines the theoretical background and rationale for the project, describes the foundational work done in the WWBP, including a description of the computational research infrastructure created to work with the massive databases containing the social media data, and provides preliminary results and complications

Real-world unexpected outcomes predict city-level mood states and risk-taking behavior.

Fluctuations in mood states are driven by unpredictable outcomes in daily life but also appear to drive consequential behaviors such as risk-taking. However, our understanding of the relationships between unexpected outcomes, mood, and risk-taking behavior has relied primarily upon constrained and artificial laboratory settings. Here we examine, using naturalistic datasets, how real-world unexpected outcomes predict mood state changes observable at the level of a city, in turn predicting changes in gambling behavior. By analyzing day-to-day mood language extracted from 5.2 million location-specific and public Twitter posts or 'tweets', we examine how real-world 'prediction errors'-local outcomes that deviate positively from expectations-predict day-to-day mood states observable at the level of a city. These mood states in turn predicted increased per-person lottery gambling rates, revealing how interplay between prediction errors, moods, and risky decision-making unfolds in the real world. Our results underscore how social media and naturalistic datasets can uniquely allow us to understand consequential psychological phenomena