Sanjay Srivastava - US grants

Personality traits are associated with a range of important life outcomes related to occupational success, close relationships, mental and physical health, and other domains. For example, people with a moody or fearful personality are more likely to have conflicts with partners, co-workers, neighbors and so on, and therefore are at risk for distressed relationships. Previously, the predominant view of personality regarded traits like "moody" as endogenous dispositions that become stable in early adulthood and change very little thereafter. However, a growing body of recent research has challenged this viewpoint and documented that personality traits can and do change throughout the lifespan, sometimes substantially. The primary goal of this research is to better understand the dynamics underlying personality changes during adulthood.

This research is designed to test two broad categories of explanations for personality change. The first concerns social roles, and begins with the observation that across the lifespan, adults move through a variety of evolving social roles as workers, parents, and caregivers, among others. Over time, social roles may dynamically interact with personality traits, with social roles promoting changes in personality and one's traits providing the basis for self-selection into, and identification with, different social roles. The second explanation involves an individual's worldview and values, defined as broad motivational orientations that prioritize desirable end-states across a variety of situations. Values and worldview have the potential to shape and reorganize personality (e.g., pursuit of communitarian values may shift an individual toward a more generous and other-oriented disposition). To test these and related hypotheses, two longitudinal studies will be conducted. Study 1 will focus on young adults who are beginning to take on adult social roles and consolidating a sense of identity and a set of personal values. Study 2 will focus on adults from a broader range of ages. In both studies, participants will complete an in-depth assessment of personality, social roles, values/worldview, and relevant control variables at four points in time, each separated by one year. This will allow the examination of whether changes in social roles or values precipitate shifts in personality traits or vice versa.

Broadly, this research seeks a more comprehensive understanding of the dynamics of personality traits: what makes them stable and what makes them change. While traits are partially rooted in genetic influences, they evidently develop through as yet poorly understood dynamic interactions between genetic and environmental processes. New models, then, are needed that more exactly specify how personality interacts with belief systems and with social environments, which can then direct research that examines how lower-level processes like gene expression and gene-environment interactions lead to important behavioral outcomes. The dynamic processes underlying personality, once identified, will point to transactional sources of change that can usefully inform and be incorporated into public policy and education. Among other possibilities, this might lead to targeted interventions to promote optimal development of key desirable psychological attributes, including civility, altruism, integrity, and achievement orientation.

? DESCRIPTION (provided by applicant): The last decade has seen explosive growth of online social networks (OSNs), which are now a pervasive part of everyday life for many people. Big data analyses of OSNs have enormous potential to enable new discoveries and applications in public health. For example we may soon be able to study how mental health indicators vary over time and between communities and regions; how major events like disasters and mass shootings affect community mental health; and potentially even use social media for individual-level screening and diagnosis. But in order to do this effectively, psychologists need to know how to make sense of vast amounts of digital records of raw behavior. That will require interdisciplinary collaborations between psychology and computer science to create new tools and methods for automated analyses of mental health variables in large and complex datasets. The goal of this project is to develop new metrics for doing large-scale, big data mental health research on Twitter, a popular online social network service with a large and diverse user base in the United States that makes most user-generated content publicly available. We are particularly interested in how Twitter data can be used to draw inferences about users' personality traits (which are indicative of risk and vulnerability), emotions, and clinical symptoms. To date, researchers studying mental health on Twitter have focused on a small number of metrics which often differ from study to study and are not always well-validated. We do not yet have a unified, comprehensive understanding of how mental health characteristics can be measured and studied online. Our project has three goals. First, we will gather data from a very large, representative sample of American Twitter users to map out what meaningful metrics can be derived from Twitter data. What can be reliably measured, and how are different kinds of metrics related to each other? By studying relationships and overlaps among these variables, we expect to identify a key set of online metrics that can be used to characterize important psychological differences among users. Second, we will recruit a sample of Twitter users to complete standardized assessments of personality, emotion, and clinical symptoms. We will look to see what kinds of online behaviors are reliable and valid markers of these psychological variables. Third, to demonstrate the utility of these markers we will monitor millions of Twitter accounts around the country for one year to measure how one specific kind of community trauma, mass shootings, affects indicators of personality, emotion, and mental health. We expect to find effects of such events on the online expression of emotion and clinical symptoms immediately following an event, and perhaps longer-term effects that endure beyond the immediate aftermath.

The last decade has seen explosive growth of online social networks, which are now a pervasive part of everyday life for many people. More and more, people encounter one another for the first time through social media and make important social decisions based on those encounters. Individuals make decisions about who to hire and who to trust based on the impressions others convey on-line. People may decide with whom they will exchange information, and can even decide with whom to pursue close personal relationships based solely on online encounters. Given the multitude of outcomes, people's online social behavior likely reflects their different goals about how they wish to be seen by others, in addition to their personality. Despite the importance of understanding how these issues operate in communication technologies, most research on self-presentational goals, personality traits, and impressions is based on traditional laboratory and survey methodologies. The goal of this project is to study how users' personalities and self-presentation goals are reflected in their social media presence, how people form accurate or biased impressions of one another online, and how people make consequential social decisions based on those impressions. The project will also produce important tools for other scientists. By combining the expertise of a personality psychologist with that of a computer scientist, the project will create new methods and techniques for doing large-scale, automated studies in social psychological science and personality psychology. The outcomes of this research will also inform policy discussions on online privacy and on the use of social media in hiring, in spreading news and information online, and other important social and economic transactions.

In their project, the investigators propose to merge "big data" methods from computer science with rigorous laboratory methods from psychology to study personality and reputation on Twitter, a popular online social network with a large and diverse user base in the United States. Five studies focus on a series of related questions about how people convey who they are through Twitter, and how others form impressions by observing such behavior. (1) Data-driven analyses will be used to identify patterns in Twitter users' publicly available information (including profile information, network size and structure, and tweet content) to determine what important stable attributes distinguish users. (2) A sample of Twitter users will complete validated psychometric assessments of personality and self-presentation goals to see how these are reflected in the users' public Twitter data. (3) Research will examine how others form personality impressions based on such Twitter profiles, and the ways those impressions are accurate or biased. (4) Studies will examine how others make important social decisions based on their accurate or inaccurate impressions of Twitter profiles. (5) Finally, research will examine how personality and impressions affect how people decide to affiliate and form online communities. This research will produce new scientific insights about how people express themselves and interact online. It will also generate new tools for assessing personality and reputation in online settings, enabling future "big data" studies in psychology.