We developed computational models to predict the emergence of depression and Post-Traumatic Stress Disorder in Twitter users. Twitter data and details of depression history were collected from 204 individuals (105 depressed, 99 healthy). We extracted predictive features measuring affect, linguistic style, and context from participant tweets (N = 279,951) and built models using these features with supervised learning algorithms. Resulting models successfully discriminated between depressed and healthy content, and compared favorably to general practitioners’ average success rates in diagnosing depression, albeit in a separate population. Results held even when the analysis was restricted to content posted before first depression diagnosis. State-space temporal analysis suggests that onset of depression may be detectable from Twitter data several months prior to diagnosis. Predictive results were replicated with a separate sample of individuals diagnosed with PTSD (Nusers = 174, Ntweets = 243,775). A state-space time series model revealed indicators of PTSD almost immediately post-trauma, often many months prior to clinical diagnosis. These methods suggest a data-driven, predictive approach for early screening and detection of mental illness.
How does cognitive diversity in a group affect its performance? Prior research suggests that group cognitive diversity poses a performance tradeoff: Diverse groups excel at creativity and innovation, but struggle to take coordinated action. Building on the insight that group cognition is not static, but is instead dynamically and interactively produced, we introduce the construct of discursive diversity, a manifestation of group cognitive diversity, which reflects the degree to which the meanings conveyed by group members in a given set of interactions diverge from one another. We propose that high-performing teams are ones that have a collective capacity to modulate shared cognition to match changing task requirements: They exhibit higher discursive diversity when engaged in ideational tasks and lower discursive diversity when performing coordination tasks. We further argue that teams exhibiting congruent modulation—that is, those with low group-level variance in members’ within-person semantic shifts to changing task requirements—are more likely to experience success than teams characterized by incongruent modulation. Using the tools of computational linguistics to derive a measure of discursive diversity and drawing on a novel longitudinal data set of intragroup electronic communications and performance outcomes for 117 remote software development teams on an online platform ( www.gigster.com ), we find support for our theory. Our findings suggest that the performance tradeoff of group cognitive diversity is not inescapable: Groups can navigate it by aligning their levels of discursive diversity to match their task requirements and by having members stay aligned with one another as they make these adjustments. This paper was accepted by Isabel Fernandez-Mateo, organizations.
How does cognitive diversity in a group affect its performance? Prior research suggests that cognitive diversity poses a performance tradeoff: diverse groups excel at creativity and innovation but struggle to take coordinated action. Building on the insight that group cognition is not static but is instead dynamically and interactively produced, we develop a novel conceptualization of group cognitive diversity—discursive diversity, or the degree to which the semantic meanings expressed by group members diverge from one another at a given point in time. We propose that the relationship between this time-varying measure of group cognition and team performance varies as a function of task type: discursive diversity enhances performance when groups are engaged in ideational tasks but impedes performance when they perform coordination tasks. Using the tools of computational linguistics to derive a measure of discursive diversity, and drawing on a novel longitudinal data set of intragroup electronic communications, group members’ demographic traits, and performance outcomes for 117 remote software development teams on an online platform (Gigster), we find support for our theory. These results suggest that the performance tradeoff of group cognitive diversity is not inescapable: Groups can circumvent it by modulating discursive diversity to match their task requirements.
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