Scott Counts

TL;DR: It is found that social media contains useful signals for characterizing the onset of depression in individuals, as measured through decrease in social activity, raised negative affect, highly clustered egonetworks, heightened relational and medicinal concerns, and greater expression of religious involvement.

TL;DR: It is shown that users are poor judges of truthfulness based on content alone, and instead are influenced by heuristics such as user name when making credibility assessments.

TL;DR: A social media depression index is introduced that may serve to characterize levels of depression in populations and confirm psychiatric findings and correlate highly with depression statistics reported by the Centers for Disease Control and Prevention (CDC).

TL;DR: Results of network analyses of information diffusion on Twitter are presented, via users’ ongoing social interactions as denoted by “@username” mentions, finding that some properties of the tweets themselves predict greater information propagation but that property of the users, the rate with which a user is mentioned historically in particular, are equal or stronger predictors.

TL;DR: The opportunity to use social media to identify mothers at risk of postpartum depression, an underreported health concern among large populations, and to inform the design of low-cost, privacy-sensitive early-warning systems and intervention programs aimed at promoting wellness post partum is motivated by the opportunity.