This study examines temporal trends, geographic distribution, and demographic correlates of anti-vaccine beliefs on Twitter, 2009–2015. A total of 549,972 tweets were downloaded and coded for the presence of anti-vaccine beliefs through a machine learning algorithm. Tweets with self-disclosed geographic information were resolved and United States Census data were collected for corresponding areas at the micropolitan/metropolitan level. Trends in number of anti-vaccine tweets were examined at the national and state levels over time. A least absolute shrinkage and selection operator regression model was used to determine census variables that were correlated with anti-vaccination tweet volume. Fifty percent of our sample of 549,972 tweets collected between 2009 and 2015 contained anti-vaccine beliefs. Anti-vaccine tweet volume increased after vaccine-related news coverage. California, Connecticut, Massachusetts, New York, and Pennsylvania had anti-vaccination tweet volume that deviated from the national average. Demographic characteristics explained 67% of variance in geographic clustering of anti-vaccine tweets, which were associated with a larger population and higher concentrations of women who recently gave birth, households with high income levels, men aged 40 to 44, and men with minimal college education. Monitoring anti-vaccination beliefs on Twitter can uncover vaccine-related concerns and misconceptions, serve as an indicator of shifts in public opinion, and equip pediatricians to refute anti-vaccine arguments. Real-time interventions are needed to counter anti-vaccination beliefs online. Identifying clusters of anti-vaccination beliefs can help public health professionals disseminate targeted/tailored interventions to geographic locations and demographic sectors of the population.

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Social support is a widely studied construct due to its associations with physical and emotional well-being outcomes (Uchino, 2006). However, little research examines the context within which receiving support may be helpful (Picard, Lee, & Hunsley, 1997). Whereas examinations of support adequacy are present in the literature (e.g., Song et al., 2012), limited research considers the difference between support needs and support received when the 2 are separated as distinct constructs. The current study consisted of 428 undergraduate college students and examined how the relation between social support needs and received social support relates to depressive and anxiety symptoms via a statistical approach suggested for need-actual discrepancy analysis (polynomial multiple regression, PMR, with response surface analysis; Edwards, 1994; Shanock, Baran, Gentry, Pattison, & Heggestad, 2010). Results indicated that greater discrepancy between needed support and received support was related to greater depressive, but not anxiety, symptoms. Specifically, when emotional support needs exceeded emotional support received, depressive symptoms tended to be highest. Moreover, perceptions of needed support were significantly greater than perceptions of received support, suggesting that college students in general perceive receiving less support than they need, and this discrepancy is related to greater depressive symptoms. (PsycINFO Database Record

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