This exploratory work investigates the role of digital media in expanding health discourse practices in a way to transform traditional structures of agency in public health. By focusing on a sample of rare disease patient organisations as representative of contemporary health activism, this study investigates the role of digital communication in the development of (1) bottom-up sharing and co-production of health knowledge, (2) health public engagement dynamics and (3) health information pathways. Findings show that digital media affordances for patient organisations go beyond the provision of social support for patient communities; they ease one-way, two-way and crowdsourced processes of health knowledge sharing, exchange and co-production, provide personalised routes to health public engagement and bolster the emergence of varied pathways to health information where experiential knowledge and medical authority are equally valued. These forms of organisationally enabled connective action can help the surfacing of personal narratives that strengthen patient communities, the bottom-up production of health knowledge relevant to a wider public and the development of an informational and eventually cultural context that eases patients' political action.

Insufficient sleep is a growing health problem among University students, especially for freshmen during their first quarter/semester of college. Little research has studied how social media technologies impact sleep quality among college students. This study aims to determine the relationship between social media use and sleep quality among freshman undergraduates during their first quarter in college. Specifically, we explored whether variations in Twitter use across the time of day and day of the week would be associated with self-reported sleep quality. We conducted a study of Freshman Twitter-using students (N = 197) over their first quarter of college, between October to December of 2015. We collected students' tweets, labeled the content of the tweets according to different emotional states, and gave theme weekly surveys on sleep quality. Tweeting more frequently on weekday late nights was associated with lower sleep quality (β = -0.937, SE = 0.352); tweeting more frequently on weekday evenings was associated with better quality sleep (β = 0.189, SE = 0.097). Tweets during the weekday that were labeled related to the emotion of fear were associated with lower sleep quality (β = -0.302, SE = 0.131). Results suggest that social media use is associated with sleep quality among students. Results provide can be used to inform future interventions to improve sleep quality among college students.

Environmental data justice (EDJ) emerges from conversations between data justice and environmental justice while identifying the limits and tensions of these lenses. Through a reflexive process of querying our entanglement in non-innocent relations, this paper develops and engages EDJ by examining how it informs the work of the Environmental Data & Governance Initiative (EDGI), a distributed, consensus-based organization that formed in response to the 2016 US presidential election. Through grassroots archiving of data sets, monitoring federal environmental and energy agency websites, and writing rapid-response reports about how federal agencies are being undermined, EDGI mobilises EDJ to challenge the 'extractive logic' of current federal environmental policy and data infrastructures. 'Extractive logic' disconnects data from provenance, privileges the matrix of domination, and whitewashes data to generate uncertainty. We use the dynamic EDJ framework to reflect on EDGI's public comment advising against the US Environmental Protection Agency's proposed rule for Transparent Science. Through EDJ, EDGI aspires to create new environmental data infrastructures and practices that are participatory and embody equitable, transparent data care.

Social media data are increasingly used by researchers to gain insights on individuals' behaviors and opinions. Platforms like Twitter provide access to individuals' postings, networks of friends and followers, and the content to which they are exposed. This article presents the methods and results of an exploratory study to supplement survey data with respondents' Twitter postings, networks of Twitter friends and followers, and information to which they were exposed about e-cigarettes. Twitter use is important to consider in e-cigarette research and other topics influenced by online information sharing and exposure. Further, Twitter metadata provide direct measures of user's friends and followers as opposed to survey self-reports. We find that Twitter metadata provide similar information to survey questions on Twitter network size without inducing recall error or other measurement issues. Using sentiment coding and machine learning methods, we find Twitter can elucidate on topics difficult to measure via surveys such as online expressed opinions and network composition. We present and discuss models predicting whether respondents' tweet positively about e-cigarettes using survey and Twitter data, finding the combined data to provide broader measures than either source alone.

Whilst data-driven strategies are allegedly prevalent in political campaigns, evidence regarding their actual effectiveness is scarce. This study investigates, from an individual perspective, the effect of issue congruency in political ads on immediate responses and voting behaviors. To reach our goal, we combined different types of data collection: mobile experience sampling method (mESM), panel survey, and content analysis. The combined approach allowed us to effectively study targeted ads within the cross-device and cross-platform environment. The results showed that voters perceive online political ads that are about a topic that they care about as more interesting, informative, and persuasive regardless of their partisanship. This positive ad perception subsequently leads to a higher probability of voting for the promoted party in the ad. We also found that an ad discussing a topic in line with the receiver's concerns positively affects the evaluation of the promoted party in the ad only when the party is already favored by the voter. Taken together, this study provides insights into the conditional effectiveness of data-driven strategies in political campaigns.