World Happiness Report 2023 154 trends in socioeconomically matched counties can be compared to study the impact of specific events, such as pandemic lockdowns, large-scale unemployment, or natural disasters. The choice of spatiotemporal resolution. Social media data is particularly suitable for longitudinal designs since many people frequently engage with social media. For example, in the U.S., 38% of respondents reported interacting with others “once per day or more” through one of the top five social media platforms (this ranges from 19% in India to 59% in Brazil across seven countries).113 Even in research studies conducted by university research labs, sample sizes of more than 1% of the U.S. population are feasible (e.g., the County- Tweet Lexical Bank with 6.1 million Twitter users).114 In principle, such an abundance of data allows for high resolution in both space and time, such as estimates for county–weeks (see Fig. 5.9). The higher resolution can provide economists and policymakers with more fine-grained, reliable information that can be used for evaluating the impact of policies within a quasi-experimental framework. Enabling data linkage. Estimates at the county- month level also appear to be well-suited for data linkage with the population surveillance projects in population health (for example, the Office of National Drug Control Policy’s [ONDCP] Non-Fatal Opioid Overdose Tracker) and serve as suitable predictors of sensitive time-varying health outcomes, such as county-level changes in rates of low birth weights. The principled and stabilized estimation of county-level time series opens the door for social-media-based measurements to be integrated with the larger ecosystem of datasets designed to capture health and well-being. Forthcoming work: Well-being and mental health assessment in time and space Studies employing digital cohorts have only recently emerged (i.e., preliminary studies in preprints) related to tracking the opioid epidemic from social media. For example, some researchers (Gen 3, Level 1) use Reddit forum data to identify and follow more than 1.5 million individuals geolocated to a state and city to test relationships between discussion topics and changes in opioid mortality rate.115 Similarly, other researchers (Gen 3, Level 2) tracks opioid rates of a cohort of counties to predict future changes in opioid mortality rates. Albeit utilizing coarse-grained temporal resolutions (i.e., annual estimates), these works lay a foundation of within-person and within-community cohort designs that can be mirrored for well-being monitoring at scale.116 The field is on the verge of combining Gen 3 sampling and aggregation with Level 3 contextualized embedding-based language analyses (Gen 3, Level 3), which will provide state-of-theart resolutions and accuracies. Gen 3 digital cohort designs – Summary and Limitations The digital cohort approach comes with the advantages of the person-level approaches, as well as increased methodological design control Photo by Shingi Rice on Unsplash
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