Genetics and genetic data have been the subject of recent scholarly work, with significant attention paid towards understanding consent practices for the acquisition and usage of genetic data as well as genetic data security. Attitudes and perceptions concerning the trustworthiness of governmental institutions receiving test-taker data have been explored, with varied findings, but no robust models or deterministic relationships have been established that account for these differences. These results also do not explore in detail the perceptions regarding other types of organizations (e.g., private corporations). Further, considerations of privacy interdependence arising from blood relative relationships have been absent from the conversation regarding the sharing of genetic data. This paper reports the results from a factorial vignette survey study in which we investigate how variables of ethnicity, age, genetic markers, and association of data with the individual's name affect the likelihood of sharing data with different types of organizations. We also investigate elements of personal and interdependent privacy concerns. We document the significant role these factors have in the decision to share or not share genetic data. We support our findings with a series of regression analyses.
Opportunities to collect real-time social media data during a crisis remain limited to location and keyword filtering despite the sparsity of geographic metadata and the tendency of keyword-based methods to capture information posted by remote rather than local users. Here we introduce a third, network filtering method that uses social network ties to infer the location of social media users in a geographic community and collect data from networks of these users during a crisis. In this paper we compare all three methods by analysing the distribution of situational reports of infrastructure damage and service disruption across location, keyword, and network-filtered social media data during a weather emergency. We find that network filtering doubles the number of situational reports collected in real-time compared to location and keyword filtering alone, but that all three methods collect unique reports that can support situational awareness of incidents occurring across a community.
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