Using Aggregated Relational Data to Feasibly Identify Network Structure without Network Data

American Economic Review · 2020 · 107 citations

• Computer Science
• Data Mining
• Computer Science

Social network data are often prohibitively expensive to collect, limiting empirical network research. We propose an inexpensive and feasible strategy for network elicitation using Aggregated Relational Data (ARD): responses to questions of the form "how many of your links have trait k ?" Our method uses ARD to recover parameters of a network formation model, which permits sampling from a distribution over node- or graph-level statistics. We replicate the results of two field experiments that used network data and draw similar conclusions with ARD alone.

Comparison of Knowledge and Information-Seeking Behavior After General COVID-19 Public Health Messages and Messages Tailored for Black and Latinx Communities

Annals of Internal Medicine · 2020 · 122 citations

• Information Retrieval
• Medicine
• Information Retrieval

BACKGROUND: The paucity of public health messages that directly address communities of color might contribute to racial and ethnic disparities in knowledge and behavior related to coronavirus disease 2019 (COVID-19). OBJECTIVE: To determine whether physician-delivered prevention messages affect knowledge and information-seeking behavior of Black and Latinx individuals and whether this differs according to the race/ethnicity of the physician and tailored content. DESIGN: Randomized controlled trial. (Registration: ClinicalTrials.gov, NCT04371419; American Economic Association RCT Registry, AEARCTR-0005789). SETTING: United States, 13 May 2020 to 26 May 2020. PARTICIPANTS: 14 267 self-identified Black or Latinx adults recruited via Lucid survey platform. INTERVENTION: Participants viewed 3 video messages regarding COVID-19 that varied by physician race/ethnicity, acknowledgment of racism/inequality, and community perceptions of mask wearing. MEASUREMENTS: Knowledge gaps (number of errors on 7 facts on COVID-19 symptoms and prevention) and information-seeking behavior (number of web links demanded out of 10 proposed). RESULTS: 7174 Black (61.3%) and 4520 Latinx (38.7%) participants were included in the analysis. The intervention reduced the knowledge gap incidence from 0.085 to 0.065 (incidence rate ratio [IRR], 0.737 [95% CI, 0.600 to 0.874]) but did not significantly change information-seeking incidence. For Black participants, messages from race/ethnicity-concordant physicians increased information-seeking incidence from 0.329 (for discordant physicians) to 0.357 (IRR, 1.085 [CI, 1.026 to 1.145]). LIMITATIONS: Participants' behavior was not directly observed, outcomes were measured immediately postintervention in May 2020, and online recruitment may not be representative. CONCLUSION: Physician-delivered messages increased knowledge of COVID-19 symptoms and prevention methods for Black and Latinx respondents. The desire for additional information increased with race-concordant messages for Black but not Latinx respondents. Other tailoring of the content did not make a significant difference. PRIMARY FUNDING SOURCE: National Science Foundation; Massachusetts General Hospital; and National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.