Social media content is one of the most visible sources of big data and is often used in health studies to draw inferences about various behaviors. Though much can be gleaned from social media data and mining, the approaches used to collect and analyze data are generally strengthened when examined through established theoretical frameworks. Health behavior, a theory driven field, encourages interdisciplinary collaboration across fields and theories to help us draw robust conclusions about phenomena. This pilot study uses a combined computer informatics and SNA approach to analyze information spread about mask-wearing as a personal mitigation effort during the COVID-19 pandemic. We analyzed one week’s worth of Twitter data (n = 10,107 tweets across 4,289 users) by using at least one of four popular mask-support hashtags (e.g., #maskup). We calculated network-measures to assess structures and patterns present within the Twitter network, and used exponential random graph modeling (ERGM) to test factors related to the presence of retweets between users. The pro-mask Twitter network was largely fragmented, with a select few nodes occupying the most influential positions in the network. Verified accounts, accounts with more followers, and those who generated more tweets were more likely to be retweeted. Contrarily, verified accounts and those with more followers were less likely to retweet others. SNA revealed patterns and structures theoretically important to how information spreads across Twitter. We demonstrated the utility of an interdisciplinary collaboration between computer informatics and SNA to draw conclusions from social media data.

Creative Commons License

Creative Commons Attribution-Noncommercial 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License