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One way to assess a certain aspect of the value of scientific research is tomeasure the attention it receives on social media. While previous research hasmostly focused on the "number of mentions" of scientific research on socialmedia, the current study applies "topic networks" to measure public attentionto scientific research on Twitter. Topic networks are the networks ofco-occurring author keywords in scholarly publications and networks ofco-occurring hashtags in the tweets mentioning those scholarly publications.This study investigates which topics in opioid scholarly publications havereceived public attention on Twitter. Additionally, it investigates whether thetopic networks generated from the publications tweeted by all accounts (bot andnon-bot accounts) differ from those generated by non-bot accounts. Our analysisis based on a set of opioid scholarly publications from 2011 to 2019 and thetweets associated with them. We use co-occurrence network analysis to generatetopic networks. Results indicated that Twitter users have mostly used genericterms to discuss opioid publications, such as "Opioid," "Pain," "Addiction,""Treatment," "Analgesics," "Abuse," "Overdose," and "Disorders." Resultsconfirm that topic networks provide a legitimate method to visualize publicdiscussions of health-related scholarly publications and how Twitter usersdiscuss health-related scientific research differently from the scientificcommunity. There was a substantial overlap between the topic networks based onthe tweets by all accounts and non-bot accounts. This result indicates that itmight not be necessary to exclude bot accounts for generating topic networks asthey have a negligible impact on the results.

Investigating Dissemination of Scientific Information on Twitter: A Study of Topic Networks in Opioid Publications