While previous research has mostly focused on the “number of mentions” of scientific research on social media, the current study applies “topic networks” to measure public attention to scientific research on Twitter. Topic networks are the networks of co-occurring author keywords in scholarly publications and networks of co-occurring hashtags in the tweets mentioning those publications. We investigate which topics in opioid scholarly publications have received public attention on Twitter. Additionally, we investigate whether the topic networks generated from the publications tweeted by all accounts (bot and nonbot accounts) differ from those generated by nonbot accounts. Our analysis is based on a set of opioid publications from 2011 to 2019 and the tweets associated with them. Results indicated that Twitter users have mostly used generic terms to discuss opioid publications, such as “Pain,” “Addiction,” “Analgesics,” “Abuse,” “Overdose,” and “Disorders.” A considerable amount of tweets is produced by accounts that were identified as automated social media accounts, known as bots . There was a substantial overlap between the topic networks based on the tweets by all accounts (bot and nonbot accounts). This result indicates that it might not be necessary to exclude bot accounts for generating topic networks as they have a negligible impact on the results. This study provided some preliminary evidence that scholarly publications have a network agenda-setting effect on Twitter.