  One way to assess the value of scientific research is to measure the
attention it receives on social media. While previous research has mostly
focused on the "number of mentions" of scientific research on social media, the
current study uses "topic networks" to measure public attention to scientific
research on Twitter. Topic networks in this research are the "co-occurring
author keywords" in scientific publications and the "co-occurring hashtags" in
the tweets mentioning scientific publications. Since bots (automated social
media accounts) may significantly influence public attention, this study also
investigates whether the topic networks based on the tweets by all accounts
(bot and non-bot accounts) differ from the topic networks by non-bot accounts.
Our analysis is based on a set of opioid scientific publications from 2011 to
2019 and the tweets associated with them. We use co-occurrence network analysis
to generate topic networks. Results indicated that the public has mostly used
generic terms to discuss opioid publications. Results confirmed that topic
networks provide a legitimate method to visualize public discussions of
(health-related) scientific publications, and how the public discusses
(health-related) scientific research differently from the scientific community.
There was a significant overlap between the topic networks based on the tweets
by all accounts and non-bot accounts. This result indicates that in generating
topic networks, bot accounts do not need to be excluded as they have negligible
impact on the results.
