  One way to assess a certain aspect of 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 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 scholarly publications.
This study investigates which topics in opioid scholarly publications have
received public attention on Twitter. Additionally, it investigates whether the
topic networks generated from the publications tweeted by all accounts (bot and
non-bot accounts) differ from those generated by non-bot accounts. Our analysis
is based on a set of opioid scholarly publications from 2011 to 2019 and the
tweets associated with them. We use co-occurrence network analysis to generate
topic networks. Results indicated that Twitter users have mostly used generic
terms to discuss opioid publications, such as "Opioid," "Pain," "Addiction,"
"Treatment," "Analgesics," "Abuse," "Overdose," and "Disorders." Results
confirm that topic networks provide a legitimate method to visualize public
discussions of health-related scholarly publications and how Twitter users
discuss health-related scientific research differently from the scientific
community. There was a substantial overlap between the topic networks based on
the tweets by all accounts and non-bot 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.
