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.

Societal impact considerations play an increasingly important role in research evaluation. In particular, in the context of publicly funded research, proposal templates commonly include sections to outline strategies for achieving broader impact. Both the assessment of the strategies and the later evaluation of their success are associated with challenges in their own right. Ever since their introduction, altmetrics have been discussed as a remedy for assessing the societal impact of research output. On the basis of data from a research center in Switzerland, this study explores their potential for this purpose. The study is based on the papers (and the corresponding metrics) published by about 200 either accepted or rejected applicants for funding by the Competence Center Environment and Sustainability (CCES). The results of the study seem to indicate that altmetrics are not suitable for reflecting the societal impact of research that was considered: The metrics do not correlate with the ex ante considerations of an expert panel.

Citations can be used in evaluative bibliometrics to measure the impact of papers. However, citation analysis can be extended by considering a multidimensional perspective on citation impact which is intended to receive more specific information about the kind of received impact. Bornmann, Wray, and Haunschild (2020) introduced the citation concept analysis (CCA) for capturing the importance and usefulness certain concepts (explained in publications) have in subsequent research. In this paper, we apply the method by investigating the impact various concepts introduced in Robert K. Merton’s book Social Theory and Social Structure has had. This book was to lay down a manifesto for sociological analysis in the immediate postwar period, and retains a major impact 70 years later. We found that the most cited concepts are “self-fulfilling” and “role” (about 20% of the citation contexts are related to one of these concepts). The concept “self-fulfilling” seems to be important especially in computer sciences and psychology. For “role,” this seems to be additionally the case for political sciences. These and further results of the study could demonstrate the high explanatory power of the CCA method.