Investigating dissemination of scientific information on Twitter: A study of topic networks in opioid publications

Abstract

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. 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 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.” 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. This study provided some preliminary evidence that scholarly publications have a network agenda-setting effect on Twitter.

Bot, or not? Comparing three methods for detecting social bots in five political discourses

“…Despite the rapid growth of available bot detection tools and methods, these methods featured prominently in the literature in the past years, recently, e.g. Boichak et al (2021), Abrahams and Leber (2021) or Haunschild et al (2021), with almost all studies using only one bot detection method without assessing discrepancies between methods. Thus, we seek to compare different bot detection methods by testing them against each other on the same data set from five political discourses in five Western democracies.…”

“…Hindman and Barash (2018) used Tweetbotornot to analyse the role of bots in disinformation campaigns in the US, finding that most accounts spreading fake or conspiracy news were probably bots or semi-automated accounts. Haunschild et al (2021) analyse topic networks in the context of scientific knowledge diffusion on Twitter with this tool. Similar to Botometer, Tweetbotornot is based on supervised machine learning (gradient boosted model) and assesses the probability of a Twitter account being a bot, delivering a score between 0 and 1 for each account that is tested.…”

SciTweets -- A Dataset and Annotation Framework for Detecting Scientific Online Discourse

“…This has led to research into scientific online discourse across various disciplines. From a social sciences perspective, works measure the engagement with scientific publications on social media [5,6,12] or investigate the role of social media in facilitating the flow of scientific information [3]. In science communication, research discusses implications of risk communication [16] or the spreading pattern associated with preliminary scientific results and the diffusion of science through social networks [17,30], while research in cognitive and social psychology investigates the perceived trustworthiness of scientific online discourse [15].…”