Does Scientific Evidence Sell? Combining Manual and Automated Content Analysis to Investigate Scientists’ and Laypeople’s Evidence Practices on Social Media
Kaija Biermann, Bianca Nowak, Lea-Marie Braun, Monika Taddicken, Nicole C. Krämer, Stefan Stieglitz
{"title":"Does Scientific Evidence Sell? Combining Manual and Automated Content Analysis to Investigate Scientists’ and Laypeople’s Evidence Practices on Social Media","authors":"Kaija Biermann, Bianca Nowak, Lea-Marie Braun, Monika Taddicken, Nicole C. Krämer, Stefan Stieglitz","doi":"10.1177/10755470241249468","DOIUrl":null,"url":null,"abstract":"Examining the dissemination of evidence on social media, we analyzed the discourse around eight visible scientists in the context of COVID-19. Using manual ( N = 1,406) and automated coding ( N = 42,640) on an account-based tracked Twitter/X dataset capturing scientists’ activities and eliciting reactions over six 2-week periods, we found that visible scientists’ tweets included more scientific evidence. However, public reactions contained more anecdotal evidence. Findings indicate that evidence can be a message characteristic leading to greater tweet dissemination. Implications for scientists, including explicitly incorporating scientific evidence in their communication and examining evidence in science communication research, are discussed.","PeriodicalId":47828,"journal":{"name":"Science Communication","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Science Communication","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1177/10755470241249468","RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMMUNICATION","Score":null,"Total":0}
引用次数: 0
Abstract
Examining the dissemination of evidence on social media, we analyzed the discourse around eight visible scientists in the context of COVID-19. Using manual ( N = 1,406) and automated coding ( N = 42,640) on an account-based tracked Twitter/X dataset capturing scientists’ activities and eliciting reactions over six 2-week periods, we found that visible scientists’ tweets included more scientific evidence. However, public reactions contained more anecdotal evidence. Findings indicate that evidence can be a message characteristic leading to greater tweet dissemination. Implications for scientists, including explicitly incorporating scientific evidence in their communication and examining evidence in science communication research, are discussed.
期刊介绍:
Science Communication is a prestigious journal that focuses on communication research. It is recognized globally for publishing top-quality manuscripts that demonstrate excellent theoretical frameworks and robust methodology. Our journal embraces a broad definition of science, encompassing not only the natural and physical sciences but also social science, technology, environment, engineering, and health. Regardless of the scientific area, effective communication is always the focal point of our investigations.
Apart from theoretical and methodological rigor, we place great emphasis on the practical implications of scientific communication. Therefore, we expect all submitted manuscripts to address the real-world applications and significance of their research, alongside theoretical considerations.
In summary, Science Communication is an internationally renowned journal dedicated to bridging the gap between science and society. By promoting effective communication in various scientific domains, we strive to engage readers with intriguing research that has tangible implications for the world around us.