Dissemination of health information through social networks: Twitter and antibiotics
By Daniel Scanfeld, Vanessa Scanfeld, Elaine L. Larson. American Journal of Infection Control, 2010
714 citations recorded by the source corpus.
Background This study reviewed Twitter status updates mentioning “antibiotic(s)” to determine overarching categories and explore evidence of misunderstanding or misuse of antibiotics. Methods One thousand Twitter status updates mentioning antibiotic(s) were randomly selected for content analysis and categorization. To explore cases of potential misunderstanding or misuse, these status updates were mined for co-occurrence of the following terms: “cold + antibiotic(s),” “extra antibiotic(s),” “flu + antibiotic(s),” “leftover antibiotic(s),” and “share antibiotic(s)” and reviewed to confirm evidence of misuse or misunderstanding. Results Of the 1,000 status updates, 971 were categorized into 11 groups: General Use (n=289), Advice/Information (n=157), Side Effects/Negative Reactions (n=113), Diagnosis (n=102), Resistance (n=92), Misunderstanding and/or Misuse (n=55), Positive Reactions (n=48), Animals (n=46), Other (n=42), Wanting/Needing (n=19), and Cost (n=8). Cases of misunderstanding or abuse were identified for the following combinations: “flu + antibiotic(s)” (n=345), “cold + antibiotic(s)” (n=302), “leftover antibiotic(s)” (n=23), “share antibiotic(s)” (n=10), and “extra antibiotic(s)” (n=7). Conclusions Social media sites offer means of health information sharing. Further study is warranted to explore how such networks may provide a venue to identify misuse or misunderstanding of antibiotics, promote positive behavior change, disseminate valid information, and explore how such tools can be used to gather real-time health data.
Subfields: Applied Microbiology and Biotechnology, Health, Sociology and Political Science
View Paper: https://www.semanticscholar.org/paper/51cfcada534282f0abc9eafbad8dd7d2d723b5ba
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In Health
- Mark Dredze
- Michael J. Paul
- Ceren Budak
- Emily K. Vraga
- Leticia Bode
- M. de Choudhury
- Emre Kıcıman
- Glen A. Coppersmith
In Sociology and Political Science
- Emily K. Vraga
- Leticia Bode
- Michael J. Paul
- Ceren Budak
- Mark Dredze
- Elmie Nekmat
- Jing Jiang
- Mohammad Rostami
Citations in the corpus
- Using Twitter to Examine Smoking Behavior and Perceptions of Emerging Tobacco Products (2013, background): “In a related vein, Twitter surveillance has revealed evidence of poor public informedness and misuse of antibiotics [11].”
- Systematic Literature Review on the Spread of Health-related Misinformation on Social Media (2019, background): “This is perhaps surprising, as health promotion and public health researchers now pay considerable attention to the potential of the internet as a tool to diffuse health-related information (Chew and Eysenbach, 2010; Ritterband and Tate, 2009; Murray et al., 2009; Scanfeld et al., 2010; Signorini et”
- A first look at COVID-19 information and misinformation sharing on Twitter (2020, background): “For instance, a 2010 study by [30] examined 1000 randomly selected tweets mentioning antibiotics and found that 700 of them contained medical misinformation or malpractice.”
- Crowdsourcing for climate and atmospheric sciences: current status and future potential (2015, positive): “Furthermore, social media feeds often generate a lot of ‘noise’ and invalid information (Scanfeld et al., 2010), which can result in biased information being amplified through the viral nature of social media misinformation (Boulos et al.”
- A New Dimension of Health Care: Systematic Review of the Uses, Benefits, and Limitations of Social Media for Health Communication (2013, background): “The 98 selected studies are summarized by study design, social media tool/application, study purpose, participants/sample and sample size, measurement tools, results, conclusion, and use of social media in Multimedia Appendix 2 [24-121].”
- You Are What You Tweet: Analyzing Twitter for Public Health (2011, background): “Finally, similar to Scanfeld, Scanfeld , and Larson (2010), we found that while the most common usage of antibiotics was correctly for infection (68”
- Twitter Discussions and Emotions About the COVID-19 Pandemic: Machine Learning Approach (2020, background): “The widespread use of social media, such as Twitter, accelerates the process of exchanging information and expressing opinions about public events and health crises [2-5].”