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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Am J Infect Control. Author manuscript; available in PMC 2013 March 19.
Published in final edited form as:
PMCID: PMC3601456

Dissemination of health information through social networks: Twitter and antibiotics

Daniel Scanfeld, Ph.D. Candidate, M.S., M.A., B.A.,corresponding author Vanessa Scanfeld, M.P.P., B.A., and Elaine L. Larson, R.N., Ph.D., M.S., B.S.



This study reviewed Twitter status updates mentioning “antibiotic(s)” to determine overarching categories and explore evidence of misunderstanding or misuse of antibiotics.


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.


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).


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.


First coined in 2004, “web 2.0” describes a change in the way people interact with information online, moving from passive consumption to active creation of content. Web 2.0 software harnesses network effects and knowledge in an open, interactive manner [1]. It is now common to participate in social networking communities (e.g. Facebook), social rating websites (e.g. Digg), customer review websites (e.g. Yelp), photo and video sharing networks (e.g. Flickr and YouTube), blogs (e.g. Huffington Post), and information aggregators (e.g. Wikipedia). These platforms enable people to share their knowledge and experience, creating a rich array of user-generated content [2]. Within this new construct, one’s peers are an important source of information, with 47% of respondents in one survey reporting the information they receive from “a person like me” to be extremely or very credible, on par with their trust of industry experts [3]. See Table 1: Social Media Tools, Definitions & Examples.

Table 1
Social Media Tools, Definitions & Examples

Not surprisingly, web 2.0 trends have extended to the health care arena, as those seeking health information online began disseminating their experiences and knowledge [4]. Collectively referred to as “medicine 2.0” or “health 2.0,” these trends are broadly defined as “the use of a specific set of Web tools (blogs, Podcasts, tagging, search, wikis, etc.) by actors in health care including doctors, patients, and scientists, using principles of open source and generation of content by users, and the power of networks in order to personalize health care, collaborate, and promote health education” [1]. A Pew study recently reported that 61% of American adults seek health information online and 37% have accessed user-generated health information online. Sixty percent of e-patients (internet users that have looked online for health information) reported that online inquiries had an impact on their health decisions. Forty-two percent of all adults say that they or someone they know has been helped by following medical advice or health information found on the internet, a 43% increase since 2006; only 3% of all adults report that they or someone they know has been harmed [5].

The purpose of this paper is to report a content analysis of the large microblog, Twitter, to determine the main categories of content contained in Twitter users’ status updates mentioning antibiotics and to explore cases of misunderstanding and misuse of antibiotics that might inform potential interventions and information campaigns.



We conducted a cross-sectional survey using content analysis of Twitter status updates between March 13, 2009 and July 31, 2009.

Study Setting

The observational study applied content analysis to publicly available Twitter Web pages using the Web site Twitter updates may be public, meaning content is viewable to any Web site visitor, or protected, meaning updates are not available on a public search and can only be viewed by approved users. Individuals can optionally identify their location, a one-line biography, and language. Twitter also tracks and makes publicly available the number of people following the user; the number of people the user is following; and the number of status updates (“tweets”) the person has posted. Only publicly available data were used in this analysis. As of August 2009, approximately 94% of Twitter accounts were public, with that number trending upward [6]. See Table 2: Dictionary of Twitter-related terms.

Table 2
Dictionary of Twitter-related terms

Search Strategy

Twitter features a search function to filter status updates that meet particular search criteria. Each individual search returns results from a variable timeframe (approximately one week), depending on the storage capacity of Twitter’s database. We included publicly available updates on Twitter that mentioned antibiotic(s) in English. No restrictions were placed on location or age. Twitter does not track age, but registrants to the service must certify that they are 13 years of age or older. We conducted two searches, one for “antibiotic” and one for “antibiotics,” on a weekly basis from March 13 to July 31, 2009. The combined results yielded a list of 52,153 status updates mentioning at least one of the search terms.

Determining Categories

We applied Q-methodology [7], to categorize the status updates. We downloaded an initial set of updates that mentioned 'antibiotic' or 'antibiotics' and then generated a list of 100 random updates intended to be sufficiently representative of the universe of antibiotic-related content expressed on Twitter [8]. Three researchers separately evaluated the updates and inductively classified them into broad categories. The categories were then discussed to reach consensus and 11 categories were initially determined: Advertisements, Advice/Information, Animals, Cost, Diagnosis, General Use, Other, Positive Feedback, Resistance, Side Effects/Negative Reactions, and Wanting/Needing. This process was then repeated, applying the 11 categories to two new sets of 100 randomly selected status updates to ensure that the categories were sufficient and to further clarify category definitions.

Based on this process, another category, labeled “Misunderstanding and/or Misuse,” was added to replace and subsume “Advertisements,” given that all examples of advertisements were from illegitimate sources. With this new categorization, legitimate advertisements would have been categorized as “Advice/Information.” The “Misunderstanding and/or Abuse” category was utilized in the following specific cases: (1) references to using antibiotics to treat a virus, cold, or flu; (2) attempts to access or sell antibiotics without a prescription; (3) incorrect use; and (4) refusal to take antibiotics under any circumstances. Therefore, when a status update provided advice or information, a specific subset of those updates was categorized as “Misunderstanding and/or Abuse.” The rest were categorized as “Advice/Information,” including cases when the advice might be incorrect.

Data Collection

Once categories were established, 1,000 status updates were selected from the complete list of 52,153 status updates mentioning antibiotic(s) using the RC4 method for randomization [9]. Each status update was grouped into a single category by two researchers, with each reviewing one half of the data set. Areas of ambiguity were discussed and agreed upon by consensus. Status updates that used the term “antibiotic(s)” metaphorically (e.g., “Turning benign symbiotes into vicious, antibiotic resistant bacteria dedicated to film industry destruction”) were removed. Some status updates included links; for cases in which content was ambiguous, the link was checked to clarify the user’s intent. To assess inter-rater reliability, we re-categorized a random sample of 10% of the status updates. The Cohen’s κ statistic was used to measure the extent to which there was agreement in the categorization of status updates and determined a value of 0.73.

We further mined the total list of 52,153 for updates that included the following phrases: “cold + antibiotic(s),” “extra antibiotic(s),” “flu + antibiotic(s),” “leftover antibiotic(s),” and “share antibiotic(s).” Each update was reviewed for indication of misunderstanding and/or misuse of antibiotics. Updates that resulted from the search but which did not indicate misunderstanding or misuse were not included.

We also recorded the number of followers for each Twitter user, the number of people the user was following, and the number of status updates the user had submitted, all standard information on a Twitter page. No attempts were made to contact individuals or obtain access to information set as private.


Of the 1000 status updates, 29 were removed because they treated the word “antibiotic(s)” as a metaphor. For an overview of the resulting categorization, see Table 3: Antibiotic-Related Categories, Definitions, Examples, and Frequencies from Twitter Status Updates.

Table 3
Antibiotic-Related Categories, Definitions, Examples, and Frequencies from Twitter Status Updates

The most common category was “general use,” including a range of updates about taking antibiotics, often simply mentioning the number of days remaining on a prescription and a desire that the antibiotics begin helping soon. The second most common category was “advice and information.” While some updates simply included the transfer of personal advice or information, such as “get antibiotics if its [sic] serious” or “Garlic generally good, but not specific to strep. Walmart pharmacy will have antibiotics…,” many referenced news articles about antibiotics and included a link, such as this: “Antibiotic delayed aging in mice: Why do mice have all the fun?”

The third most prevalent category was “side effects/negative reactions,” which included a variety of complaints and side effects from taking the medication. Examples of side effects ranged from the general, such as, “those antibiotics made me want to die,” to the more specific, “I am on antibiotics that make me want to vomit.” Negative reactions generally revolved around inconveniences, such as not being able to drink alcohol or sensitivity to the sun.

Figure 1: Word Cloud is a visual representation of word content commonly used to represent user-generated content. This word cloud includes the 1000 status updates analyzed for this study. It depicts the 150 most frequently utilized words, with each word’s frequency correlated with font size.

Figure 1
Word Cloud

We further explored the data by mining the 52,153 status updates for terms likely to be correlated with misunderstanding or misuse and then reviewing them to confirm evidence confusion or mishandling. The most popular word combination in this category was “flu + antibiotics,” with 345 status updates including misinformation reaching a total of 172,571 followers. The next most popular word combination was “cold + antibiotics,” with 302 status updates reaching a total of 850,375 followers. The remaining combinations, “leftover + antibiotic(s),” “extra + antibiotic(s),” and “share + antibiotic(s)” were determined to indicate misuse in 40 cases, with a total reach of 23,016 followers. See Table 4: Misuse and Misunderstanding: Categories, Examples, Frequencies, and Reach of Twitter Status Updates.

Table 4
Misuse and Misunderstanding: Categories, Examples, Frequencies, and Reach of Twitter Status Updates


Launched publicly in July 2006, Twitter is a social networking and micro-blogging service that allows users to post frequent updates. An update is limited to 140 characters and can be posted through three methods: web form, instant message online, or text message via mobile phone [10]. Twitter users follow one another’s updates and can search all updates for keywords of interest. Twitter has been used to disseminate information and news, solicit feedback, communicate with companies, share ideas, document events, and provide personal updates [11]. Notably, Twitter has been employed by NASA to provide updates on the status of space shuttle flights [12], by a student journalist to get himself out of an Egyptian jail by “tweeting” a single word to his Twitter network: “Arrested” [13], and to disseminate information during protests after a contested election in Iran in June 2009 [14, 15].

Use of Twitter has grown rapidly, with a 1,460% increase in global audience between June 2008 and June 2009. The site had an estimated 44.5 million visitors worldwide as of June 2009 (which only includes traffic to, not usage on desktop and mobile clients, which is also common) [16]. Twitter reached one billion tweets in November 2008 [17] and served over 3.7 billion tweets as of September 4, 2009 [18].

With a median age of 31 years, Twitter attracts 19% of online adults ages 18–24, 20% of online adults ages 25–34, and 10% of online adults ages 35–44 years old. Twitter users are disproportionately from lower-income households and are more ethnically and racially diverse than the full U.S. population due to the skewed age distribution toward a younger population. Twitter users are also more likely to live in urban areas (35%, compared with 29%, of all internet users). Use of Twitter is correlated with utilization of other social media, with 23% of social network users saying they have used Twitter or a similar service, compared with just 4% of those who do not use social services [11].

According to a Pew Internet & American Life survey, 12% of e-patients use Twitter or another service to share health-related updates about themselves or to see others’ health-related updates [5]. One survey by Jupiter, a company which researches and analyzes the impact of Internet and consumer technologies, provides insight into why people look for and share information online; 36% of respondents use the internet to see what other consumers say about a medication or treatment, 31% use the internet to research other consumers’ knowledge and experiences, 27% use the internet to learn skills or get education that help to manage a condition, and 17% use it for emotional support [19]. People who feel they have a lot at stake, such as people living with a disability or chronic disease, are more likely to engage intensely with online resources [20].

This study confirmed that Twitter is a space for the informal sharing of health information and advice. The dissemination of information on Twitter through networks of followers and a culture of "retweeting" demonstrate the potential reach of this medium for the dissemination of both valid and invalid information. It is therefore important for health care professionals to have a basic understanding of such services and the nature of the health-related information that is shared on them. Given the immense popularity of such sites, they have become an integral way in which people gather and disseminate information [21].

Twitter and similar services may provide a venue to identify potential misuse or misunderstanding of antibiotics, to promote positive behavior change, and to disseminate valid information. For instance, an organization can enable people taking medications to sign up to receive Twitter updates reminding them to take their medication at proper intervals to avoid missing doses or failing to complete a prescription. Research on behavior change interventions delivered by mobile telephone short-message service can be used as a model for such Twitter-based reminders, including prevention of sexually transmitted diseases [22], smoking cessation [23], improving travel vaccination rates [24], and supporting insulin therapy [25]. One recent paper reviewed 14 studies and reported positive behavior change outcomes in 13 of them [26]. To disseminate information to those exhibiting confusion or sharing misinformation, online services are available to monitor and auto-respond to trigger word combinations, such as “flu + antibiotics” [27].

Finally, such tools can potentially be used to gather important real-time health data by creating a “mashup,” which combines health status updates with location-based information [28]. To track outbreaks, for example, it would be relatively easy for a health organization to enable people to submit Twitter status updates with symptoms and location data using a predefined format so that the updates are machine readable and easily mapped [29, 30]. A good model for such an initiative is the Twitter Vote Report project, which aggregated and mapped voter reports regarding long lines, broken machines, and problems with registration rolls in real time [31]. The project used a range of hashtag metadata to track these reports, including #votereport (added to all reports), #[zip code] to track where the individual was voting, #machine to report problems with a voting machine, and wait:[minutes] to report the length of the line [32]. Such updates can be submitted proactively by participants for the sake of generating a data pool, as with the Twitter Vote Report, or passively, like the antibiotics-related data we explored for this paper. In either case, such information could be integrated with existing disease surveillance systems to supplement the information already being aggregated from different sources [33, 34].

This study has limitations that warrant discussion. First, the validity of the content and self reported behaviors in Twitter status updates is unknown. This study used status updates as an indicator of what people think, believe, or understand; it is possible, however, that some status updates are embellished or exaggerated for effect and thus cannot be taken at face value. In addition, the extent to which findings are generalizable to other micro-blogging platforms is unknown. We may have experienced a seasonal bias; the study did not take place during the traditional influenza season, but an outbreak of H1N1 increased the frequency with which Twitter users discussed the flu and, likely, the co-occurrence of the terms “flu + antibiotics.” This effect, however, would not necessarily affect the relative level of accurate or inaccurate information being discussed. It is possible, however, that the novel nature of the H1N1 strain, in combination with amplified news coverage, may have led to an increase in misinformation. Finally, there may have been measurement biases, as choosing categories was subjective and a different group of researchers might have determined a different set, though Q sort methodology was used to reduce this effect. Also, categorization required a series of individual judgments, and a portion of the status updates could have been reasonably placed into multiple categories, resulting in lower inter-rater reliability. The study team worked to reduce this effect by categorizing ambiguous cases by consensus.

Despite these limitations, this study offers valuable findings. First, given that health information is shared extensively on such networks, it is important for health care professionals to have a basic familiarity with social networking media services, such as Twitter. Second, such services can potentially be used to gather important real-time health data and may provide a venue to identify potential misuse or misunderstanding of antibiotics, promote positive behavior change, and disseminate valid information.

Research focusing on microblogs and social networking services is still at an early stage. Further study is needed to assess how to promote healthy behaviors and to collect and disseminate trustworthy information using these tools.


Financial support. Columbia University, The Center for Interdisciplinary Research to Reduce Antimicrobial Resistance, Grant # T90 NR010824: Training in Interdisciplinary Research to Reduce Antimicrobial Resistance (TIRAR)


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Potential conflicts of interest. All authors: no conflicts.

Contributor Information

Daniel Scanfeld, The Integrated Program in Cellular, Molecular, Structural and Genetic Studies, Columbia University, Address: Fidock Lab, Columbia University Medical Center, Hammer HSC, Room 1502, 701 W 168th Street, New York, NY 10032, Business phone number: (212) 305-6958, Home phone number: (732) 241-9086, ude.aibmuloc@0112smd..

Vanessa Scanfeld, MixedInk.

Elaine L. Larson, School of Nursing and Department of Epidemiology, Mailman School of Public Health, Columbia University.


1. Hughes B, Joshi I, Wareham J. Health 2.0 and Medicine 2.0: tensions and controversies in the field. J Med Internet Res. 2008;10(3):e23. [PMC free article] [PubMed]
2. The Experts vs. the Amateurs: A Tug of War over the Future of Media. Knowledge@Wharton 2009. 2008. Mar 19, [cited 2009 April 17]. Available from:
3. Edelman . Edelman; 2009. 2009 Edelman Trust Barometer.
4. Moturu ST, Liu H, Johnson WG. Trust evaluation in health information on the World Wide Web. Conf Proc IEEE Eng Med Biol Soc 2008; 2008. pp. 1525–1528. [PubMed]
5. Fox S, Jones S. The Social Life of Health Information. Pew Internet & American Life Project. 2009
6. Moore RJ. Twitter Data Analysis: An Investor's Perspective 2009. 2009. Oct 5, [cited 2009 November 23]. Available from:
7. Webler T, Danielson S, Tuler S. Using Q method to reveal social perspectives in environmental research. Greenfield: Social and Environmental Research Institute; 2009.
8. Akhtar-Danesh N, Baumann A, Cordingley L. Q-methodology in nursing research: a promising method for the study of subjectivity. West J Nurs Res. 2008;30(6):759–773. [PubMed]
9. Rivest R. The RC4 Encryption Algorithm, RSA Data Security Inc. Proprietary. 1992 Mar
10. Twitter. CrunchBase. 2009. [cited 2009 April 19]. Available from: Available from:
11. Lenhart A, Fox S. Twitter and status updating. Pew Internet & American Life Project. 2009
12. Chang K. Phoenix to Earthlings: I’ve Landed! Awesome! The New York Times: 2008.
13. Simon M. 'Twitters' his way out of Egyptian jail. 2008. 2008. Apr 25, [cited 2009 April 20]. Available from:
14. Ostrow A. Twitter Reschedules Maintenance Around #IranElection Controversy. Mashable: The Social Media Guide 2009. 2009. Jun 15, [cited 2009 August 15]. Available from:
15. Labott E. State Department to Twitter: Keep Iranian tweets coming. AC360 2009. 2009. Jun 16, [cited 2009 August 15]. Available from:
16. Schonfeld E. Twitter Reaches 44.5 Million People Worldwide In June (comScore) [cited 2009 August 18];TechCrunch 2009. 2009 Aug 3; Available from:
17. Schroeder S. Twitter: One Billion Tweets. Wow. [cited 2009 April 20];Mashable: The Social Media Guide 2008. 2008 Nov 12; Available from:
18. Gigatweet. Popacular. 2009. [cited 2009 September 4]. Available from:
19. Levy M, et al. Online Health: Assessing the Risk and Opportunity of Social and One-to- One Media. New York: JupiterResearch; 2007.
20. Fox S. The Engaged E-patient Population. Pew Internet & American Life Project. 2008
21. McLean R, Richards BH, Wardman JI. The effect of Web 2.0 on the future of medical practice and education: Darwikinian evolution or folksonomic revolution? Med J Aust. 2007;187(3):174–177. [PubMed]
22. Lim MS, et al. SMS STI: a review of the uses of mobile phone text messaging in sexual health. Int J STD AIDS. 2008;19(5):287–290. [PubMed]
23. Rodgers A, et al. Do u smoke after txt? Results of a randomised trial of smoking cessation using mobile text messaging. Tobacco Control. 2005:255–261. [PMC free article] [PubMed]
24. Vilella A, et al. The role of mobile phones in improving vaccination rates in travelers. Prev Med. 2004;38(4):503–509. [PubMed]
25. Franklin V, et al. "Sweet Talk": text messaging support for intensive insulin therapy for young people with diabetes. Diabetes Technol Ther. 2003;5(6):991–996. [PubMed]
26. Fjeldsoe BS, Marshall AL, Miller YD. Behavior change interventions delivered by mobile telephone short-message service. Am J Prev Med. 2009;36(2):165–173. [PubMed]
27. Needleman R. Twitterhawk: Clever Twitter marketing, or spam? CNET News 2009. 2009. Feb 6, [cited 2009 August 11]. Available from:
28. Mashup (web application hybrid) Wikipedia. [cited 2009 August 4]. Available from:
29. Cheung KH, et al. HCLS 2.0/3.0: health care and life sciences data mashup using Web 2.0/3.0J. Biomed Inform. 2008;41(5):694–705. [PMC free article] [PubMed]
30. Hardey M. Public health and Web 2.0. J R Soc Promot Health. 2008;128(4):181–189. [PubMed]
31. About. Twitter Vote Report. [cited 2009 August 11]. Available from:
32. How to Participate. Twitter Vote Report. [cited 2009 August 11]. Available from:
33. Keller M, et al. Use of unstructured event-based reports for global infectious disease surveillance. Emerg Infect Dis. 2009;15(5):689–695. [PMC free article] [PubMed]
34. Eysenbach G. Infodemiology and infoveillance: framework for an emerging set of public health informatics methods to analyze search, communication and publication behavior on the Internet. J Med Internet Res. 2009;11(1):e11. [PMC free article] [PubMed]