Background: The development of effective health care and public health interventions requires a comprehensive understanding of the perceptions, concerns, and stated needs of health care consumers and the public at large. Big datasets from social media and question-and-answer services provide insight into the public’s health concerns and priorities without the financial, temporal, and spatial encumbrances of more traditional community-engagement methods and may prove a useful starting point for public-engagement health research (infodemiology).
Objective: The objective of our study was to describe user characteristics and health-related queries of the ChaCha question-and-answer platform, and discuss how these data may be used to better understand the perceptions, concerns, and stated needs of health care consumers and the public at large.
Methods: We conducted a retrospective automated textual analysis of anonymous user-generated queries submitted to ChaCha between January 2009 and November 2012. A total of 2.004 billion queries were read, of which 3.50% (70,083,796/2,004,243,249) were missing 1 or more data fields, leaving 1.934 billion complete lines of data for these analyses.
Results: Males and females submitted roughly equal numbers of health queries, but content differed by sex. Questions from females predominantly focused on pregnancy, menstruation, and vaginal health. Questions from males predominantly focused on body image, drug use, and sexuality. Adolescents aged 12–19 years submitted more queries than any other age group. Their queries were largely centered on sexual and reproductive health, and pregnancy in particular.
Conclusions: The private nature of the ChaCha service provided a perfect environment for maximum frankness among users, especially among adolescents posing sensitive health questions. Adolescents’ sexual health queries reveal knowledge gaps with serious, lifelong consequences. The nature of questions to the service provides opportunities for rapid understanding of health concerns and may lead to development of more effective tailored interventions.
The development of effective health care and public health interventions requires a comprehensive understanding of the perceptions, concerns, and stated needs of health care consumers and the public at large [, ]. Clinical and behavioral interventions are most successful when aimed at improving outcomes that are important and relevant to patients. Interventions targeted at these patient-centered outcomes are most effectively developed when patients are engaged in the research process, particularly regarding the identification of salient problems. Funders of health care research increasingly expect proposals to include substantial evidence of attention to patient-centered outcomes through public engagement in the research process, including the process of developing and framing research questions [ - ].
There are many successful models of engaging the public in research, ranging from long-term engagement models such as community-based participatory and action research to the use of focus groups, interviews, and specific designs to elicit stakeholder feedback [, ]. However, there are substantial challenges associated with these approaches. First, these approaches require a significant investment of time and resources, valued commodities that may not be available to researchers and their teams, nor to communities and their members [ ]. Second, in traditional research geographic constraints often limit the number and diversity of individuals who can be included in a single project. Third, most of these methods begin with an a priori research question relevant to the community but often generated by the researcher, which restricts public involvement in the framing of research priorities [ ]. In order to overcome the aforementioned limitations and develop relevant and effective patient-centered health interventions, new methods of patient and public engagement are needed.
The Internet has changed the ways in which people seek out and share health-related information [, ]. Research shows that 35% of Americans report having used the Internet, including social media platforms, to determine what medical condition they or someone they know might have [ , ]. Advances in mobile phone technology make searching the Internet for health-related issues even easier. A recent poll found that 62% of mobile phone owners have used their phone in the past year to look up information about a health condition [ ]. Researchers have increasing access to anonymized data from these sites, which have thus far been used to research and disseminate information about disease and disease processes [ ]. More recently, social media and other Web-based data sources have been used to facilitate early outbreak detection [ - ]. These datasets can also be used as a point of entry for public involvement in health research. Social media data provide insight into the public’s health concerns and priorities without the financial, temporal, and spatial encumbrances of more traditional community-engagement methods. While these newer methods cannot replace the more traditional ones, social media methods may prove a useful starting point for public engagement in the health research enterprise.
In 2014, the Indiana University Social Network Health Research Laboratory developed a partnership with ChaCha (ChaCha Search, Inc, Carmel, IN, USA) , a US-based company that operates a human-guided question-and-answer service that provides free, real-time answers to any question through its website, text messaging, or mobile apps. The data provide a powerful and unique opportunity to listen to the authentic health concerns of individuals. Other Internet-based platforms also provide opportunities to assess population health concerns. Social media platforms have been widely discussed in the literature [ - ]. These platforms, while valuable, are designed for users to communicate with a broad audience of friends or the public at large (eg, Twitter, Facebook), and posts are part of social identity presentation [ ]. Conversely, ChaCha queries are a private exchange between an anonymous user and anonymous human guides or a computer. The private nature of the exchange allows users to put forth questions that may be stigmatizing in other settings.
Through our partnership with ChaCha, our laboratory is examining the use of Internet-based question-and-answer services to elicit the patient’s voice and develop health interventions that resonate with public concern. The purpose of this paper is to describe ChaCha user characteristics and health-related queries, and to discuss how this big dataset may be used to better understand the perceptions, concerns, and stated needs of health care consumers and the public at large.
In early 2015 we conducted an automated retrospective textual analysis of 1.9 billion anonymous queries submitted to ChaCha by 19.3 million unique users between January 2009 and November 2012. Because we analyzed only existing, de-identified data, the Indiana University Institutional Review Board determined that the study did not meet definitions of human subject research.
We aggregated queries by year in tabulated ASCII text files, in which each line contained 16 data fields representing 1 ChaCha query and 16 associated descriptors (). Each year’s file was imported to a Linux machine with 64 GB of RAM. Perl scripts were used to parse and summarize the raw data for cleaning and subsequent analyses. A total of 2.004 billion queries were read, of which 3.50% (70,083,796/2,004,243,249) were missing 1 or more data fields, leaving 1.934 billion complete lines of data for these analyses.
|1||Date and time (eastern time) of query|
|2||Full category path|
|5||Source type (voice, text message)|
|6||System used to route and answer question|
|7||City in which user lives (user reported)|
|8||State in which user lives (user reported)|
|9||Region in which user lives (derived from state given in field 8)|
|10||Country in which user lives|
|11||Area code of user’s phone number (user reported)|
|12||Zip code in which user lives (user reported)|
|13||User’s sex (user reported)|
|14||User’s age (user reported)|
|15||User unique identifier (machine generated)|
|16||Text of query|
There were 19.3 million unique ChaCha users who submitted at least one query during the dates under study. The median user age was 17 years, and approximately 68.35% (5,431,866/7,947,118) of users were younger than age 20 years. There were roughly equal numbers of male (4,367,538/8,875,704, 49.21%) and female (4,508,166/8,875,704, 50.79%) users. The median number of queries per user was 16, with a range of 1–1128 (99th percentile). Approximately 75.93% (1,468,646,207/1,934,159,453) of queries had user profiles from which we could derive the user’s sex, and similarly age from 74.41% (1,439,144,291/1,934,159,453) of queries. A little more than half (800,109,775/1,468,646,207, 54.48%) of these queries were submitted by females. The majority (987,749,753/1,439,144,291, 68.63%) were submitted by users between 12 and 19 years of age. Among these adolescent users, more queries were submitted by females (603,941,883/1,053,718,318, 57.32%) than by males (449,776,435/1,053,718,318, 42.68%). In total, 74.26% (1,436,399,307/1,934,159,453) of queries were made via short message service text message, and the rest from a mix of Web interface, other mobile apps, and voice calls to an automated system. User location (place of residence) was missing for about 73.56% (1,422,701,099/1,934,159,453) of queries. The vast majority of queries were made from the United States (1,933,171,565/1,934,159,453, 99.95%), and approximately 0.05% (987,887/1,934,159,453) of queries originated from the United Kingdom.depicts the user’s location in the United States for the 26.44% (511,458,354/1,934,159,453) of queries for which this information was available.
Service use peaked in 2011, during which there were nearly 672 million queries. Monthly service use fluctuated between 10 million queries in January 2009 and a peak of approximately 60 million queries in May 2011. There were no noteworthy service use trends by month or day of the week. Users most often submitted their questions between 9 PM and 12 AM.
Content of Queries
All incoming queries were initially filtered a by proprietary ChaCha algorithm that identifies keywords to sort 75.45% (1,459,279,135/1,934,159,453) of queries into 12 broad categories () that are further divided into 129 subcategories. Excluding ChaCha customer service-related questions, the queries we analyzed most commonly fell into 5 ChaCha-described categories: (1) Entertainment & Arts, (2) Language & Lookup, (3) Society & Culture, (4) Science & Technology, and (5) Health. Of a total of 106 million health queries, 78.17% (83,056,248/106,254,243) were generated by users who specified their sex and age. We focus here on the subset of those queries (n=68 million) that passed a proprietary ChaCha algorithm that looks for sentence structure, interrogative words, and other factors to filter out “bad questions” that lack sufficient information to be answered.
|Category||Total number of questions||Questions per male user||Questions per female user||% male users, this category||% female users, this category||% of categorized questions (n=1,459,279,135)|
|Entertainment & Arts||391,911,144||40.2||42.4||49.36% (3,850,766/7,801,869)||50.64% (3,951,103/7,801,869)||26.86% (391,911,144)|
|Language & Lookup||226,403,804||19.4||25.9||48.49% (3,786,865/7,809,778)||51.51% (4,022,913/7,809,778)||15.51% (226,403,804)|
|Customer Service||174,889,683||17.5||19.0||49.27% (3,727,948/7,566,817)||50.73% (3,838,869/7,566,817)||11.98% (174,889,683)|
|Society & Culture||136,908,800||12.5||17.2||48.62% (3,354,359/6,899,650)||51.38% (3,545,291/6,899,650)||9.38% (136,908,800)|
|Science & Technology||109,703,527||11.1||10.5||49.97% (3,437,238/6,878,206)||50.03% (3,440,968/6,878,206)||7.52% (109,703,527)|
|Health||106,247,678||11.7||16.4||47.17% (2,847,543/6,036,379)||52.83% (3,188,836/6,036,379)||7.28% (106,247,678)|
|Sex||89,136,284||15.7||12.6||51.09% (2,587,600/5,064,404)||48.91% (2,476,804/5,064,404)||6.11% (89,136,284)|
|Lifestyle||74,829,194||8.1||9.5||48.87% (3,095,517/6,334,749)||51.13% (3,239,232/6,334,749)||5.13% (74,829,194)|
|Politics & Government||47,119,373||6.7||6.6||50.26% (2,436,934/4,848,274)||49.74% (2,411,340/4,848,274)||3.23% (47,119,373)|
|Sports||46,741,475||9.7||5.1||55.51% (2,617,724/4,715,548)||44.49% (2,097,824/4,715,548)||3.20% (46,741,475)|
|Business||29,509,62||4.4||4.3||49.60% (2,148,975/4,332,832)||50.40% (2,183,857/4,332,832)||2.02% (29,509,621)|
|Travel||25,878,552||3.7||4.2||47.93% (2,190,337/4,569,614)||52.07% (2,379,277/4,569,614)||1.77% (25,878,552)|
We examined whole-sentence health queries, first those that were generated by roughly equal proportions of males and females, then those that were predominately (≥90%) submitted by females, and finally those predominately (>80%) submitted by males. Among the sex-balanced queries, questions about pregnancy were by far the most prevalent, such as the following: “How are babies made?” “Can you get pregnant on your period?” “What are the signs of pregnancy?” The only other health query frequently submitted by both males and females was about the length of time that alcohol remains in the body.
The queries submitted predominately by females focused on signs and symptoms of reproductive and urinary tract infections, ovulation, and pregnancy. The most common query was about signs and symptoms of yeast infection, followed by inquiries about how to treat, get rid of, or cure a yeast infection. Females more commonly than males asked about the menstrual cycle and its relationship to pregnancy: “When do you ovulate?” “When are you most likely to get pregnant?” “Am I pregnant?” Toxic shock syndrome was frequently mentioned by females, who wanted to know more about its symptoms. Other predominately female user queries included body image questions such as “How can you make your butt bigger?” “How do you get rid of cellulite?”, and 1 relational question: “How do you get over a guy?”
Whole-sentence queries submitted predominately by males focused on body image, particularly penis size and methods for increasing it: “Does ExtenZe work?” “How to make your penis bigger?” “How do I get a six-pack?” Marijuana was the next most-common subject of health queries submitted by males: “What is the best kind of marijuana?” “How many grams in an ounce?” “Why is marijuana illegal?” This was followed by queries related to women’s anatomy and physiology: “How deep is a vagina?” “How do you get a girl pregnant?” Personal health queries focused on testicular discomfort (pain, itching), whether creatine use is safe, and physical fitness goals.
Next we examined smaller word groups, of 2- and 3-word phrases, sorted by sex.presents the 10 most prevalent 3-word phrases submitted by males, and shows those submitted by females. Findings mirrored the whole-word analysis with the addition of weight-loss questions arising in queries submitted by both male and female users. and illustrate the most prevalent 2-word phrases submitted predominately by males and females, respectively. shows the most prevalent 2-word phrases submitted by both males and females.
|3-word phrase||Total queries where sex indicated||No. submitted by males||% from males|
|girl pregnant period||31,670||20,892||65.97% (20,892/31,670)|
|pass drug test||84,231||55,328||65.69% (55,328/84,231)|
|stay ur system||24,880||15,944||64.08% (15,944/24,880)|
|fail drug test||20,372||12,823||62.94% (12,823/20,372)|
|urine drug test||16,183||10,075||62.25% (10,075/16,183)|
|kill brain cells||22,120||13,477||60.92% (13,477/22,120)|
|marijuana stay system||23,891||13,765||57.61% (13,765/23,891)|
|long marijuana stay||30,325||17,410||57.41% (17,410/30,325)|
|long-term effects||24,048||13,623||56.65% (13,623/24,048)|
|3-word phrase||Total queries where sex indicated||No. submitted by females||% from females|
|symptoms yeast infection||36,158||32,177||88.99% (32,177/36,158)|
|15 year girl||33,139||28,245||85.23% (28,245/33,139)|
|early signs pregnancy||49,749||40,368||81.14% (40,368/49,749)|
|urinary tract infection||91,179||72,242||79.23% (72,242/91,179)|
|birth control pills||101,851||79,706||78.26% (79,706/101,851)|
|birth control pill||69,488||53,437||76.90% (53,437/69,488)|
|help lose weight||79,265||59,683||75.29% (59,683/79,265)|
|lose weight fast||52,361||39,264||74.99% (39,264/52,361)|
|pregnant birth control||48,347||33,946||70.21% (33,946/48,347)|
Finally, we examined patterns in queries by age groups. The most prevalent 2-word phrases in queries from users aged 13–19, 20–39, and ≥40 years are depicted in- , respectively.
Among adolescents younger than 19 years, more females than males submitted queries, whereas among young adults aged 19–29 years, more males than females submitted queries. Age patterns were also sex-related patterns, as reflected in the most prevalent 3-word phrases ().
|Age in years||Males||Females|
|13–19||average weight 17||17 year girl|
|weight 17 year||weight 17 year|
|16 year olds||18 year girl|
|20–39||pill white oblong||pill oblong white|
|pill oblong white||pill white oblong|
|side blank side||white oblong pill|
|≥40||white oblong pill||congestive heart failure|
|congestive heart failure||side blank side|
|13 year girl||small round white|
Exploring the ways in which consumers use the Internet to seek health information can also aid Internet-based recruitment for research studies of various types, to improve communication between consumers and health care providers, and to inform the content and geographic scope of marketing for evidenced-based interventions using Internet-accessible platforms. To our knowledge, this is the first analysis of ChaCha data, and these initial results provide valuable methodological and content insights. Methodologically, the results of this initial query affirm our a priori assumption, and the findings of other studies examining Internet health information seeking, that big-data analytical techniques applied to these datasets allow for highly efficient identification of health concerns of users and provide substantial opportunities to develop interventions focused on patient-centered outcomes. Consider that our team analyzed 68 million health-related queries among 1.9 billion overall, generated by 19 million unique users, in less than 5 months and with a total cost of less than $15,000.00. Our entire team working full-time using traditional patient-engagement strategies would have been unable to generate this volume of data in our collective lifetimes, and the cost would be untenable. The ability to analyze such a large volume of user-generated health information-seeking data in such a short time has the potential to fundamentally change patient-centered outcomes research. Patient-engagement strategies are at the heart of effective health outcomes research but are costly and time intensive. Big-data analytic strategies have the potential to make widespread adoption of patient-centered engagement strategies possible at a fraction of the cost.
Several significant content findings from this initial analysis of the ChaCha dataset are consistent with the literature regarding adolescents’ use of social media (eg, Twitter) for seeking health information. The first is that the majority of health-related queries were submitted by adolescent users, which suggests that adolescents are comfortable using an anonymous text-based question-and-answer service for health information seeking, and a similar platform could be useful for interventions targeted to adolescents. The second is that adolescents’ health queries reveal potential knowledge gaps that have serious, lifelong consequences. The vast majority of health questions submitted by adolescents were focused on sexual and reproductive health. They frequently asked about when and how a girl could become pregnant, the signs and symptoms of pregnancy, and the effectiveness and adverse-effect profile of birth control. There were also a large number and proportion of adolescent user-generated queries about the detection and treatment of reproductive tract infections (primarily yeast and urinary tract infections), the length of time that marijuana remains detectable in the blood or urine, weight loss, and wisdom tooth removal. The content of adolescents’ queries indicates their interest in and need for real-time, anonymous answers to questions about their sexual and reproductive health.
As with most studies that analyze social media data, this study had several limitations. First, we do not know whether users were searching for their own knowledge or on behalf of a friend or family member. Second, demographic data were self-reported by anonymous users, who may have misrepresented their city, state, sex, or age. Third, our research team was not provided access to this data until 2014, rendering the data 3–6 years old at the time of analysis. As a result, it is possible that the terminology used to describe health concerns, especially among adolescents, may be slightly outdated. However, we are less focused on how people talk about health concerns than on what issues cause them enough concern to prompt health information seeking. We believe it is unlikely that the core health concerns raised by users of the ChaCha services have changed dramatically in the last 3–6 years. Importantly, had we applied traditional methods to collect these data, the time lag between collection and analysis would have been substantially longer than the 3- to 6-year gap in our study. Finally, given that this is a proprietary dataset, as are many other social media datasets, it is not convenient for other investigators to replicate this work.
While other question-and-answer services exist, and many are more popular than ChaCha, the ChaCha service has several unique features that make it appealing for patient-centered research. First, ChaCha use is completely anonymous. Users of other question-and-answer sites, such as Quora, are required to sign up for the service using potentially traceable information such as email or Facebook profile. While Quora may be a secure site, the requisite entry of identifiable information in order to use the site may limit the pool of users and the types of questions they are willing to ask. Popular search engines such as Google or Bing provide a greater sense of privacy, but they leave a searchable history, which may also promote self-censorship. Moreover, ChaCha was specifically designed as a question-and-answer service, in which users understood there was a human curating the answers on the other end of the line. This simulates the health care encounter more closely than a Web search, in which the curating is done by the information seeker.
Additional research with these and other social media data are needed to develop a deeper understanding of spatial and temporal patterns in health information seeking that can inform patient-centered research. The ChaCha service provided a perfect environment for maximum frankness, especially around sensitive health questions. Just below the surface of this massive dataset are the quietly whispered questions, both banal and extraordinary, that represent the hopes, fears, dreams, and concerns of millions of people. Without compromising their anonymity in any way, we can listen in, to improve the health and wellbeing of millions more.
Access to this dataset was generously made available to the Social Network Health Research Laboratory at the Indiana University School of Nursing by ChaCha. Financial support for this project was provided by the Indiana University School of Nursing, Center for Research and Scholarship.
Conflicts of Interest
Multimedia Appendix 1
The most prevalent 2-word phrases submitted to ChaCha predominately by male users. View interactive graph at [http://www.jmir.org/ojs/public/graphs/male/] .ZIP File (Zip Archive), 173KB
Multimedia Appendix 2
The most prevalent 2-word phrases submitted to ChaCha predominately by female user. View interactive graph at [http://www.jmir.org/ojs/public/graphs/female/] .ZIP File (Zip Archive), 176KB
Multimedia Appendix 3
The most prevalent 2-word phrases submitted to ChaCha by both males and females. View interactive graph at [http://www.jmir.org/ojs/public/graphs/both/] .ZIP File (Zip Archive), 177KB
Multimedia Appendix 4
The most prevalent 2-word phrases submitted to ChaCha by users aged 13â€“19 years. View interactive graph at [http://www.jmir.org/ojs/public/graphs/age_1/] .ZIP File (Zip Archive), 172KB
Multimedia Appendix 5
The most prevalent 2-word phrases submitted to ChaCha by users aged 20â€“39 years. View interactive graph at [http://www.jmir.org/ojs/public/graphs/age_2/] .ZIP File (Zip Archive), 170KB
Multimedia Appendix 6
The most prevalent 2-word phrases submitted to ChaCha by users aged â‰¥40 years. View interactive graph at [http://www.jmir.org/ojs/public/graphs/age_3/] .ZIP File (Zip Archive), 173KB
- Frank L, Basch E, Selby JV, Patient-Centered Outcomes Research Institute. The PCORI perspective on patient-centered outcomes research. JAMA 2014 Oct 15;312(15):1513-1514. [CrossRef] [Medline]
- Selby JV, Slutsky JR. Practicing partnered research. J Gen Intern Med 2014 Dec;29 Suppl 4:814-816 [FREE Full text] [CrossRef] [Medline]
- Garces J, Lopez G, Wang Z, Elraiyah T, Nabhan M, Campana J, et al. Mayo Clinic. 2012. Eliciting Patient Perspective in Patient-Centered Outcomes Research: A Meta Narrative Systematic Review URL: http://www.pcori.org/assets/Eliciting-Patient-Perspective-in-Patient-Centered-Outcomes-Research-A-Meta-Narrative-Systematic-Review.pdf [accessed 2016-02-08] [WebCite Cache]
- Minkler M, Wallerstein N. Community-Based Participatory Research for Health: From Process to Outcomes. 2nd edition. San Francisco, CA: Jossey-Bass; 2008.
- Denzin NK, Lincoln YS. Strategies of Qualitative Inquiry. 3rd edition. Los Angeles, CA: Sage Publications, Inc; 2008.
- Goodman RM. Community-based participatory research: questions and challenges to an essential approach. J Public Health Manag Pract 2001 Sep;7(5):v-vi. [Medline]
- Minkler M. Ethical challenges for the “outside” researcher in community-based participatory research. Health Educ Behav 2004 Dec;31(6):684-697. [CrossRef] [Medline]
- Wong C, Harrison C, Britt H, Henderson J. Patient use of the internet for health information. Aust Fam Physician 2014 Dec;43(12):875-877. [Medline]
- Nielsen Company, NM Incite. 2012. State of the Media: The Social Media Report 2012 URL: http://www.nielsen.com/content/dam/corporate/us/en/reports-downloads/2012-Reports/The-Social-Media-Report-2012.pdf [accessed 2016-02-03] [WebCite Cache]
- Fox S, Duggan M. Health online. Washington, DC: Pew Internet & American Life Project; 2013. URL: http://bibliobase.sermais.pt:8008/BiblioNET/Upload/PDF5/003820.pdf [accessed 2016-02-03] [WebCite Cache]
- Smith A. U.S. smartphone use in 2015. Washington, DC: Pew Internet & American Life Project; 2015. URL: http://www.pewinternet.org/files/2015/03/PI_Smartphones_0401151.pdf [accessed 2016-02-03] [WebCite Cache]
- Kass-Hout TA, Alhinnawi H. Social media in public health. Br Med Bull 2013;108:5-24. [CrossRef] [Medline]
- Brownstein JS, Freifeld CC, Reis BY, Mandl KD. Surveillance Sans Frontières: Internet-based emerging infectious disease intelligence and the HealthMap project. PLoS Med 2008 Jul 8;5(7):e151 [FREE Full text] [CrossRef] [Medline]
- Paul M, Drezde M. You Are What You Tweet: Analyzing Twitter for Public Health.: Association for the Advancement of Artificial Intelligence; 2011. URL: http://www.aaai.org/ocs/index.php/ICWSM/ICWSM11/paper/view/2880/3264 [accessed 2016-02-03] [WebCite Cache]
- Signorini A, Segre AM, Polgreen PM. The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS One 2011;6(5):e19467 [FREE Full text] [CrossRef] [Medline]
- ChaCha Search, Inc. ChaCha. URL: http://www.chacha.com/ [accessed 2016-02-04] [WebCite Cache]
- Gary JL. Social media: how to use it effectively. J Orthop Trauma 2015 Nov;29 Suppl 11:S5-S8. [CrossRef] [Medline]
- Jimeno-Yepes A, MacKinlay A, Han B, Chen Q. Identifying diseases, drugs, and symptoms in Twitter. Stud Health Technol Inform 2015;216:643-647. [Medline]
- Gomes C, Coustasse A. Tweeting and treating: how hospitals use Twitter to improve care. Health Care Manag (Frederick) 2015;34(3):203-214. [CrossRef] [Medline]
- Dion X. Using social networking sites (namely Facebook) in health visiting practice--an account of five years experience. Community Pract 2015 Feb;88(2):28-31. [Medline]
- Wilson RE, Gosling SD, Graham LT. A review of Facebook research in the social sciences. Perspect Psychol Sci 2012 May;7(3):203-220. [CrossRef] [Medline]
Edited by G Eysenbach; submitted 14.08.15; peer-reviewed by E Buhi, E Castro-Sánchez; comments to author 10.09.15; revised version received 06.11.15; accepted 04.01.16; published 09.03.16Copyright
©Chad Priest, Amelia Knopf, Doyle Groves, Janet S Carpenter, Christopher Furrey, Anand Krishnan, Wendy R Miller, Julie L Otte, Mathew Palakal, Sarah Wiehe, Jeffrey Wilson. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 09.03.2016.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.