Your boss has a say over you while you’re on the clock or using the boss’s equipment. But can the boss control what you say off-duty, on the Web? Officials in Kent County, Delaware, think so:

The county’s Levy Court — the equivalent of a county council — has an existing rule that bars employees from using government equipment for personal social media activity at work. But a recent proposal would extend that ban to include activity during non-work times, specifically as it relates to commentary that disparages co-workers or reflects unfavorably toward the county government [Brian Heaton, “Social Media Usage Becoming a Free Speech Question for Governments,” Government Technology, 2011.05.17 ].

As law professor Phillip Sparkes points out in this article, Kent County is going well beyond the boundaries on public employee speech set by Garcetti v Ceballos (2006). That case recognized that public employers can place some limits on what city officials, teachers, and other public employees say while acting in an official capacity. However, that case does not allow government to impose rules on off-duty speech like those proposed by Kent County.

Arvada, Colorado, CIO Michele Hovet offers a more realistic approach to public employees’ First Amendment rights:

“I think folks that draw lines as far as what you can and can’t do on your free time are avoiding the inevitable,” she said. “Social media has been here and it’s not going away. Locking it down is just going to create more management headaches in the long run” [Heaton, 2011.05.17].

People are going to talk… and Tweet. They’re going to use their smartphones and iPads to do so. Trying to control employees’ every utterance is unconstitutional and impractical. Instead of trying to keep employees from talking, local governments will make better use of their time working to treat employees and the public right so they all have good things to talk about.

I’m speaking today on my effort to create an instrument for coding narrative content in online discussions. Following are sources cited in the presentation.

ReCal2: Reliability for Coders: a handy online calculator for intercoder/interrater reliability for nominal data. Calculates Percent Agreement, Scott’s Pi, Cohen’s Kappa, and Krippendorff’s Alpha (nominal). Site also offers tools for more than two raters coding ordinal, interval, and ratio data.

Altman, D. G. (1991). Practical Statistics for Medical Research. Chapman & Hall/CRC.

Greene, K., & Brinn, L. S. (2003). Messages Influencing College Women’s Tanning Bed Use: Statistical versus Narrative Evidence Format and a Self-Assessment to Increase Perceived Susceptibility. Journal of Health Communication, 8(5), 443–461.

Greenhalgh, T., & Hurwitz, B. (1999). Narrative based medicine: Why study narrative? BMJ, 318(7175), 48-50. Retrieved from

Jones, D., Turner, M., Singleton, C., & Ramsay, J. (2009). A study analysing inconsistent responses from people with multiple sclerosis in a recent national audit. Disability and Rehabilitation, 31(25), 2064-2072.

Klenke, K. (2008). Qualitative Research in the Study of Leadership. Emerald Group.

Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159.

Lombard, M., Snyder-Duch, J., & Campanella Bracken, C. (2010, June 1). Intercoder Reliability. Matthew Lombard. Retrieved 02:40:32, from

Osborne, J. W. (2008). Best Practices in Quantitative Methods. SAGE.

Papacharissi, Z. (2007). Audiences as Media Producers: Content Analysis of 260 Blogs. In M. Tremayne (Ed.), Blogging, Citizenship, and the Future of Media. New York: Routledge.

Polkinghorne, D. (1988). Narrative Knowing and the Human Sciences. Albany, NY: State University of New York.

Sarbin, T. R. (1986). Narrative Psychology: The Storied Nature of Human Conduct. Praeger.

Winterbottom, A., Bekker, H., Conner, M., & Mooney, A. (2008). Does narrative information bias individual’s decision making? A systematic review. Social Science & Medicine, 67(12), 2079.

Text of presentation on prior research, delivered in INFS 892, 2011.03.03

Health information technology (HIT) research frequently cites privacy as a concern of health care providers and patients. Privacy is recognized internationally as critical factor in HIT adoption (Anderson, Forgner, Johns, & Reinhardt, 2006). Technological, social, organizational, and legal changes since the 1960s have contributed to steadily growing privacy concerns (Westin, 2003). However, the new technology of online social networks as applied to health—health social networks—poses new conditions and benefits that may change previous privacy concerns.

Health Social Networks

The “backbone” of online social networks is the connected collection of user profiles (Boyd & Ellison, 2008). Such profiles usually include user names, photos, and basic demographic information. On health social networks, these profiles may include medical information that would generally be kept private in traditional interactions with health professionals but which HSN users share to help each other find users with information and experience relevant to their specific health concerns. Online social networks generally include information about connections between members. That information may reside in explicitly declared links, like unidirectional “Like” or “Follower” links or bidirectional “Friend” links (Boyd & Ellison, 2008).

A health social network is an online social network formed around shared interest in a specific health condition like obesity or cancer, a specific area of health care like children’s medicine or hospice, or health information in general. The key characteristic of an online health social network is interaction focused on sharing knowledge and providing emotional support for individuals dealing with health-related questions and problems affecting themselves or other people they care about. Swan (2009) defines a health social network as “a website where consumers may be able to find health resources at a number of different levels.” Swan’s definition includes the word consumer, an increasingly inapt term (Adams, 2010a) (Heidelberger, 2009) given that an important characteristic of health social networks and any other type of online social network is that users are not merely consuming content but producing and organizing much of the content available in the form of, for example, posts, comments, discussions, and ratings. While Internet use is often characterized by “lurking,” the act of reading online discussions without making one’s presence known through participation, some evidence shows that health-related online groups show a markedly above-average level of user participation (Nonnecke & Preece, 2000). This increased participation and the blurring of producers and consumers into conducers affects how individuals configure their understandings of health (Adams, 2010b).

The value of any online social network lies in the sharing of information. In health social networks, one of the vital “different levels” (Swan, 2009) at which users seek information is peer information, advice from users just like them that relates specifically to their experience. Lurkers can find certain information relevant to their health situations without ever making their presence known, but to make the information on a health social network fully relevant to one’s specific health situation, a user generally must choose to engage the community in a conversation, share some personal detail, and surrender some level of privacy.


Privacy is subject to different interpretations in different situations (Allen, 1988). Privacy is not simply the desire to withhold information; more comprehensively, privacy refers to the desire to control disclosure of information (Lanier & Saini, 2008). Privacy means controlling what personal information is revealed to others, when that information is revealed, and how it is used (Westin, 1967, 2003). The view of privacy as a matter of control enjoys broad acceptance (Elgesem, 1996; Fried, 1984; Lessig, 2002). However, some scholars argue that, while control plays an important part in managing privacy, having control does not equate with having privacy (Dinev & Hart, 2004; Laufer & Wolfe, 1977; Xu, 2007). Control may merely be a means to the privacy ends of restricted access and protection from intrusion (Tavani & Moor, 2001).

Westin (1967) identifies four states of privacy:

  1. solitude: freedom from observation by others
  2. intimacy: small-group seclusion in which members can achieve close, relaxed, frank relationship
  3. anonymity: freedom from identification and surveillance in public places and for public acts
  4. reserve: desire to limit disclosure to others (as summarized in Margulis, 2003).

Westin (1967) also identifies four main functions of privacy:

  1. personal autonomy
  2. emotional release
  3. self-evaluation
  4. limited and protected communication

Some scholars narrow the concept of privacy to informational privacy to focus on the ability of individuals to limit the access others have to their information (Alpert, 2003). In developing the “Concern for Information Privacy” construct, (Smith, Milberg, & Burke, 1996) identify four main components: collection, errors, unauthorized access, and secondary use. (Stewart & Segars, 2002) validated and extended the CFIP instrument. CFIP focuses on organizational handling and uses of information. However, the Internet creates important changes in how individuals interact with organizations and with each other with respect to personal information. The Internet gives consumers more access to and control over the data they share in the marketplace (Malhotra, Kim, & J. Agarwal, 2004). Grounded in social contract theory, the construct of Internet users’ information privacy concerns (IUIPC) focuses on consumers’ engagement with e-commerce and consists of three main components:

  1. collection: equitable exchange of information according to mutually accepted rules
  2. control: freedom to voice an opinion or exit the contractual agreement
  3. awareness of privacy practices: understanding of the rules and practices established by the firm and its agreement with consumers (Malhotra et al., 2004).

Subsequent research has used social contract theory similarly to understand patient interaction with medical websites (Gaurav Bansal, Zahedi, & Gefen, 2010).

The National Consumer Health Privacy Survey 2005 (Bishop, Holmes, & Kelley, 2005) provides these key findings about user interaction

  1. Consumers are concerned about health information privacy.
  2. Consumers are unaware of their privacy rights.
  3. A small fraction (13%) of consumers engage in specific privacy-protection behavior. Most cited behaviors have to do with keeping insurers from finding out information that might affect payment or premiums.
  4. Consumers are willing to trade privacy for benefits. Note that at this time, more consumers identified paper records as secure than identified electronic records as secure.

Privacy in Health Social Networks

More recent research suggests that privacy concerns are affected by health status: individuals in poor health are less likely to share information with consumer health websites (G. Bansal & Davenport, 2010). Connected research attempts to link privacy concerns with other personal dispositions: emotional instability may work through perceived sensitivity of health information to relate to heightened privacy concerns, but extroversion, agreeableness, conscientiousness, and intellect show little sign of significant connection to privacy concerns (Gaurav Bansal et al., 2010). Notice that the preceding studies, like many discussions of information privacy, address privacy as a consumer issue. Westin (2003) broadens the discussion of privacy to look at individual privacy concerns in the context of their relationships as citizens to government and as employees to employers in addition to the commercial relationship of consumers to business. The IUIPC focuses on the social-contract relationship between individuals and the firm as each entity surrenders different rights and makes different promises for different benefits (Malhotra et al., 2004). These conceptualizations fit with the theme of much literature on health information privacy as it considers the impacts of how health care providers may use (or misuse) electronic health records to process genetic information and other sensitive patient data (e.g., Alpert, 2003).

However, these conceptualizations assume a hierarchy that does not necessarily manifest itself in an online social network. Certainly online social networks may involve citizen–government, employee–employer, and consumer–business interactions. While there may well be consumer–business interactions in health social networks in the form of network members seeking information from staff of the online service (e.g., formal forum moderators or coaches, designated medical experts) or advertisements soliciting business, online social network interactions will generally be dominated by a form of interaction not addressed by most discussions of privacy concerns: non-hierarchical citizen–citizen interactions, users communicating with users as equals seeking the same balance between privacy rights and informational benefits.

One of the above studies acknowledges that health social networks may play by different rules from the typical consumer medical websites. In finding that extroversion shows no significant relationship with health information sensitivity, (Gaurav Bansal et al., 2010) note the the consumer-type website they used in their investigation provides a “leaner” online setting where extroverts “apparently are less forthcoming.” They acknowledge that personality traits may have different influence in different communication settings and recommend further investigation along those lines. Such investigation makes sense for health social networks, as they would provide a richer communication setting where, instead of simply making a commercial transaction for a product, users are engaging directly and often by name with each other. This richer channel can carry more social presence, which could well have more influence over privacy perceptions and behaviors than a “lean” commercial transaction. (Gaurav Bansal et al., 2010) recognize that trust in a website (a factor with a strong positive relationship to intent to disclose health information) could be enhanced for some “customers” by social interaction, the heart and soul of health social networks. Evidence that consumer-oriented research may not capture the full privacy picture in health social networks comes from recent findings that individuals with chronic conditions are more likely to go online to find others with similar health concerns than individuals who do not have chronic conditions (Fox, 2011).

Unique characteristics of online social networks pose challenges to normal privacy expectations and behaviors. The social Web is inherently leaky: they increase the chances for individuals to lose control of information they share (Solove, 2007). Online social networks constitute a “mediated public” where information becomes persistent, searchable, replicable, and open to invisible audiences (boyd, 2007). Information that would be transient in a private face-to-face conversation persists online in entries in the Google cache and copies stored on other computers. Information that would be lost in noise and fallible memory can be retraced and recalled almost instantly by search engines. Information shared online can be replicated in numerous other contexts that may have no direct relation to the original intent that motivated the sharing (such as a discussion on a weight-loss surgery forum about constipation finding its way into a doctoral dissertation on the use of narrative on health social networks). And information directed at one specific person or group in an online forum may be viewed by and provoke reactions from numerous individuals whom the original speaker does not and may not ever know. All of these factors can make social interactions online “eternal,” transcending the moment and context in which they happen and escaping the control of the people originating them (Albrechtslund, 2008). This “eternal” nature is seen in health social network profiles whose stories and data continue to inform their communities even after the originators of those profiles die (Goetz, 2008).

However, concerns about privacy in online social networks often operate from an assumption of vertical hierarchy underpinning the traditional view of surveillance (Albrechtslund, 2008). Concerns about surveillance from some “Big Brother” lead to assumptions that sacrifices of privacy in online social networks stem either from cost-benefit analysis or from ignorance of the dangers. Albrechtslund posits that online social networks support “participatory surveillance” in which individuals empower themselves and each other by sharing information rather than trading it. This participatory paradigm improves our understanding of privacy dynamics online as we witness a shift from the anonymity and pseudonymity that dominated Internet discourse in the first decade of the Web to the increased voluntary posting of personal information on social networks and integration of online and offline activities (Adams, 2010b), a shift enhanced by the increasing availability and popularity of Web 2.0 technology like blogs, Facebook, and Twitter.

Privacy is generally seen as a positive construct, and research focuses on how to protect or preserve it (Angst & R. Agarwal, 2009). One study views sharing health information online as a “disutility” (G. Bansal & Davenport, 2010). Arguably, though, from a health perspective, privacy could be seen as a negative factor standing in the way of building and sharing knowledge and support. In a discussion of blogging in a health social network context, (Adams, 2010b) suggests there is a need for further investigation of whether engaging in online documentation and communication about personal health may motivate individuals to stick with routines in pursuit of their health goals. This suggestion hearkens to the concept of participatory surveillance: health social network users may benefit from being able to “keep an eye on” each other and know that others are similarly keeping an eye on them. Increased information sharing also creates a richer database of disease treatment and patient experience, an advantage that PatientsLikeMe explicitly embraces in an “Openness Philosophy” that challenges the primacy of privacy:

“Currently, most health-care data is inaccessible due to privacy regulations or proprietary tactics,” it declares. “As a result, research is slowed, and the development of breakthrough treatments takes decades. . . . When you and thousands like you share your data, you open up the health-care system. . . . We believe that the Internet can democratize patient data and accelerate research like never before” (Goetz, 2008).

Some users of health social networks report finding informational support among peers that they may not find among their regular physicians:

Thank you for the replies. Of course I will talk to my doctor about this but wanted to come on here because it is more beneficial to me to find out how it affects a person that has had WLS [weight loss surgery]. Doctors are not always familiar with how our new systems work ( user, 2009).

User behavior in health social networks suggests different attitudes toward privacy than we might expect in public face-to-face interactions. In, for instance, plenty of interaction takes place privately, in member-only chat rooms and via other protected channels. However, thousands of users post medical information in publicly accessible profiles, blogs, and forum posts. Users identifiable by username, location, and photo openly discuss personal details like weight loss and gain, prescriptions, constipation, sexual dysfunction, and surgical complications that would be considered impolite if not imprudent to share in the physical company of strangers and which would certainly violate HIPAA if released by those users’ health care providers.

It is possible, of course, that many users do not grasp the privacy implications of using health social networks. A study of health social networks for diabetics found varying capabilities for users to control privacy settings and share information; only eight of the ten studied networks offer accessible privacy policies, and most of them are hard to read (Weitzman, Cole, Kaci, & Mandl, 2011). Providing users with more control over publication may actually “induce them to reveal more sensitive information,” a result which raises concerns that users may conflate increased control over publication with increased control over access and usage of their personal information  (Brandimarte, Acquisti, & Loewenstein, 2010). Access and usage pose the real risk of intrusion and harm, but as potential actions of others, they are less salient than the satisfaction users may derive from the immediate exercise of control over their own information publication.

Concern for information privacy has been shown to correlate negatively with likelihood of adopting electronic health records (Angst & R. Agarwal, 2009). However, our own research at DSU on adoption of electronic health records in South Dakota suggests that privacy concerns are not nearly as prominent in the minds of practitioners as other barriers to ER adoption. And whatever privacy concerns may keep some people from engaging with health information technology, there are thousands of users who are willing to share personal stories and health information on health social networks like PatientsLikeMe (Goetz, 2008).

Privacy concerns in health social networks may be mitigated by the sense of community. Consider this observation from multiple sclerosis patient and PatientsLikeMe user Laurie Fournier:

Pretty much everybody I know over 45 has some kind of medical condition. Some people have had cataracts surgery, or they have high blood pressure, or high cholesterol or diabetes. Everyone has something. And if everyone has something, that really levels the playing field (Goetz, 2008).

The sense of community in health social networks may be even stronger among users with rare diseases. Individuals with rare diseases—i.e., diseases affecting no more than 20,000 people—are “power users” of Internet health resources (Shute, 2011). The observation that such users are “ready and willing to share with each other so that other people can benefit from their experiences” (Shute, 2011) suggests the need to look beyond a purely selfish privacy calculus: users may surrender privacy not to gain emotional or informational support for themselves but to provide such support to other members of their online community.

Privacy remains a valid concern for all individuals. Privacy sets boundaries, and people need physical and psychological boundaries to define their sense of identity. The increasing integration of online social networks into daily activities, especially in the area of patient use of health social networks, will not end the value of privacy. However, health social networks emphasize the difference in privacy concerns with hierarchical institutions and with peers who share health interests and goals.

Works Cited

Adams, S. A. (2010a). Revisiting the online health information reliability debate in the wake of “web 2.0”: An inter-disciplinary literature and website review. International journal of medical informatics, 79(6), 391–400.

Adams, S. A. (2010b). Blog-based applications and health information: Two case studies that illustrate important questions for Consumer Health Informatics (CHI) research. International Journal of Medical Informatics, 79(6), e89-e96. doi:10.1016/j.ijmedinf.2008.06.009

Albrechtslund, A. (2008). Online social networking as participatory surveillance. First Monday (Chicago), 13(3).

Allen, A. L. (1988). Uneasy access: Privacy for women in a free society. Rowman & Littlefield Pub Inc.

Alpert, S. A. (2003). Protecting medical privacy: challenges in the age of genetic information. Journal of Social Issues, 59(2), 301–322.

Anderson, G. F., Forgner, B. K., Johns, R. A., & Reinhardt, U. E. (2006). Health Care Spending and Use of Information Technology in OECD Countries. Health Affairs, 25(3), 819-831. Retrieved from

Angst, C. M., & Agarwal, R. (2009). Adoption of Electronic Health Records in the Presence of Privacy Concerns: The Elaboration Likelihood Model and Individual Persuasion. MIS Quarterly, 33(2), 339-370. doi:Article

Bansal, G., & Davenport, R. (2010). Moderating Role of Perceived Health Status on Privacy Concern Factors and Intentions to Transact with High versus Low Trustworthy Health Websites. MWAIS 2010 Proceedings, 7.

Bansal, G., Zahedi, F. “., & Gefen, D. (2010). The impact of personal dispositions on information sensitivity, privacy concern and trust in disclosing health information online. Decision Support Systems, 49(2), 138-150. doi:10.1016/j.dss.2010.01.010

Bishop, L., Holmes, B. J., & Kelley, C. M. (2005). National consumer health privacy survey 2005. Oakland: California Healthcare Foundation, 1–5.

Boyd, D. M., & Ellison, N. B. (2008). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication, 13(1), 210–230.

boyd, D. (2007, May). Social Network Sites: Public, Private, or What? The Knowledge Tree.

Brandimarte, L., Acquisti, A., & Loewenstein, G. (2010). Misplaced Confidences: Privacy and the Control Paradox. Presented at the Ninth Workshop on the Economics of Information Security (WEIS 2010), Cambridge, MA.

Dinev, T., & Hart, P. (2004). Internet privacy concerns and their antecedents-measurement validity and a regression model. Behaviour & Information Technology, 23(6), 413–422.

Elgesem, D. (1996). Privacy, respect for persons, and risk. Philosophical perspectives on computer-mediated communication, 45–66.

Fox, S. (2011). Peer-to-peer Healthcare. Pew Internet & American Life Project. Pew Research Center’s Internet & American Life Project. Retrieved from

Fried, C. (1984). Privacy. In F. D. Schoeman (Ed.), Philosophical dimensions of privacy: An anthology. Cambridge Univ Pr.

Goetz, T. (2008, March 23). Practicing Patients. The New York Times. Retrieved from

Heidelberger, C. A. (2009). Citizens, Not Consumers. In V. Weerakkody, M. Janssen, & Y. K. Dwivedi (Eds.), Handbook of Research on ICT-Enabled Transformational Government: A Global Perspective (pp. 51-71). Hershey, PA: IGI Global.

Lanier, C. D., & Saini, A. (2008). Understanding consumer privacy: A review and future directions. Academy of Marketing Science Review, 12(2), 1–48.

Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22–42.

Lessig, L. (2002). Privacy as property. Social Research: An International Quarterly, 69(1), 247–269.

Malhotra, N. K., Kim, S. S., & Agarwal, J. (2004). Internet Users’ Information Privacy Concerns(IUIPC): The Construct, the Scale, and a Causal Model. Information Systems Research, 15(4), 336–355.

Margulis, S. T. (2003). On the Status and Contribution of Westin’s and Altman’s Theories of Privacy. Journal of Social Issues, 59(2), 411–429.

Nonnecke, B., & Preece, J. (2000). Lurker demographics: counting the silent. In Proceedings of the SIGCHI conference on Human factors in computing systems (pp. 73-80). The Hague, The Netherlands: ACM. doi:10.1145/332040.332409

Shute, N. (2011, March 1). People Coping With Rare Disease Are Internet Power Users. NPR: Shots. Retrieved March 3, 2011, from

Smith, H. J., Milberg, S. J., & Burke, S. J. (1996). Information privacy: measuring individuals’ concerns about organizational practices. MIS quarterly, 20(2), 167–196.

Solove, D. J. (2007). The future of reputation: gossip, rumor, and privacy on the Internet. Yale Univ Pr.

Stewart, K. A., & Segars, A. H. (2002). An empirical examination of the concern for information privacy instrument. Information Systems Research, 13(1), 36–49.

Swan, M. (2009). Emerging Patient-Driven Health Care Models: An Examination of Health Social Networks, Consumer Personalized Medicine and Quantified Self-Tracking. International Journal of Environmental Research and Public Health, 6(2), 492-525. doi:10.3390/ijerph6020492

Tavani, H. T., & Moor, J. H. (2001). Privacy protection, control of information, and privacy-enhancing technologies. ACM SIGCAS Computers and Society, 31(1), 6–11.

Weitzman, E. R., Cole, E., Kaci, L., & Mandl, K. D. (2011). Social but safe? Quality and safety of diabetes-related online social networks. Journal of the American Medical Informatics Association. Retrieved from

Westin, A. F. (1967). Privacy and freedom (Vol. 97). London.

Westin, A. F. (2003). Social and political dimensions of privacy. Journal of Social Issues, 59(2), 431–453.

Xu, H. (2007). The effects of self-construal and perceived control on privacy concerns. In Proceedings of the 28th Annual International Conference on Information Systems (ICIS 2007). Montréal, Québec, Canada.


I keep forgetting what I planned to write about. That’s what the Internet is for: to remember stuff so I don’t have to. Here are the three topics I’ll tackle in our health informatics research class:

  1. Patient privacy concerns in health social networks (with potential for research topic on user-generated content)
  2. Mobile/ubiquitous computing in health care settings (remember, this can address use by practitioners and/or patients!)
  3. Health care provider use of online social networks


I knew open source software was just a Bolshevik plot:

Сегодня стало известно, что премьер-министр Владимир Путин подписал документ, в котором описан график перехода властных структур на свободное ПО (СПО).

…Заместитель главы Минкомсвязи Илья Массух рассказал CNews, что документ предусматривает полный переход федеральных властей и бюджетников на свободное ПО. План занимает 17 страниц, скачать его можно здесь (идея сохранить документ об СПО в формате .doc принадлежит аппарату правительства РФ) [Владислав Мещеряков, «Путин распорядился перевести власти на Linux»,, 2010.12.27].


Today it became known that Prime Minister Vladimir Putin signed an order laying out a timetable for government agencies to switch to open source software (OSS).

…MinComNet [love those Soviet-style abbreviations] Deputy Chief Ilya Massikh told CNews that the document provides for a full transfer to open source software by federal agencies and budget offices. The 17-page plan can be viewed here (it’s the Russian government’s idea to save a document about OSS in .doc format) [Vladislav Meshcheryakov, “Putin Orders Government Switch to Linux,”, 2010.12.27].

Alt Linux CEO Aleksei Smirnov tells CNews that the switch will save the Russian government money on licensing fees and software import costs while sparking innovation and economic development.

I just happened upon a presentation from an Internet Research Ethics workshop held in October. The presentation, “Blogs: Public, Private, and the ‘Intimsphere’ — a Danish Example,” includes this interesting statement:

From a research ethics perspective, in the United States, research conducted using a blog as a data source would not reviewable by an IRB. For instance, if a researcher used only text from a blog, as part of an analysis, and did not interact with the blog author through, e.g., interviews or surveys, no IRB review or approval would be needed, as it is not considered “human subjects” under the federal definition (45cfr46.102f: “Human subject means a living individual about whom an investigator (whether professional or student) conducting research obtains (1) Data through intervention or interaction with the individual, or (2) Identifiable private information.” “Identifiable private information” is “information about behavior that occurs in a context in which an individual can reasonably expect that no observation or recording is taking place, and information which has been provided for specific purposes by an individual and which the individual can reasonably expect will not be made public (for example, a medical record).” Therefore, if a researcher is getting data from a blog that is public, then it would not meet the criteria for review as set forth in the US regulatory documents [Charles Ess, “Blogs: Public, Private, and the ‘Intimsphere’ — a Danish Example,” Ethics and Internet Research Commons: Building a Sustainable Future pre-conference workshop, 11th Annual Conference of the Association of Internet Researchers, 2010.10.20].

Dr. Ess is not declaring blog research an ethics-free zone. He recounts his own blog research in which he made significant efforts to involve the blog participants he observed in his research, to seek their consent, and to protect their privacy.

But this interpretation of Institutional Review Board requirements supports the “public park” analogy: as I put these words on this public blog, I have as little expectation of privacy as if I were standing on a park bench and speaking loudly. Anyone walking by can listen, study what I say, and offer their conclusions about the meaning of my speech.

Come find out what I’ve been working toward for the last three and a half years! On Tuesday, December 7, I will present what we academics call a doctoral dissertation proposal defense.

At 11 a.m., I’ll stand up in front of an audience of stern-looking academics and other interested parties and talk about the really big paper I’m planning to write about my really big research project on storytelling, social networks, and health (see below for the nitty gritty). I’ll talk for 30 minutes; the general public (yes, you!) gets to grill me for 10 minutes. Then my committee grills me for 20 minutes, throws me out to conduct secret deliberations, then drags me back in to tell me whether they’ll let me keep thinking and writing. If my profs give me the thumbs up Tuesday, I get to disappear down the rabbit hole for a few more months, come back out with lots of data, charts, and tested hypotheses, and do a full dissertation defense. And then, if I’m really good, I get some nice letters to put at the end of my name.

Sounds like fun, right? If so, then join in! The proposal defense takes place in the Tunheim Classroom Building, Room 111, on the DSU campus. If you won’t be in Madison on the 7th but would like to listen and submit questions, e-mail me, and I’ll send you a link to the online session.

Ten minutes is an awfully short time for public questions (give me the chance, and I’ll talk with an audience all day long!). If you have questions or feedback that don’t fit in the time Tuesday, I’ll be happy to take your input right here in the comment section. Fire away: just like Johnny Five, I need input!


The Nitty Gritty

  • Title: “Effects of Narrative on Interpersonal Connection and Communication in Health Social Networks”
  • Date: Tuesday, December 7th
  • Time: 11:00 am (CST)
  • Place: TCB (Tunheim Classroom Building), Room 111

Research Questions:

  1. Does storytelling influence the structure of an online social network?
  2. Do storytellers play a distinct role in sustaining an online social network?

I plan to investigate how people communicate within a health social network, a website providing a forum for interaction among individuals interested in specific health issues (see, for example, CureTogether, PatientsLikeMe, and ObesityHelp). I want to know whether people who use narrative more frequently—i.e., people who tell more stories, share more personal experiences—tend to have more “Friends” (in the Facebook sense of the word) and draw more responses with the online content they provide.

Some theory: Narrative theory says that we make meaning through stories. We are a storytelling species; that’s how we make sense of our world. Social cognitive theory says that social influences shape individual thought and action. Social network theory further supports the idea that our connections with our social network influence who we are and what we do.

Together, these theories suggest that in the context of a health social network, users will gravitate toward information that appeals to their sense of narrative. Personal narratives may provide context, establish authority, and indicate commonality, all of which may appeal more to health social network users than non-narrative information. If narrative content and the users producing it do generate more conversation and connections within health social networks, then that will suggest that storytellers provide distinct value to health social networks and play an important role in sustaining those networks.

In studying the influence of narrative content in health social networks, this dissertation tackles just one aspect of a larger research agenda on the influence of online social networks on health behavior. Like any other health intervention, health social networks matter only so far as they help patients get better, feel better, and live longer. Health social networks may expand our access to information and resources and thus help us make better, more satisfying health decisions. Health social networks may also expose us to all sorts of untested, ill-informed content that leads us to make worse health decisions than if we had just listened to doctor’s orders. Investigating the role of narrative content in health social networks is one step toward evaluating whether health social networks positively influence health behavior.

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