The present paper is devoted to a short analysis of four TED presentations by Christakis (2010), Cukier (2014), Golbeck (2013), and Selanikio (2014), who discuss the topic of data analytics. Two of the authors consider the issue directly in the context of healthcare, but it is possible to apply the information from all of the presentations to the field.
There are multiple applications of social media in healthcare, but from the point of view of analytics, it is a useful source of crucial information. Social media is an umbrella term for multiple forms of electronic networks that are primarily aimed at moving “from passive consumption to active creation of diverse types of content by Internet users” (Kotov, 2015, p. 309). As pointed out by Christakis (2010), social networks are not a new invention; people have been creating networks throughout their history by forming relationships with people who are bound to be in relationships with other people.
These real-world networks are governed by a variety of principles and rules that are defined by human biology and psychology, their social norms, and even some mathematical laws. For example, the nodes of the networks (people) can be characterized by different numbers of relationships, and the relationships can be of various kinds (friendship, spousal connection, and so on). These characteristics can define, for instance, the chance of a person being affected by a contagious phenomenon. Therefore, by studying any form of network (social media included), people can gain the information that allows predicting events based on the found rules. Christakis (2010) points out that one of such predictable events is epidemics.
It is noteworthy that socially contagious phenomena are not limited to pathogen-related ones. For instance, Mitra and Padman (2014) offer an example of tracking patient health plan choices with the aim of spreading patient engagement in their personal health through social media, which is a clear illustration of the use of analytics to trigger a contagious event. Christakis (2010) points out that any socially contagious phenomenon is bound to manifest itself in social networks since it is spread through these networks, which makes it detectable. Since prediction is based on gathering and analyzing information, it is apparent that social networks can be used to gain data of importance for the agenda. As a result, a fashion trend can be tracked as well as a disease. For example, Golbeck (2013) confirms that it is possible to use social media data for predicting and inferring, but she mostly discusses marketing applications of the ability, which she considers largely unethical.
Indeed, there are barriers to social media use, including feasibility concerns and ethical issues. Cukier (2014) mentions that there are dangers in big data, predominantly those connected to its misuse. It is a very significant issue that Golbeck (2013) and Christakis (2010) want to resolve through science (for example, education and protecting applications). Also, Selanikio (2014) points out that the Internet is not omnipotent for the time being: for example, the statistics of child deaths for some countries are mainly unavailable. However, Selanikio (2014) also admits that the Internet is very convenient for searches and has a greater speed and potential for accuracy than the traditional means. In fact, Selanikio (2014) demonstrates that traditional methods are almost useless when big data is considered. At the same time, as pointed out by Cukier (2014), big data promises great improvements, which implies that healthcare is going to employ it (Raghupathi & Raghupathi, 2014). As a result, the four presentations demonstrate that healthcare can and should employ social media while bearing in mind the multiple issues and searching for the means of resolving them, for example, through science.
Christakis, N. (2010). Web.
Cukier, K. (2014). Web.
Golbeck, J. (2013). Web.
Kotov, A. (2015). Social media analytics for healthcare. In C. K. Reddy & C. C. Aggarwal (Eds.), Healthcare data analytics (pp. 309-334). New York, NY: CRC Press.
Mitra, S. & Padman, R. (2014). Journal of Cases on Information Technology, 16(1), 73-89. Web.
Raghupathi, W. & Raghupathi, V. (2014). Big data analytics in healthcare: promise and potential. Health Information Science And Systems, 2(1), 3. Web.
Selanikio, J. (2013). Web.