13. Which social media should I consider using?
Social networks serve unique populations, so you should choose judiciously. Blogs and LinkedIn groups tend to support discussions on areas of common interest (professional, medical, scientific, etc.).Pinterest, Messenger, Instagram, WhatsAPP can be used as resources. Facebook, with 1,500,000,000 monthly active users, is the largest social network and like Twitter, it allows members to exchange messages on more personal topics, including social issues, healthcare, and quality of service. Searches on YouTube and Vimeo, can provide you with information on product usability, human error, design defects, accidents, etc. For a listing of several hundred social media networking sites go to: https://en.wikipedia.org/wiki/List_of_social_networking_websites
Consider this unique use of social media. In a 2015 study by hundreds of social media sites,
Psychological language on Twitter predicts county-level heart disease mortality. Eichstaedt et al. demonstrated that the psychological language used on Twitter was a better predictor of a county’s level of heart disease mortality than a model that “combined 10 common demographic, socioeconomic, and health risk factors, including smoking, diabetes, hypertension, and obesity” (Abstract).
Identifying and “listening to” social media users discussing topics of interest to you can provide insights that may answer your questions and/or provide you with insights that will allow you to formulate better questions and develop more informed response options. Smith, Rainie, Shneiderman, and Himelboim (2014) used a social media network analysis tool, based on link analysis, to identify patterns in Tweets that occurred within different types of groups (polarizing crowds, tight crowds, brand clusters, community clusters, broadcast networks, and support networks). By examining the topics discussed on these various platforms, you can identify conversations that you would like to monitor and individuals/groups that could provide information pertinent to your questionnaire and/or identify informed and motivated individuals who would qualify as respondents.
Mitchell and Guskin (2013) describe the Twitter population as younger, more mobile, and better educated than the population in general. Brickman Butta (2012) provides insights into the advantages and disadvantages of using Facebook as a source of information. Markoff (2016) cautions that, in the era of false news, a large number of Tweets may have been sent by chatbots, which is another reason to be judicious when using social media data.
Brickman Bhutta, C. (2012). Not by the book: Facebook as a sampling frame. Sociological Methods & Research, 41(1), 57-88. Retrieved from: http://www.thearda.com/workingpapers/download/Not%20by%20the%20Book%20-%20Bhutta.doc
Eichstaedt, J. C., Schwartz, H. A., Kern, M. L., Park, G., Labarthe, D. R., Merchant, R. M., … & Seligman, M. E. (2015). Psychological language on Twitter predicts county-level heart disease mortality. Psychological Science, 26(2), 159-169. Retrieved from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4433545/
Markoff, J, (November 17, 2016). Automated pro-Trump bots overwhelmed pro-Clinton messages, researchers say. Retrieved from https://www.nytimes.com/2016/11/18/technology/automated-pro-trump-bots-overwhelmed-pro-clinton-messages-researchers-say.html?emc=eta1&_r=0
Mitchell, A. & Guskin, E. (November 4, 2013). Twitter news consumers: Young, mobile and educated. Retrieved from http://www.journalism.org/2013/11/04/twitter-news-consumers-young-mobile-and-educated/
Smith, M. A., Rainie, L., Shneiderman, B., & Himelboim, I. (2014). Mapping Twitter topic networks: From polarized crowds to community clusters. Pew Research Center, 20. Retrieved from http://www.pewinternet.org/2014/02/20/mapping-twitter-topic-networks-from-polarized-crowds-to-community-clusters/