Jamison-Powell, S., Linehan, C., Daley, L., Garbett, A., & Lawson, S. (2012). “I can’t get no sleep”: Discussing #insomnia on Twitter. Proceedings of the 2012 ACM annual conference on Human Factors in Computing Systems (pp. 1501–1510). Retrieved from http://dl.acm.org/citation.cfm?id=2208612
Purpose of the Research:
(1) To explore the role of social media in the discussion of mental health issues, and with particular reference to insomnia and sleep disorders: whether individuals are disclosing their insomnia related information online, if so, what they are disclosing.
(2) To provide analytical results to those in the interaction design community interested in integrating online social media systems in health interventions.
A mixed-method approach is used to analyze 18,901 tweets using hashtag #insomnia. First, an automatic content analysis is used to confirm “whether individuals are disclosing their insomnia related information online”. Second, a qualitative thematic analysis is performed to answer the question “what individuals are disclosing about their insomnia”.
(1) Content analysis using a software named Linguistic Inquiry and Word Count (LIWC) . Simply put, this software contains a pre-defined English dictionary which includes categories of negative words and positive words, as well as word roots related to health, time, anger, anxiety, sadness, etc. The software performs frequency counting and statistical analysis of the input text based on this dictionary. In order to compare these #insomnia tweets with the general posts on Twitter, 17,532 random general non-specific tweets on Twitter were used as a control group. LIWC is used to compare the #insomnia tweets and the non-specific tweets, and produces table 1 below.
(2) Inductive thematic analysis. The authors acknowledge that the LIWC software analysis results maybe inaccurate because of the subjectivity and ambiguity of informal human language, as well as the large amount of abbreviations used in tweets due to the character limit. So they perform inductive thematic analysis. A sample of 749 is taken from every 25th tweet of the original 18,901 tweets. One researcher first read the 749 tweets for several times, and comes up with 49 categories, and gives the results to two other researchers. The three of them discuss and decide on 45 categories. Then they take another sample of 514 tweets from every 35th tweet of the original 18,901 tweets, and code the 514 tweets into the 45 categories independently. Inter-rater reliability is also examined. All the researchers finally refine the categories into a hierarchy in figure 1.
(1) Results in table 1 below confirms that individuals do disclose insomnia related information online.
(2) Inductive thematic analysis categorize the content of #insomnia tweets into two major categories: describing the insomnia experience and coping with insomnia.
The authors claim that this paper, in a generic sense, contributes to a growing body of literature regarding online self-disclosure. Based on the analysis results, they come up with a brief design requirement for a social network structure allowing insomnia therapy to be delivered in an online group. “The structure would be a useful format for users to exchange support and practical information. To allow users to exercise catharsis, a space could be provided to disclose symptoms and frustrations together with an area where the users can “communicate with” insomnia. To comply with the principles of sleep hygiene, a digital barrier should be in place in any interactive platform, this would reduce the amount of access a user has to an online support network at times when they should be refraining from stimulating environments. However, rather than simply restrict all access a “life-line” should be kept open, providing information on good sleep hygiene and progressive muscle relaxation exercises to aid sleep. “
This paper relates to what we learned in CGT512 how to analysis qualitative data and thus produce design requirements.
This paper also relates to my research interests in social media analytics for instrumenting the social and emotional aspects of college students’ life. It provides information on useful software and methods.