Interactive Journal of Medical Research (Jul 2013)
Word Frequency and Content Analysis Approach to Identify Demand Patterns in a Virtual Community of Carriers of Hepatitis C
Abstract
BackgroundOrkut, a Brazilian virtual social network, is responsible for popularization of the Internet among people of low income and educational level. It’s observed that rapid growth of virtual communities can be reached by low cost Internet access in community local area network houses. Orkut poses an important social resource for Brazilian patients with chronic conditions like hepatitis C virus (HCV) carriers, who face several obstacles in adapting to everyday difficulties. ObjectiveIdentify Patterns of Recurring Demands (PRD) expressed in messages posted by members of virtual communities dedicated to HCV carriers. MethodsPre-selection: we identified terms commonly associated to HCV on generic Internet searches (primary Keywords - Kps); Kps were used to identify the most representative HCV communities in a virtual community site (Orkut); all messages published along 8 years on all topics of the community were collected and tabulated; the word frequency was used to construct a “word cloud” (graphic representation of the word frequency) on which was applied a content analysis technique. ResultsThe most cited terms expressed: search for information about medications (prescribed and “forbidden”); emphasis on counting time, which were interpreted as surviving expectations; frequent mention of God, doctors, and “husbands” (female carriers were 68%). These elements provided material for further research – they will be useful in the construction of categories in discourse analysis. ConclusionsThe present work is a disclosure of preliminary findings considered original and promising. The word frequency/content analysis approach expressed needs of social support and material assistance that may provide subsidies for further qualitative approach and public health policies aimed to HCV carriers. The study of PRD by word frequency may be useful in identifying demands underestimated by other means.