![]() Finally, I designed and implemented a system which can automatically collect and analyze online news comments to help researchers, public health workers, and policy makers to better monitor and understand the public's opinion on issues such as controversial health-related topics. In the second study, I estimated the public's view on the ac by applying sentiment analysis to four years of Twitter data, and demonstrated that the the rates of positive/negative responses measured by tweet sentiment are in general agreement with the results of Kaiser Family Foundation Poll. The outputs of two methods cover most of the public concerns of MMR vaccines reported in previous survey studies. The first study applied topic modeling and text summarization to automatically distill users' key concerns about the purported link between autism and vaccines. Furthermore, I conducted two studies to evaluate the effectiveness of understanding public opinions on controversial medical and public health issues from social media information using text-mining techniques. Through a review of tweets mentioning eye-related medical concepts identified by MetaMap, I diagnosed the common reasons of tweets mislabeled by natural language processing tools tuned for biomedical texts, and trained a classifier to exclude non medically-relevant tweets to increase the precision of the extracted data. To understand the availability and characteristics of health-related information in social media, I first identified the users who seek health information online and participate in online health community, and analyzed their motivations and behavior by two case studies of user-created groups on MedHelp and a diabetes online community on Twitter. In this thesis, I developed methods to extract health-related information from multiple sources of social media data, and conducted studies to generate insights from the extracted information using text-mining techniques. This information could be utilized for the purposes of public health surveillance as well as solicitation of public opinions. Social media websites are increasingly used by the general public as a venue to express health concerns and discuss controversial medical and public health issues. The author reflects on the implications of these findings for online social networks devoted to patients and the patient experience. This comprehensive review of the literature of health care (PubMed MEDLINE, CINAHL), library science, and information science (Library and Information Science and Technology Abstracts, Library and Information Science Abstracts, and Library Literature) examines the research domains in which consumer and patient language has been explored.Ĭonsumer contributions to controlled vocabulary appear to be seriously under-researched inside and outside of health care. How have information systems traditionally incorporated these languages in their controlled vocabularies and thesauri? How do system builders know what consumers and patients say? However, patient language poses considerable challenges for health communication and networking. "Tags," user-generated descriptors functioning as labels for user-generated content, are increasingly important to social networking, and the language used by patients is thus becoming important for knowledge representation in these systems. Thirty-nine percent of patients in the United States identified themselves as users of social networks in a recent survey. Online social networking sites are web services in which users create public or semipublic profiles and connect to build online communities, finding like-minded people through self-labeled personal attributes including ethnicity, leisure interests, political beliefs, and, increasingly, health status.
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