Sujin Kim1, Thomas Pinkerton, Nithya Ganesh. 1. Division of Biomedical Informatics, College of Public Health, University of Kentucky, Lexington, USA. sujinkim@uky.edu
Abstract
BACKGROUND: A novel strain of human influenza A (H1N1) posed a serious pandemic threat worldwide during 2009. The public's fear of pandemic flu often raises awareness and discussion of such events. OBJECTIVES: The goal of this study was to characterize major topical matters of H1N1 questions and answers raised by the online question and answer community Yahoo! Answers during H1N1 outbreak. METHODS: The study used Text Mining for SPSS Clementine (v.12; SPSS Inc., Chicago, IL) to extract the major concepts of the collected Yahoo! questions and answers. The original collections were retrieved using "H1N1" in search, keyword and then filtered for only "resolved questions" in the "health" category submitted within the past 2 years. RESULTS: The most frequently formed categories were as follows: general health (health, disease, medicine, investigation, evidence, problem), flu-specific terms (H1N1, swine, shot, fever, cold, infective, throat), and nonmedical issues (feel, North American, people, child, nations, government, states, help, doubt, emotion). The study found that URL data are fairly predictable: those providing answers are divided between ones dedicated to giving trustworthy information-from news organizations and the government, for instance-and those looking to espouse a more biased point of view. CONCLUSION: Critical evaluation of online sources should be taught to select the quality of information and improve health literacy. The challenges of pandemic prevention and control, therefore, demand both e-surveillance and better informed "Netizens."
BACKGROUND: A novel strain of human influenza A (H1N1) posed a serious pandemic threat worldwide during 2009. The public's fear of pandemic flu often raises awareness and discussion of such events. OBJECTIVES: The goal of this study was to characterize major topical matters of H1N1 questions and answers raised by the online question and answer community Yahoo! Answers during H1N1 outbreak. METHODS: The study used Text Mining for SPSS Clementine (v.12; SPSS Inc., Chicago, IL) to extract the major concepts of the collected Yahoo! questions and answers. The original collections were retrieved using "H1N1" in search, keyword and then filtered for only "resolved questions" in the "health" category submitted within the past 2 years. RESULTS: The most frequently formed categories were as follows: general health (health, disease, medicine, investigation, evidence, problem), flu-specific terms (H1N1, swine, shot, fever, cold, infective, throat), and nonmedical issues (feel, North American, people, child, nations, government, states, help, doubt, emotion). The study found that URL data are fairly predictable: those providing answers are divided between ones dedicated to giving trustworthy information-from news organizations and the government, for instance-and those looking to espouse a more biased point of view. CONCLUSION: Critical evaluation of online sources should be taught to select the quality of information and improve health literacy. The challenges of pandemic prevention and control, therefore, demand both e-surveillance and better informed "Netizens."