Assessment of H1N1 questions and answers posted on the Web.

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."

[1]  David M. Pennock,et al.  Using internet searches for influenza surveillance. , 2008, Clinical infectious diseases : an official publication of the Infectious Diseases Society of America.

[2]  MARILYN DOMAS WHITE,et al.  Questions in reference interviews , 1998, J. Documentation.

[3]  Armin R Mikler,et al.  Using Web and Social Media for Influenza Surveillance , 2010, Advances in experimental medicine and biology.

[4]  R. Haynes,et al.  Optimal search strategies for retrieving systematic reviews from Medline: analytical survey , 2004, BMJ : British Medical Journal.

[5]  R. Haynes,et al.  Medline : analytical survey scientifically strong studies of diagnosis from Optimal search strategies for retrieving , 2004 .

[6]  Carol Lefebvre,et al.  Identifying systematic reviews in MEDLINE: developing an objective approach to search strategy design , 1998, J. Inf. Sci..

[7]  Lada A. Adamic,et al.  Knowledge sharing and yahoo answers: everyone knows something , 2008, WWW.

[8]  T. Thompson A Conversation with Secretary Tommy Thompson: In February, Tommy G. Thompson Stepped Down as Governor of Wisconsin to Serve as Secretary of the U.S. Department of Health and Human Services (HHS) , 2001 .

[9]  Jeremy Ginsberg,et al.  Detecting influenza epidemics using search engine query data , 2009, Nature.

[10]  R. M. Wolfe,et al.  Vaccine Criticism on the World Wide Web , 2005, Journal of medical Internet research.

[11]  R. Brian Haynes,et al.  Enhancing Retrieval of Best Evidence for Health Care from Bibliographic Databases: Calibration of the Hand Search of the Literature , 2001, MedInfo.

[12]  D. Chung,et al.  Characteristics of cancer blog users. , 2007, Journal of the Medical Library Association : JMLA.

[13]  Marc Lipsitch,et al.  Studies Needed to Address Public Health Challenges of the 2009 H1N1 Influenza Pandemic: Insights from Modeling , 2010, PLoS medicine.

[14]  K. A. McKibbon,et al.  Optimal search strategies for retrieving scientifically strong studies of treatment from Medline: analytical survey , 2005, BMJ : British Medical Journal.

[15]  Wendy W. Chapman,et al.  Analysis of Web Access Logs for Surveillance of Influenza , 2004, MedInfo.

[16]  Alan R. Aronson,et al.  Semi-Automatic Indexing of Full Text Biomedical Articles , 2005, AMIA.

[17]  G. Eysenbach Infodemiology and Infoveillance: Framework for an Emerging Set of Public Health Informatics Methods to Analyze Search, Communication and Publication Behavior on the Internet , 2009, Journal of medical Internet research.

[18]  Charles E. Kahn,et al.  Effective Metadata Discovery for Dynamic Filtering of Queries to a Radiology Image Search Engine , 2008, Journal of Digital Imaging.