Intelligent Database Systems Lab N.Y.U.S.T. I. M. How valuable is medical social media data? Content analysis of the medical web Presenter :Tsai Tzung Ruei Authors :Kerstin Denecke, Wolfgang Nejdl InfSci 2009 國立雲林科技大學 National Yunlin University of Science and Technology

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Outline Motivation Objective Methodology Experiments Conclusion Comments 2

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation It is still an open question where to search for complying a specific information need due to the large amount and diversity of information available. Finding the best knowledge source to comply a specific information need is difficult, because relevant information can be either hidden in web pages or encapsulated in social media data such as blogs and Q&A portals. 3 We focus on health-related information provided in the Internet. Why? First, health-related experiences and medical histories offer unique data for research purposes, for practitioners, and for patients. Second, it is still an open question whether existing text and content analysis tools are able to process medical social media data and to identify relevant (medical) information out of them.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objective To give an overview on content differences in the various social media resources on health-related topics. In particular, the content of medical Question & Answer Portals, medical weblogs, medical reviews and Wikis is compared. 4

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Research questions Data collection Data analysis 5 1. Which topics do the different health-related web resources focus on? 2. What similarities and differences in content exist between different medical social media data resources? 3. To what extent do medical blogs contain information or experiences? 1. Query&Answer Forums 2. Medical weblogs 3. Reviews 4. Wikis and encyclopedias 1. Assessing the medical content 2. Assessing the information type of documents

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Data collection 1. Query&Answer Forums 2. Medical weblogs 3. Reviews 4. Wikis 6

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Data analysis  Assessing the medical content 7 SeReMeD Medical text A semantic representation

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology Data analysis  Assessing the information type of documents 8 Assumptions 1. Extensive use of medical terminology is an indication for informative content. 2. Adjectives are an indication for affective content.

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments The manually classified posts of physicians and patients 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Result 1 10

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Result 2 11

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Result 3 12

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Excluded data written by nurses Training results 13 Assumption more patient-written posts are informative than nurse-written posts. contradiction

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Conclusion MAJOR CINTRIBUTION  Several conclusions can be drawn from the aforementioned results. Our hypotheses proved only to be partly true.  provide an overview on the content available in the (medical) Web.  Disorders and Physiology  Anatomy and Procedures  Drugs FUTURE WORK  Plan to test the proposed method to identify informative (or ‘good’) answers to health-related queries in rather general Q&A portals such as Yedda, where answers can be given by any person. This could help to filter out comments and irrelevant answers. 14 Drug reviews and AskDrWiki weblogs and Q&A AskDrWiki and MedlinePlus

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments Advantage  To help to identify the best-suited information source in order to comply a specific information need.  A potential application of our algorithm is its exploitation for sorting or ranking search results within a blog post search engine. Drawback  This work is unique in a sense that such an analysis was still missing in particular for the domain of medicine. Application  information retrieval 15