how valuable is medical social media data? content analysis of the medical web

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


N.Y.U.S.T.

I. M.Outline

Motivation Objective Methodology Experiments Conclusion Comments

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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.

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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 questionwhether existing text and content analysis tools are able to process medical social media data and to identify relevant (medical) information out of them.


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.

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N.Y.U.S.T.

I. M.Methodology

Research questions Data collection Data analysis

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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 Forums2. Medical weblogs3. Reviews4. Wikis and encyclopedias

1. Assessing the medical content2. Assessing the information type of documents


N.Y.U.S.T.

I. M.Methodology

Data collection

1. Query&Answer Forums

2. Medical weblogs

3. Reviews

4. Wikis

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N.Y.U.S.T.

I. M.Methodology

Data analysis Assessing the medical content

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SeReMeDMedicaltext

A semantic representation


N.Y.U.S.T.

I. M.Methodology

Data analysis Assessing the information type of documents

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Assumptions1. Extensive use of medical terminology is an indication for informative content.

2. Adjectives are an indication for affective content.


N.Y.U.S.T.

I. M.Experiments

The manually classified posts of physicians and patients

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N.Y.U.S.T.

I. M.Experiments

Result 1

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N.Y.U.S.T.

I. M.Experiments

Result 2

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N.Y.U.S.T.

I. M.Experiments

Result 3

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N.Y.U.S.T.

I. M.Experiments

Excluded data written by nurses

Training results

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Assumptionmore patient-written posts are informative than nurse-written posts.

contradiction


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.

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Drug reviews and AskDrWiki

weblogs and Q&A

AskDrWiki and MedlinePlus


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

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how valuable is medical social media data? content analysis of the medical web

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