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Crawling the German Health Web: Exploratory Study and Graph Analysis.

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A focused web crawler effectively gathered German Health Web data, identifying key information hubs and content providers. This approach enables analysis of health information trends and the development of specialized search engines.

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Area of Science:

  • Computational linguistics
  • Web science
  • Information retrieval

Background:

  • The internet is a vital source of health information, but its vastness makes manual tracking challenging.
  • Understanding the health domain online requires identifying information hubs, prestigious content providers, and emerging trends.
  • Automatic web crawling offers a computational approach to analyze large-scale health information.

Purpose of the Study:

  • To demonstrate the suitability of a focused crawler for acquiring the German Health Web (GHW).
  • To analyze the GHW's structure, including size, key providers, and public/private stakeholder ratios.
  • To share experiences in developing and operating a scalable health web crawler.

Main Methods:

  • A support vector machine classifier was trained to differentiate health-related from non-health-related web pages.
  • The open-source StormCrawler framework was extended to implement a focused crawler.
  • The crawler operated for 227 days, with performance evaluated by harvest rate and recall.

Main Results:

  • The text classifier achieved high accuracy (0.937-0.966), precision (0.934-0.954), and recall (0.944-0.989).
  • The crawl yielded 13.5 million relevant and 119.5 million non-relevant pages, with a harvest rate of 19.76% and recall of 0.821.
  • The GHW graph comprises 215,372 nodes and 403,175 edges, with public institutions dominating top-ranked sites (40%).

Conclusions:

  • The focused crawler is effective for acquiring a significant portion of the GHW.
  • The collected data facilitates the identification of major health information hubs and content providers.
  • Future applications include assessing health trends and developing health-specific search engines.