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Area of Science:

  • Health Informatics
  • Web Science
  • Computational Linguistics

Background:

  • The internet is a key source of health information for the public.
  • Health information online often does not match users' health literacy levels.
  • Identifying information providers and assessing content readability is crucial.

Purpose of the Study:

  • To identify prominent health information providers in the German Health Web (sGHW).
  • To quantify the readability and vocabulary of online health information.
  • To analyze the suitability of information sources for varying health literacy levels.

Main Methods:

  • Utilized a focused crawler to build the Sampled German Health Web (sGHW).
  • Applied PageRank to identify important websites and Latent Dirichlet Allocation (LDA) for topic modeling.
  • Conducted automated readability analysis using Flesch Reading Ease (FRE) and the 4th Vienna formula (WSTF).
  • Assessed vocabulary suitability with a Support Vector Machine classifier.

Main Results:

  • Collected over 14 million health-related web pages, forming a large web graph.
  • Identified 11 major themes, with "Research & Science" and "Illness & Injury" being most prevalent.
  • Found that 84.63% of sites had difficult or very difficult readability (WSTF ≥ 12) and 89.70% were hard to read (FRE ≤ 49).
  • 44.00% of sites used vocabulary suitable for a lay audience.

Conclusions:

  • Major health information hubs and topics within the sGHW were identified.
  • The study highlights significant readability challenges in online health information.
  • Low readability may create barriers for patients, despite appropriate vocabulary.