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Identifying peer experts in online health forums.

V G Vinod Vydiswaran1, Manoj Reddy2

  • 1Department of Learning Health Sciences, University of Michigan, 300 N. Ingalls St, Ann Arbor, 48108, MI, USA. vgvinodv@umich.edu.

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Summary

Peer experts in online health forums are lay users with credibility. Identifying them using activity and post features can enhance forum trustworthiness and information reliability.

Keywords:
Health forum analysisOnline health communitiesPeer experts

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Area of Science:

  • Health Informatics
  • Social Computing
  • Information Science

Background:

  • Online health forums are popular platforms for peer support and information exchange.
  • Peer experts are defined as lay users with demonstrated credibility and expertise gained through personal experience.
  • Identifying peer experts is crucial for understanding and improving online health communities.

Purpose of the Study:

  • To motivate the need for identifying peer experts in online health forums.
  • To characterize the interaction behavior and development of peer expertise.
  • To develop and evaluate methods for distinguishing peer experts from novice users.

Main Methods:

  • Analysis of member profiles and activity in a popular online health forum.
  • Study of temporal patterns in user comments to understand expertise development.
  • Training a supervised classifier using activity level, textual features, and post progression.

Main Results:

  • A support vector machine classifier with a radial basis function kernel was optimal.
  • Key features for identification included semantic word classes and higher mean user activity.
  • The classifier demonstrated promising preliminary results in distinguishing peer experts.

Conclusions:

  • A novel class of 'peer experts' within health forums has been defined.
  • Preliminary methods show promise in distinguishing peer experts from novices.
  • Identifying peer expertise can enhance the reliability and trustworthiness of online health information.