Harnessing global expertise: A comparative study of expertise profiling methods for online communities

Abstract

Building expertise profiles in global online communities is a critical step in leveraging the range of expertise available in the global knowledge economy. In this paper we introduce a three-stage framework that automatically generates expertise profiles of online community members. In the first two stages, document-topic relevance and user-document association are estimated for calculating users' expertise levels on individual topics. We empirically compare two state-ofthe-art information retrieval techniques, the vector space model and the language model, with a Latent Dirichlet Allocation (LDA) based model for computing document-topic relevance as well as the direct and indirect association models for computing user-document association. In the third stage we test whether a filtering strategy can improve the performance of expert profiling. Our experimental results using two real datasets provide useful insights on how to select the best models for profiling users' expertise in online communities that can work across a range of global communities.

User adoption of physician's replies in an online health community: An empirical study

“…Source credibility captures authority and reliability of information (Chaiken, 1980), and is composed of two parts: competence and trustworthiness. Physicians in the OHC are registered under their real names to stimulate perceived trustworthiness, while competence is reflected in the physicians' knowledge and expertise (Lewandowsky, Little, & Kalish, 2007;Liu, Wang, Johri, Zhou, & Fan, 2012). Knowledge could not be directly measured in our study; we measured it using data on physician's past online experience in terms of adoption rate and user satisfaction, collected from the physician's profile.…”

Where your photo is taken: Geolocation prediction for social images

“…We use LMs to represent user profiles. The user profiling in our work profiles the historical tags used by individuals, and the purpose is different from the previous work on profiling users' expertise (e.g., Liu, Wang, Johri, Zhou, & Fan, ). As discussed in this article, the proposed methods improve on the prediction accuracy over the method by Serdyukov et al.…”

Mining Shapes of Expertise in Online Social Q&A Communities

“…Several studies attempt to measure expertise in an online Q&A community [3,6,9,11,12], mostly to find experts who can best answer a specific new question. Some studies find expertise of users in different 'topics' of expertise [6,9,11].…”

“…Some studies find expertise of users in different 'topics' of expertise [6,9,11]. For example, Liu et al [6] found question topics using Latent Dirichlet Allocation (LDA) in a StackExchange community. But we found these topics to be too specific for characterizing expertise shapes, e.g.…”