This article introduces a text classification framework designed to identify helpful user-generated content within online knowledge-sharing communities. Built upon the Knowledge Adoption Model, this framework incorporates dimensions of argument quality and source credibility, significantly enhancing traditional text classification techniques that rely solely on lexical features. Empirical evaluations using data from a popular online community confirm the effectiveness of including these multidimensional factors in improving content helpfulness identification.
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Wang, Gang, Xiaomo Liu, and Weiguo Fan. "A knowledge adoption model based framework for finding helpful user-generated contents in online communities." (2011).
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