Nayer Wanas, Motaz El-Saban, Heba Ashour, and Waleed Ammar
Online discussions forums, known as forums for short, are
conversational social cyberspaces constituting rich repositories of
content and an important source of collaborative knowledge.
However, most of this knowledge is buried inside the forum
infrastructure and its extraction is both complex and difficult. The
ability to automatically rate postings in online discussion forums,
based on the value of their contribution, enhances the ability of
users to find knowledge within this content. Several key online
discussion forums have utilized collaborative intelligence to rate
the value of postings made by users. However, a large percentage
of posts go unattended and hence lack appropriate rating.
In this paper, we focus on automatic rating of postings in online
discussion forums. A set of features derived from the posting
content and the threaded discussion structure are generated for
each posting. These features are grouped into five categories,
namely (i) relevance, (ii) originality, (iii) forum-specific features,
(iv) surface features, and (v) posting-component features. Using a
non-linear SVM classifier, the value of each posting is categorized
into one of three levels High, Medium, or Low. This rating
represents a seed value for each posting that is leveraged in
filtering forum content. Experimental results have shown
promising performance on forum data.
In CIKM, WICOW workshop