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

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