ENGLISH ABSTRACT: In online discussion forums, comment moderation systems are often faced with the problem of establishing the value of an unseen online comment. By knowing the value of comments, the system is empowered to establish rank and to
enhance the user experience. It is also useful for identifying malicious users that consistently show behaviour that is detrimental to the community.
In this paper, we investigate and evaluate various machine
learning techniques for automatic comment scoring. We derive
a set of features that aim to capture various comment
quality metrics (like relevance, informativeness and spelling)
and compare it to content-based features. We investigate the
correlation of these features against the community popularity
of the comments. Through investigation of supervised
learning techniques, we show that content-based features
better serves as a predictor of popularity, while quality-based
features are better suited for predicting user engagement.
We also evaluate how well our classifier based rankings correlate
to community preference.