On predicting behavioral deterioration in online discussion forums

Abstract

Early detection of behavioral deterioration can be of great importance in preventing individuals' misbehavior from escalating in severity. This paper addresses the problem of behavioral deterioration in the context of online discussion forums. We propose a novel method that builds behavioral sequences from temporal information to gain a better understanding of behaviors exhibited by forum members, and then explores n-gram features to predict behavioral deterioration from consecutive combinations of sequential patterns corresponding to misbehavior. We conduct extensive experiments using real-world datasets and demonstrate the ability of our method to predict behavioral deterioration with a high degree of accuracy, as evaluated by F-1 scores. Our quantitative analysis of the model's performance yields F-1 scores of over 0.7. Specifically, we find that the best-performing model is linear SVM, with an average F-1 score of 0.74. Some future research avenues are proposed.