Probabilistic Approaches to Controversy Detection

Abstract

Recently, the problem of automated controversy detection has attracted a lot of interest in the information retrieval community. Existing approaches to this problem have set forth a number of detection algorithms, but there has been little effort to model the probability of controversy in a document directly. In this paper, we propose a probabilistic framework to detect controversy on the web, and investigate two models. We first recast a state-of-the-art controversy detection algorithm into a model in our framework. Based on insights from social science research, we also introduce a language modeling approach to this problem. We evaluate different methods of creating controversy language models based on a diverse set of public datasets including Wikipedia, Web and News corpora. Our automatically derived language models show a significant relative improvement of 18% in AUC over prior work,and 23% over two manually curated lexicons.