Probabilistic Approaches to Controversy Detection

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 controversy 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 extensively evaluate different methods of creating controversy language models based on a diverse set of public datasets including Wikipedia, Web and News corpora. Based on insights from social science research, we build language models of controversy and introduce formal models of both prior work and our own. Our automatically derived language models show a significant relative improvement of 18% in AUC over prior work, and 23% over two manually curated lexicons.