Scalable discovery of contradictions on the web.

Our study addresses the problem of large-scale contradiction detection and management, from data extracted from the Web. We describe the first systematic solution to the problem, based on a novel statistical measure for contradictions, which exploits first- and second-order moments of sentiments. Our approach enables the interactive analysis and online identification of contradictions under multiple levels of time granularity. The proposed algorithm can be used to analyze and track opinion evolution over time and to identify interesting trends and patterns. It uses an incrementally updatable data structure to achieve computational efficiency and scalability. Experiments with real datasets show promising time performance and accuracy.