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The recent proliferation of political and social forums has given rise to a wealth of freely accessible naturalistic arguments. People can “talk” to anyone they want, at any time, in any location, about any topic. Here we use a Mechanical Turk annotated corpus of forum discussions as a gold standard for the recognition of disagreement in online ideological forums. We analyze the utility of meta-post features, contextual features, dependency features and word-based features for signaling the disagreement relation. We show that using contextual and dialogic features we can achieve accuracies up to 68% as compared to a unigram baseline of 63%.