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This paper looks at the problem of detecting a particular type of social behavior in discussions: attempts to establish credibility as an authority on a particular topic. Using maximum entropy modeling, we explore questions related to feature extraction and turn vs. discussion-level modeling in experiments with online discussion text given only a small amount of labeled training data. We also introduce a method for learning interaction words from unlabeled data. Preliminary experiments show that a word-based approach (as used in topic classification) can be used successfully for turn-level modeling, but is less effective at the discussion level. We also find that sentence complexity features are almost as useful as lexical features, and that interaction words are more robust than the full vocabulary when combined with other features.