Automated annotation of social behavior in conversation is necessary for large-scale analysis of real-world conversational data. Important behavioral categories, though, are often sparse and often appear only in specific subsections of a conversation. This makes supervised machine learning difficult, through a combination of noisy features and unbalanced class distributions. We propose within-instance content selection, using cue features to selectively suppress sections of text and biasing the remaining representation towards minority classes. We show the effectiveness of this technique in automated annotation of empowerment language in online support group chatrooms. Our technique is significantly more accurate than multiple baselines, especially when prioritizing high precision.
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