This paper presents models for detecting agreement/disagreement between speakers in English and Arabic broadcast conversation shows. We explore a variety of features, including lexical, structural, durational, and prosodic features. We experiment with these features using Conditional Random Fields models and conduct systematic investigations on efficacy of various feature groups across languages. Sampling approaches are examined for handling highly imbalanced data. Overall, we achieved 79.2% (precision), 50.5% (recall), 61.7% (F1) for agreement detection and 69.2% (precision), 46.9% (recall), and 55.9% (F1) for disagreement detection, on English broadcast conversation data; and 89.2% (precision), 30.1% (recall), 45.1% (F1) for agreement detection and 75.9% (precision), 28.4% (recall), and 41.3% (F1) for disagreement detection, on Arabic broadcast conversation data.