From activities as simple as scheduling a meeting to those as complex as balancing a national budget, people take stances in negotiations and decision making. This project represents a focused exploration of spoken interactions to provide a characterization of linguistic factors associated with stance-taking and develop computational methods that exploit these features to automatically detect stance-taking behavior. Robust linguistic markers of stance-taking are identified through analysis of both controlled elicitations and archived recordings of Congressional hearings on the financial crisis. The former allow experimental comparisons to highlight sometimes subtle contrasts, while the latter enable validation and extension of those findings in real-world, high-stakes discussions. The analysis includes novel acoustic-phonetic measures of dynamic patterns in speech, such as vowel space scaling and pitch/energy velocity. Findings are validated via stance recognition experiments combining acoustic and lexical cues, which lay the foundation for automatic tracking of trends and shifts in political attitudes.