This paper empirically analyzes bot activity in contentious Twitter conversations using case studies from the Asia-Pacific. Bot activity is measured and characterized using a series of interoperable tools leveraging dynamic network analysis and machine learning. We apply this novel and flexible methodological framework to derive insights about information operations in three contexts: the senatorial elections in the Philippines, the presidential elections in Indonesia, and the relocation of a military base in Okinawa. Varying levels of bot prevalence and influence are identified across case studies. The presented findings demonstrate principles of social cyber-security in concrete settings and highlight conceptual and methodological issues to inform further development of analytic pipelines in studying online information operations.