Conversational Inequality Through the Lens of Political Interruption

Clay H. Yoo, Jiachen Wang, Yuxi Luo, Kunal Khadilkar, Ashiqur R. KhudaBukhsh

Abstract: We present a novel dataset of dialogues containing interruption with an aim to conduct a large-scale analysis of interruption patterns of people from diverse backgrounds in terms of gender, race/ethnicity, occupation, and political orientation. Our dataset includes 625,409 dialogues containing interruptions found in 275,420 transcripts from CNN, Fox News, and MSNBC spanning between January 2000 and July 2021. From this large, unlabeled pool of interruptions, we release an annotated dataset consisting of 2,000 dialogues with fine-grained interruption labels. We use this dataset to train an interruption classifier and predict the interruption type of a given dialogue. Our results reveal that male speakers (in our collected samples) tend to talk more than female speakers, while female speakers interrupt more. Moreover, people tend to use less intrusive interruptions when talking to others sharing the same political belief. This pattern becomes more pronounced among news media with stronger political bias.