Abstract
Power relationships express one party’s dominance, control, influence, and authority over the other. In this article, and using state-of-the-art AI tools, we show that power relationships can be automatically identified in textual data. Generating thousands of synthetic utterances expressing either dominance or compliance, we trained/ran three models that showed good classification performance. Moreover, using GPT-4, we present a novel method for presenting power asymmetry in conversations and visualizing the dynamics of power relationships over time. This methodology is presented and illustrated by analyzing a case study—The play Pygmalion by George Bernard Show.
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2024
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