Modeling what makes a request persuasive— eliciting the desired response from a reader— is critical to the study of propaganda, behavioral economics, and advertising. Yet current models can't quantify the persuasiveness of requests or extract successful persuasive strategies. Building on theories of persuasion, we propose a neural network to quantify persuasiveness and identify the persuasive strategies in advocacy requests. Our semi-supervised hierarchical neural network model is supervised by the number of people persuaded to take actions and partially supervised at the sentence level with human-labeled rhetorical strategies. Our method outperforms several baselines, uncovers persuasive strategics-offering increased interpretability of persuasive speech-and has applications for other situations with document-level supervision but only partial sentence supervision.
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