Examining the Ordering of Rhetorical Strategies in Persuasive Requests

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Authors Omar Shaikh, Jiaao Chen, Jon Saad-Falcon, Duen Horng Chau, Diyi Yang arXiv ID 2010.04625 Category cs.CL: Computation & Language Cross-listed cs.AI, cs.LG Citations 9 Venue Findings Last Checked 5 months ago
Abstract
Interpreting how persuasive language influences audiences has implications across many domains like advertising, argumentation, and propaganda. Persuasion relies on more than a message's content. Arranging the order of the message itself (i.e., ordering specific rhetorical strategies) also plays an important role. To examine how strategy orderings contribute to persuasiveness, we first utilize a Variational Autoencoder model to disentangle content and rhetorical strategies in textual requests from a large-scale loan request corpus. We then visualize interplay between content and strategy through an attentional LSTM that predicts the success of textual requests. We find that specific (orderings of) strategies interact uniquely with a request's content to impact success rate, and thus the persuasiveness of a request.
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