"Examining the Ordering of Rhetorical Strategies in Persuasive Requests" is work conducted by Omar Shaikh, Jiaao Chen, Jon Saad-Falcon, Polo Chau, and Diyi Yang at the Machine Learning Center at Georgia Tech.
This paper was accepted to the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP).
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 the 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.
Full paper: https://arxiv.org/pdf/2010.04625.pdf
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