Omar Shaikh

Examining the Ordering of Rhetorical Strategies in Persuasive Requests

Jiaao Chen
Jon Saad-Falcon
Diyi Yang
Findings of the Association for Computational Linguistics: Empirical Methods in Natural Language Processing (EMNLP Findings), 2020

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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BibTeX

			
@inproceedings{shaikh-etal-2020-examining,
  title = "Examining the Ordering of Rhetorical Strategies in Persuasive Requests",
  author = "Shaikh, Omar  and
    Chen, Jiaao  and
    Saad-Falcon, Jon  and
    Chau, Polo  and
    Yang, Diyi"
  booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2020",
  month = nov,
  year = "2020",
  address = "Online",
  publisher = "Association for Computational Linguistics",
  url = "https://www.aclweb.org/anthology/2020.findings-emnlp.116",
  pages = "1299--1306",
}