Challenge: Existing studies have examined persuasive discourses with regard to dynamics or lexical features.
Approach: They propose to annotate five types of EUs in a persuasive forum and propose a baseline neural model that identifies the EU boundary and type.
Outcome: The proposed model reveals that EUs definitively characterize online persuasive strategies.

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Annotating and Analyzing Semantic Role of Elementary Units and Relations in Online Persuasive Arguments (P19-2)

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Challenge: Existing studies on the features of persuasiveness focus on lexical features and argumentative features.
Approach: They propose an annotation scheme that captures the semantic role of arguments in a popular online persuasion forum, ChangeMyView.
Outcome: The proposed scheme captures the semantic role of arguments in a popular online persuasion forum, so-called ChangeMyView.
Detecting Winning Arguments with Large Language Models and Persuasion Strategies (2026.findings-eacl)

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Challenge: Recent studies have focused on predicting winning arguments, i.e., those that effectively convince a reader to adopt a certain opinion.
Approach: They propose to use large language models with a chain-of-thought framework to guide reasoning over six persuasion strategies to determine persuasiveness.
Outcome: The proposed approach leverages large language models with a chain-of-thought framework that guides reasoning over six persuasion strategies.
Analyzing Persuasion Strategies of Debaters on Social Media (2022.coling-1)

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Challenge: Existing studies on the analysis of persuasion in online discussions focus on the effectiveness of comments in individual discussions and ignore the effectiveness analysis of debaters over multiple discussions.
Approach: They propose to quantify debaters effectiveness in the online discussion platform "ChangeMyView" they aim to explore diverse insights into their persuasion strategies .
Outcome: The proposed analysis of debater effectiveness in the ChangeMyView subreddit reveals that debaters have different levels of effectiveness, behavioral characteristics and text stylistic features .
Corpus for Modeling User Interactions in Online Persuasive Discussions (2020.lrec-1)

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Challenge: Several studies have focused on the identification and classification of argumentative components and the argumentative relations between the components.
Approach: They propose an annotation scheme and corpus that captures user-generated inner-post arguments and inter-post relations between users in ChangeMyView.
Outcome: The proposed annotation scheme captures user-generated inner-post arguments and inter-post relations in ChangeMyView, a persuasive forum.
Let’s Make Your Request More Persuasive: Modeling Persuasive Strategies via Semi-Supervised Neural Nets on Crowdfunding Platforms (N19-1)

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Challenge: Existing models can't quantify persuasiveness of requests or extract successful persuasive strategies.
Approach: They propose a semi-supervised hierarchical neural network model to quantify persuasiveness and identify persuasive strategies in advocacy requests.
Outcome: The proposed method outperforms baseline models and offers increased interpretability of persuasive speech.
AutoPersuade: A Framework for Evaluating and Explaining Persuasive Arguments (2024.emnlp-main)

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Challenge: Existing tools for persuasion are well-equipped to identify which of a pre-existing set of messages is most persuasive, but they do not offer causal evidence on whether or how they have succeeded.
Approach: They propose a framework for identifying topical components of persuasive arguments that are autopersuade.
Outcome: The proposed framework validates the results through human studies and out-of-sample predictions.
Exploring the Role of Argument Structure in Online Debate Persuasion (2020.emnlp-main)

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Challenge: Existing work in NLP has shown that linguistic features extracted from debate text and features encoding the characteristics of the audience are both critical in persuasion studies.
Approach: They propose to incorporate argument structure features into an LSTM-based model to assess the persuasiveness of debates.
Outcome: The proposed model incorporates argument structure features to predict debaters that make the most convincing arguments on online debate forums.
Understanding User Resistance Strategies in Persuasive Conversations (2020.findings-emnlp)

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Challenge: Persuasive dialog systems have various usages, such as donation persuation and physical exercise persulasion.
Approach: They adopt a preliminary framework on persuasion resistance in psychology and build a fine-grained resistance strategy annotation scheme to analyze the persuitee's resistance strategies.
Outcome: The proposed system can understand and address user resistance strategies appropriately.
MA2P: A Meta-Cognitive Autonomous Intelligent Agents Framework for Complex Persuasion (2026.findings-acl)

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Challenge: Existing approaches to persuasion generate generic or weakly grounded responses even when such cues are identified.
Approach: They propose a meta-cognitive autonomous intelligent agent framework for complex persuasion that coordinates perception management, mental-state inference, strategy execution, memory maintenance, and performance evaluation.
Outcome: The proposed framework achieves a higher persuasion success rate than baselines.
PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings (2025.acl-long)

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Challenge: Visual persuasion uses visual elements to influence cognition and behaviors . lack of comprehensive data sets connect persuasiveness of images with personal information .
Approach: They propose to use a dataset to connect persuasiveness with personal information . they find psychological characteristics enhance the generation and evaluation of persuasive images .
Outcome: The proposed dataset provides persuasiveness scores of images evaluated by human annotators along with demographic and psychological characteristics.

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