Exploring the Role of Argument Structure in Online Debate Persuasion
- URL: http://arxiv.org/abs/2010.03538v1
- Date: Wed, 7 Oct 2020 17:34:50 GMT
- Title: Exploring the Role of Argument Structure in Online Debate Persuasion
- Authors: Jialu Li, Esin Durmus and Claire Cardie
- Abstract summary: We investigate the role of discourse structure of the arguments from online debates in their persuasiveness.
We find that argument structure features play an essential role in achieving the better predictive performance.
- Score: 39.74040217761505
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Online debate forums provide users a platform to express their opinions on
controversial topics while being exposed to opinions from diverse set of
viewpoints. Existing work in Natural Language Processing (NLP) has shown that
linguistic features extracted from the debate text and features encoding the
characteristics of the audience are both critical in persuasion studies. In
this paper, we aim to further investigate the role of discourse structure of
the arguments from online debates in their persuasiveness. In particular, we
use the factor graph model to obtain features for the argument structure of
debates from an online debating platform and incorporate these features to an
LSTM-based model to predict the debater that makes the most convincing
arguments. We find that incorporating argument structure features play an
essential role in achieving the better predictive performance in assessing the
persuasiveness of the arguments in online debates.
Related papers
- A Unifying Framework for Learning Argumentation Semantics [50.69905074548764]
We present a novel framework, which uses an Inductive Logic Programming approach to learn the acceptability semantics for several abstract and structured argumentation frameworks in an interpretable way.
Our framework outperforms existing argumentation solvers, thus opening up new future research directions in the area of formal argumentation and human-machine dialogues.
arXiv Detail & Related papers (2023-10-18T20:18:05Z) - Persua: A Visual Interactive System to Enhance the Persuasiveness of
Arguments in Online Discussion [52.49981085431061]
Enhancing people's ability to write persuasive arguments could contribute to the effectiveness and civility in online communication.
We derived four design goals for a tool that helps users improve the persuasiveness of arguments in online discussions.
Persua is an interactive visual system that provides example-based guidance on persuasive strategies to enhance the persuasiveness of arguments.
arXiv Detail & Related papers (2022-04-16T08:07:53Z) - Exploring Discourse Structures for Argument Impact Classification [48.909640432326654]
This paper empirically shows that the discourse relations between two arguments along the context path are essential factors for identifying the persuasive power of an argument.
We propose DisCOC to inject and fuse the sentence-level structural information with contextualized features derived from large-scale language models.
arXiv Detail & Related papers (2021-06-02T06:49:19Z) - The Unfolding Structure of Arguments in Online Debates: The case of a
No-Deal Brexit [0.0]
We propose a five-step methodology to extract, categorize and explore the latent argumentation structures of online debates.
Using Twitter data about a "no-deal" Brexit, we focus on the expected effects in case of materialisation of this event.
Results show that the proposed methodology can be employed to perform a statistical rhetorics analysis of debates.
arXiv Detail & Related papers (2021-03-09T12:29:43Z) - Strategic Argumentation Dialogues for Persuasion: Framework and
Experiments Based on Modelling the Beliefs and Concerns of the Persuadee [6.091096843566857]
Two key dimensions for determining whether an argument is good in a particular dialogue are the degree to which the intended audience believes the argument and counterarguments, and the impact that the argument has on the concerns of the intended audience.
We present a framework for modelling persuadees in terms of their beliefs and concerns, and for harnessing these models in optimizing the choice of move in persuasion dialogues.
arXiv Detail & Related papers (2021-01-28T08:49:24Z) - Aspect-Controlled Neural Argument Generation [65.91772010586605]
We train a language model for argument generation that can be controlled on a fine-grained level to generate sentence-level arguments for a given topic, stance, and aspect.
Our evaluation shows that our generation model is able to generate high-quality, aspect-specific arguments.
These arguments can be used to improve the performance of stance detection models via data augmentation and to generate counter-arguments.
arXiv Detail & Related papers (2020-04-30T20:17:22Z) - AMPERSAND: Argument Mining for PERSuAsive oNline Discussions [41.06165177604387]
We propose a computational model for argument mining in online persuasive discussion forums.
Our approach relies on identifying relations between components of arguments in a discussion thread.
Our models obtain significant improvements compared to recent state-of-the-art approaches.
arXiv Detail & Related papers (2020-04-30T10:33:40Z) - The Role of Pragmatic and Discourse Context in Determining Argument
Impact [39.70446357000737]
This paper presents a new dataset to initiate the study of this aspect of argumentation.
It consists of a diverse collection of arguments covering 741 controversial topics and comprising over 47,000 claims.
We propose predictive models that incorporate the pragmatic and discourse context of argumentative claims and show that they outperform models that rely on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument.
arXiv Detail & Related papers (2020-04-06T23:00:37Z) - What Changed Your Mind: The Roles of Dynamic Topics and Discourse in
Argumentation Process [78.4766663287415]
This paper presents a study that automatically analyzes the key factors in argument persuasiveness.
We propose a novel neural model that is able to track the changes of latent topics and discourse in argumentative conversations.
arXiv Detail & Related papers (2020-02-10T04:27:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.