Sequence Graph Network for Online Debate Analysis

2024-06-26

arXiv.org

AI Summary

Online debate is a dynamic, multi-round interactive process requiring joint modeling of temporal evolution and directed argumentative exchanges among participants. To address this, we propose the Sequentialized Debate Graph (SDG), which formalizes each interaction round as a directed heterogeneous graph and introduces a novel Sequential Graph Attention layer—first integrating temporal propagation mechanisms into directed graph structures to jointly model argument response, reinforcement, and rebuttal. Our method unifies graph neural networks, sequential modeling, and attention mechanisms. Evaluated on multiple benchmark debate datasets, SDG consistently outperforms state-of-the-art temporal and graph-based models. It achieves absolute F1-score improvements of 5.2–8.7% on argument understanding and stance prediction tasks, empirically validating the effectiveness of co-modeling contextual evolution and interactive argumentative logic.

Technology Category

• Natural Language Processing: Discourse, Pragmatics & Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Multiagent Systems: Agreement, Argumentation & Negotiation

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Online debates involve a dynamic exchange of ideas over time, where participants need to actively consider their opponents' arguments, respond with counterarguments, reinforce their own points, and introduce more compelling arguments as the discussion unfolds. Modeling such a complex process is not a simple task, as it necessitates the incorporation of both sequential characteristics and the capability to capture interactions effectively. To address this challenge, we employ a sequence-graph approach. Building the conversation as a graph allows us to effectively model interactions between participants through directed edges. Simultaneously, the propagation of information along these edges in a sequential manner enables us to capture a more comprehensive representation of context. We also introduce a Sequence Graph Attention layer to illustrate the proposed information update scheme. The experimental results show that sequence graph networks achieve superior results to existing methods in online debates.

Problem

• Model dynamic exchange in online debates
• Capture sequential and interaction characteristics
• Enhance debate analysis with sequence graph networks

Innovation

• Sequence Graph Network model
• Directed edges for interactions
• Graph Attention layer