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Sequence Graph Network for Online Debate Analysis

Authors:
Quan Mai
Susan Gauch
Douglas Adams
Miaoqing Huang

Keywords: Graph neural networks; dialog modeling; sequence graph network; online debates.

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 sequencegraph 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.

Pages: 15 to 22

Copyright: Copyright (c) IARIA, 2024

Publication date: May 26, 2024

Published in: conference

ISSN: 2308-4375

ISBN: 978-1-68558-165-7

Location: Barcelona, Spain

Dates: from May 26, 2024 to May 30, 2024