Sequence Graph Network for Online Debate Analysis
This addresses the challenge of analyzing dynamic online debates for researchers or platforms, but it appears incremental as it builds on graph and sequence modeling techniques.
The paper tackled the problem of modeling complex online debates by developing a sequence-graph network that incorporates sequential characteristics and interactions, achieving superior results compared to existing methods.
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.