Understanding the structural and linguistic properties of conversational data in social media is crucial for extracting meaningful insights to understand opinion dynamics, (mis-)information spreading, and the evolution of harmful behavior. Current state-of-The-Art mathematical frameworks, such as hypergraphs and linguistic tools, such as large language models (LLMs), offer robust methodologies for modeling high-order group interactions and unprecedented capabilities for dealing with natural language-related tasks. In this study, we propose an innovative approach that blends these worlds by abstracting conversational networks via hypergraphs and analyzing their dynamics through LLMs. Our aim is to enhance the stance detection task by incorporating the high-order interactions naturally embedded within a conversation, thereby enriching the contextual understanding of LLMs regarding the intricate human dynamics underlying social media data.

Deciphering Conversational Networks: Stance Detection via Hypergraphs and LLMs

Antelmi A.;
2024-01-01

Abstract

Understanding the structural and linguistic properties of conversational data in social media is crucial for extracting meaningful insights to understand opinion dynamics, (mis-)information spreading, and the evolution of harmful behavior. Current state-of-The-Art mathematical frameworks, such as hypergraphs and linguistic tools, such as large language models (LLMs), offer robust methodologies for modeling high-order group interactions and unprecedented capabilities for dealing with natural language-related tasks. In this study, we propose an innovative approach that blends these worlds by abstracting conversational networks via hypergraphs and analyzing their dynamics through LLMs. Our aim is to enhance the stance detection task by incorporating the high-order interactions naturally embedded within a conversation, thereby enriching the contextual understanding of LLMs regarding the intricate human dynamics underlying social media data.
2024
ACM Web Science Conference
Stuttgart, Germany
21-24 May, 2024
Companion Proceedings of the 16th ACM Web Science Conference, Websci Companion 2024 - Reflecting on the Web, AI and Society
Association for Computing Machinery, Inc
3
4
9798400704536
https://dl.acm.org/doi/abs/10.1145/3630744.3658418
Conversational networks; hypergraphs; LLMs; stance detection
De Vinco D.; Antelmi A.; Spagnuolo C.; Aiello L.M.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/2318/1997450
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