利用 LLM 解码分散自发抗议活动中的积极分子舆论

IF 4.5 2区计算机科学 Q1 COMPUTER SCIENCE, CYBERNETICS

IEEE Transactions on Computational Social Systems Pub Date : 2024-06-05 DOI:10.1109/TCSS.2024.3398815

Baoyu Zhang;Tao Chen;Xiao Wang;Qiang Li;Weishan Zhang;Fei-Yue Wang

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引用次数: 0

摘要

基于对法国 Nahel Merzouk 抗议活动的网络舆论调查，我们提出了一种基于大语言模型（LLM）分析和预测抗议活动舆论的方法，揭示了新兴社交媒体对抗议活动的影响。我们证明，抗议活动在社交媒体上引发的舆论具有一定的滞后性，但评论情绪和表达与抗议活动的趋势是一致的。随着抗议活动的展开，我们分析了公众情绪的演变。我们在历史数据的基础上构建了预测抗议活动的提示，并使用 p-tuning 和 Lora 方法对 LLM 进行了微调。此外，我们还讨论了如何利用区块链技术优化分布式自组织抗议活动，并降低虚假信息和暴力冲突的可能性。

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Decoding Activist Public Opinion in Decentralized Self-Organized Protests Using LLM

Based on an investigation of online public opinion on the Nahel Merzouk protests in France, an approach for analyzing and predicting public opinion on protests based on large language model (LLM) is proposed, revealing the impact of emerging social media on the protests. We demonstrate that protests generate public opinion on social media with some lag, but that comment sentiment and expression are consistent with protest trends. As the protests unfolded, we analyzed the evolution of public sentiment. We constructed the prompt based on historical data to predict the protests using the p-tuning and Lora approach to fine-tune LLM. In addition, we discuss how to use blockchain technology to optimize distributed, self-organizing protests and reduce the potential for disinformation and violent conflict.

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来源期刊

IEEE Transactions on Computational Social Systems Social Sciences-Social Sciences (miscellaneous)

CiteScore

10.00

自引率

20.00%

发文量

316

期刊介绍： IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.