基于改进的灰狼优化器与LSTM模型相结合,研究大学在线公众论风险的演变
Chao Cao1,2, Ziyu Li3, Lingzhi Li4
1School of Marxism, Central South University, Changsha, China.
PloS one
|January 24, 2025
概括
本研究引入了一种新的模型,用于使用灰狼优化器 (GWO) 和长短期记忆 (LSTM) 预测在线论危机. 该模型准确预测危机趋势,并有助于及时干预,这对社会稳定至关重要.
科学领域:
- 计算社会科学 计算社会科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 在线平台促进了信息的快速传播,增加了公众论的危机可见性和机构的复杂性.
- 准确预测在线论危机,特别是与校园有关的危机,对于社会稳定至关重要.
研究的目的:
- 开发和验证公众论危机预测模型.
- 为了提高预测在线论危机的准确性.
- 为评估和管理论风险提供一个工具.
主要方法:
- 一个混合模型,将灰狼优化器 (GWO) 算法与长期短期存储器 (LSTM) 网络相结合.
- 分析新浪微博上的热门话题,以验证预测准确度.
- 引入公众论风险指数以说明趋势.
主要成果:
- 拟议的GWO-LSTM模型证明了在线公众论危机趋势的高预测准确性.
- 该模型有效地说明了公众论风险的水平和变化.
- 模拟表明该模型在预测趋势和指导干预策略方面的实用性.
结论:
- 开发的模型有能力预测在线论危机趋势,并协助危机干预.
- 为了有效管理论危机,立即响应和干预至关重要.
- 这项研究强调了先进的分析框架对于论风险管理的重要性.
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