防止在线虚假信息的传播:成本效益高的动态预算分配反驳,媒体审查和社交机器人检测
Yi Wang1, Shicheng Zhong2, Guo Wang3
1School of Big Data and Information Industry, Chongqing City Management College, Chongqing 400000, China.
Mathematical biosciences and engineering : MBE
|July 28, 2023
概括
本研究介绍了一种动态预算分配 (DBA) 策略,以打击在线虚假信息. 拟议的DBA算法通过优化反驳,审查和机器人检测努力,有效地减少了支持虚假信息的帐户.
科学领域:
- 计算机科学 计算机科学
- 社交网络分析 社交网络分析
- 信息科学 信息科学 信息科学
背景情况:
- 在线虚假信息构成了重大挑战.
- 现有的对策包括反驳,媒体审查和社交机器人检测.
- 这些方法的协作有效性需要战略整合.
研究的目的:
- 制定一个具有成本效益的动态预算分配 (DBA) 战略,以打击在线虚假信息.
- 尽量减少在线社交网络中支持虚假信息的帐户的比例.
- 为了优化反驳,媒体审查和社交机器人检测的联合使用.
主要方法:
- 提出了一个虚假信息传播模型来分析DBA战略效应.
- 制定了一个权衡模型,并将问题缩小到一个最佳控制模型.
- 开发了一个启发式数值算法 (DBA算法) 来解决最佳性系统.
主要成果:
- DBA算法有效地识别出最佳的动态预算分配策略.
- 数字实验验证了DBA算法的有效性.
- 估计了模型的关键参数,并检查了DBA战略的表现.
结论:
- 拟议的DBA算法在寻找最佳策略来打击虚假信息方面是有效的.
- 通过战略预算制定,整合反驳,审查和机器人检测至关重要.
- 该研究为在线网络中具有成本效益的虚假信息防御提供了一个框架.
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