Abstract With the fast-growing IoT, regular connectivity through a range of heterogeneous intelligent devices across the Social Online Networks (SON) is feasible and effective to analyze sociological principles. Therefore, Increased user contributions, including web posts, videos and reviews slowly impact the lives of people in the recent past, which triggers volatile knowledge dissemination and undermine protection through gossip dissemination, disinformation, and offensive online debate. Based on the early diffusion status, the goal of this research is to forecast the popularity of online content reliably in the future. Though conventional prediction models are focused primarily on the discovery or integration of a network functionality into a changing time mechanism has been considered as unresolved issues and it has been resolved using Predicting The Security Threats of Internet Rumors (PSTIR) and Spread of False Information Based On Sociological (SFIBS) model with sociology concept. In this paper, the proportion of trustworthy Facebook fans who post regularly in early and future popularity has been analyzed linearly using PSTIR and SFIBS methods. Facebook statistics remind us that mainstream fatigue is an important prediction principle and The mainstream fatigue principle, Besides, it shows the effectiveness of the PSTIR and SFIBS based on experimental study.

中文翻译：

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基于社会学原理预测网络谣言和虚假信息传播的安全威胁

摘要 随着物联网的快速发展，通过跨社交在线网络（SON）的一系列异构智能设备进行定期连接对于分析社会学原理是可行和有效的。因此，最近增加的用户贡献，包括网络帖子、视频和评论，慢慢影响了人们的生活，这引发了不稳定的知识传播，并通过八卦传播、虚假信息和攻击性在线辩论破坏了保护。基于早期的传播状态，本研究的目标是可靠地预测未来在线内容的流行度。尽管传统的预测模型主要关注网络功能的发现或将其集成到不断变化的时间机制中，但一直被认为是未解决的问题，并且已使用预测互联网谣言的安全威胁 (PSTIR) 和基于具有社会学概念的社会学 (SFIBS) 模型。在本文中，使用 PSTIR 和 SFIBS 方法线性分析了在早期和未来流行中定期发帖的可信赖 Facebook 粉丝的比例。Facebook 的统计数据提醒我们，主流疲劳是一个重要的预测原理和主流疲劳原理，此外，它还展示了基于实验研究的 PSTIR 和 SFIBS 的有效性。