Predicting the security threats of internet rumors and spread of false information based on sociological principle

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, dis-information, and offensive online debate. Based on the early diffusion status, the goal of this research is to forecast the popularly 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 popularly 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.

Automated summary

This research focuses on analyzing user engagement and content popularity in social online networks using the PSTIR and SFIBS models based on sociological concepts. The study successfully predicts online security threats and spread of false information, highlighting mainstream fatigue as an important prediction principle.