The Spread, Rise, and Fall of University Students' Interconnected Internet Public Opinion in the Age of Big Data

Abstract

With the interconnected network's quick growth and widespread adoption, it has only made sense that it would serve as a hub for the dissemination of ideologies and cultural information as well as an amplifier for public opinion. The world is dualistic. The popularity of the connected network has both positive and negative effects on society. It makes people's lives more convenient, but it also has some drawbacks. Public opinion will quickly build up on the interconnected network as network communication becomes a significant method of disseminating social information, and the number of public opinion events on the interconnected network will also rise. Accurately understanding the law of higher education students' online public opinion to effectively direct and utilise online public opinion to carry out ideological education for students and to realise the establishment of students' good values, mental health, and behavioural norms, it is necessary to understand how to spread and rise and fall in the era of big data work. The parameter inversion model of online public opinion is established in this article based on the aforementioned issues. The parameter inversion algorithm is used to calculate the trend value of online public opinion, and the degree of fitting between the trend value of actual data and the trend value of parameter inversion is compared. The study discovered that the experiment's fitting value is as high as 90%. The model's prediction of the overall trend of the event development is correct, indicating that the model parameters are inverted, even though the actual public opinion data are affected by a variety of random factors, so some deviations may occur at local points. The internal law of the evolution of events that spread public opinion has been discovered, and it can be used to accurately describe the evolution and development of the public opinion dissemination process as it is driven by its internal mechanism. In the age of big data, this article analyses and summarises the rise, fall, and distribution of online public opinion among students at institutions of higher education. It also serves as a guide for monitoring and directing online public opinion in colleges and other institutions of higher education.