Abstract
Owing to rapid development of the Internet and the rise of the big data era, microblog has become the main means for people to spread and obtain information. If people can accurately predict the development trend of a microblog event, it will be of great significance for the government to carry out public relations activities on network event supervision and guide the development of microblog event reasonably for network crisis. This paper presents effective solutions to deal with trend prediction of microblog events’ popularity. Firstly, by selecting the influence factors and quantifying the weight of each factor with an information entropy algorithm, the microblog event popularity is modeled. Secondly, the singular spectrum analysis is carried out to decompose and reconstruct the time series of the popularity of microblog event. Then, the box chart method is used to divide the popularity of microblog event into various trend spaces. In addition, this paper exploits the Bi-LSTM model to deal with trend prediction with a sequence to label model. Finally, the comparative experimental analysis is carried out on two real data sets crawled from Sina Weibo platform. Compared to three comparative methods, the experimental results show that our proposal improves F1-score by up to 39%.
Highlights
With the rapid development of the Internet and the rise of the era of big data, microblog has become the main means for people to spread and obtain information
In order to solve the issue of trend prediction of event popularity from microblogs, this paper presents an effective predictive algorithm based on Bi-LSTM network model
This paper takes the popularity of microblog events as input and the changed state, Thisby paper takesalgorithm, the popularity of microblog events asnetwork input and the changed measured box plot as output to build Bi-LSTM
Summary
With the rapid development of the Internet and the rise of the era of big data, microblog has become the main means for people to spread and obtain information. And accurate prediction of the evolution trend of microblog events can help the government accurately evaluate the development trend of microblog events and provide effective decision support for the formulation of public event guidance strategies [1]. When an event is exposed in social network, the upsurge of the Internet media and netizens’. Discussion about the event on social media will affect the popularity of the events in real time. Social networks provide a large amount of real-time and continuous data for exploring the evolution of microblog events [3]. Due to the non-linear and multivariate characteristics of microblog data, this paper has to solve two challenging problems for microblog event popularity prediction
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
