Weibo event information propagation continuous dynamic prediction method

Abstract

The invention discloses a Weibo event information propagation continuous dynamic prediction method, and belongs to the data mining field. The method comprises the following steps: aiming at Sina Weibo, attempting to predict the weibo total quantity of the next phase on the basis of currently given propagation information; hourly dividing event propagation, using the propagation characteristics inthe time period from the event generation to the current moment to predict the event weibo propagation total quantity in the next hour according to a GBDT model, wherein the weibo propagation characteristics include weibo quantity, participation numbers and weibo emotions. The optimal time period length and weibo characteristics are combined in the prediction model, and selected through comprehensively considering each characteristic contribution degree and correlation, thus effectively improving the model prediction precision, enabling the average model precision to exceed 70%, reducing computing complexity, avoiding useless computing, and effectively supporting early warning and intervention measures aiming at the event.

Description

technical field [0001] The invention belongs to the field of data mining, and relates to a continuous dynamic prediction method for microblog event information dissemination. Background technique [0002] In recent years, with the extensive penetration and innovative development of Internet technology, social media represented by Weibo has been widely and deeply integrated into every aspect of people's lives. Social media has become an important platform for people to find information, express opinions and communicate. [0003] Research based on Twitter shows that the attributes of social media are closer to event networks than to social attributes. The diversity of information sources in social networks, the suddenness of events, and the extensiveness of dissemination make event analysis and dissemination prediction widely used, such as politically sensitive event monitoring, news hotspot discovery, business public opinion analysis, stock market public opinion fluctuations...

AI Technical Summary

Problems solved by technology

However, these predictive models are all evaluated for a single microblog, and the correlation and collinearity of features are not fully considered when the model considers the dependent variable.

[0006] Although there are also a lot of research work that transforms the problem of social network communication prediction into a binary or multi-classification problem, by extracting the context information, content features, communication network and other features in the process of microblog communication, and analyzing them according to the amount of microblog communication Popularity classification, such as 0 is a class, 1-100 is a class, 100-10000 is a class, and more than 10000 is a class, and a classifier based on a logistic regression model or a classification tree is constructed for training and learning; however, this classification or The regression method can only give a very rough prediction of the spread scale for a single microblog, and it cannot be applied in practice.

[0007] Studies have shown that it is still quite difficult to predict the final scale and development trend based on given certain dissemination information, or a large amount of background information and perfect dissemination information are required, which is difficult to achieve in practical applications

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