Abstract
In order to solve the information transfer of social network media, a new information transfer prediction method is presented by a triangle ring attractor. At first, the description method of social media information is given in this model, and the evaluation indexes such as information recall rate, matching degree, and recall rate are emphatically expounded. At the same time, the characteristics of media information are analyzed with a triangular ring attractor, and an information transfer prediction model is established. Finally, through the simulation experiment, the key factors influencing the method are deeply analyzed. Experimental results show that compared with other algorithms, this method has good adaptability in the degree of information attenuation and information checking rate.
Highlights
With the rapid development of network information technology, Internet-oriented dissemination mode has become the mainstream media in the information era, and has begun to change people’s daily life and lifestyle from various aspects. e subjects of information dissemination in social network media are centralized, and the ability to recognize and discriminate the information is affected by the social environment [1,2,3,4,5,6]. e social network characteristics involved in social media are not analyzed and discussed. ese are only surface information, and there is no in-depth analysis of the characteristics of nodes in social networks
The research on information dissemination at the macrolevel mostly predicts the breadth of information dissemination, the depth of dissemination, and the speed of communication, but ignores the influence of social network topology on information dissemination. erefore, it is necessary to find indicators further for quantifying the structure of information dissemination structure, so as to accurately establish an information propagation structure morphology prediction model. e research of the microprediction method is mainly based on the details of information dissemination, considering the probability that information will be forwarded again after reaching a certain node
Erefore, on the basis of the above work, this paper proposes a new prediction model for information dissemination in social media based on a triangle ring attractor [23,24,25], which firstly presents the depiction method of social media information, analyzes the characteristic quantity of media information based on triangle ring attractor, and establishes the prediction model for information dissemination
Summary
With the rapid development of network information technology, Internet-oriented dissemination mode has become the mainstream media in the information era, and has begun to change people’s daily life and lifestyle from various aspects. e subjects of information dissemination in social network media are centralized, and the ability to recognize and discriminate the information is affected by the social environment [1,2,3,4,5,6]. e social network characteristics involved in social media are not analyzed and discussed. ese are only surface information, and there is no in-depth analysis of the characteristics of nodes in social networks. Erefore, it is necessary to consider the competition and cooperation between nodes to establish a Mathematical Problems in Engineering more accurate information propagation prediction model. Erefore, on the basis of the above work, this paper proposes a new prediction model for information dissemination in social media based on a triangle ring attractor [23,24,25], which firstly presents the depiction method of social media information, analyzes the characteristic quantity of media information based on triangle ring attractor, and establishes the prediction model for information dissemination. E structure of this paper is as follows: Section 1 presents the depiction method of social media information; the prediction model for information dissemination is established in Section 2; the simulation experiments are carried out in Section 3; Section 4 summarizes the full text The key factors affecting this method are analyzed deeply through simulation experiments. e structure of this paper is as follows: Section 1 presents the depiction method of social media information; the prediction model for information dissemination is established in Section 2; the simulation experiments are carried out in Section 3; Section 4 summarizes the full text
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