Abstract
A lot of information in the social network is accompanied by the continuous transmission of users, and there are many forms of propagation, fermentation, evolution, emergence and outbreaks, which make it difficult for analysts to predict the information dissemination situation at the next moment. However, if the information dissemination can be effectively predicted and perceived, it plays a very important role in hot event discovery, personalized information recommendation, bad information early warning and so on. Therefore, the study of this problem is of great practical value. This paper first study of situational awareness information transmission method, including the definition of information dissemination situational awareness problem and expounds the basic thought, and analyzes the information dissemination situation and level of the modularity, the relationship between the three exponential smoothing is used for information dissemination model for situational awareness, and to evaluate the application effect of the model has carried on the detailed; In addition, this chapter also studies the prediction method of information spread outburst, including the definition of information explosion, the analysis of related factors that affect the prediction of information dissemination, and the modeling and evaluation of the information outburst prediction model. In addition, some issues related to which features are more sensitive to information explosion prediction are also studied.
The authors have retracted this article [1] because of overlap with doctoral dissertation [2]. Figures 5, 6 and 7 were taken from the dissertation without permission or attribution. Part 2.3 of the article “Evaluation of information Outburst prediction model” quotes the data from this doctoral dissertation and makes some erroneous changes. All authors agree to this retraction.
[1] Jiang, W., Jiang, Y., Chen, J., Wang, Y., Xu, Y.: A new information exposure situation awareness model based on cubic exponential smoothing and its prediction method. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao L. (eds.) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol. 1042. Springer, Singapore (2019). https://doi.org/10.1007/978-981-15-1377-0_17
[2] Yi, C.: Research on Mechanisms of Information Propagation And Control Strategies in Social Networks. Dissertation at Harbin University of Science and Technology (2016)
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15 July 2020
The authors have retracted this article [1] because of overlap with doctoral dissertation [2]. Figures 5, 6 and 7 were taken from the dissertation without permission or attribution. Part 2.3 of the article “Evaluation of information Outburst prediction model” quotes the data from this doctoral dissertation and makes some erroneous changes. All authors agree to this retraction.
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Acknowledgement
This work was supported by the National Natural Science Foundation of China (61772196; 61472136), the Hunan Provincial Focus Social Science Fund (2016ZDB006), Hunan Provincial Social Science Achievement Review Committee results appraisal identification project (Xiang social assessment 2016JD05), Key Project of Hunan Provincial Social Science Achievement Review Committee (XSP 19ZD1005). The authors gratefully acknowledge the financial support provided by the Key Laboratory of Hunan Province for New Retail Virtual Reality Technology (2017TP1026).
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Jiang, W., Jiang, Y., Chen, J., Wang, Y., Xu, Y. (2019). RETRACTED CHAPTER: A New Information Exposure Situation Awareness Model Based on Cubic Exponential Smoothing and Its Prediction Method. In: Sun, Y., Lu, T., Yu, Z., Fan, H., Gao, L. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2019. Communications in Computer and Information Science, vol 1042. Springer, Singapore. https://doi.org/10.1007/978-981-15-1377-0_17
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DOI: https://doi.org/10.1007/978-981-15-1377-0_17
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