|
|
|
| Predicting Model in Network Impact: a Monitoring and Warning System for Public Opinion in Universities under Big Data Framework |
| Liu Xiangdong1, Cao Yuting2, Li Limei3 |
1.Department of Statistics, School of Economics, Jinan University, Guangzhou, Guangdong, 510632;
2.School of Foreign Languages, Jinan University, Guangzhou, Guangdong, 510632;
3.Office of the President, Shenzhen University, Shenzhen, Guangdong, 518060 |
|
|
|
|
Abstract The Internet has great impact on the dissemination of ideas, and in particular public opinion, among college students. Under these new circumstances, it is of great significance to build up and gradually improve a monitoring and warning system for public opinion in universities, which will enable us to know how the students think, and address relevant issues in order to help them to establish the correct "three-values". This paper proposes a monitoring system for college student online public opinion, a predicting model of user influence based on the continuous time Markov process, through which we will find the most influential users (key figures) the social network of college students. With an automatic text classification method based on machine learning, the key figures are mainly classified into three categories: positive key figures, neutral key figures, and negative key figures. Finally, the paper proposes some measures in accordance with different types of key figures to promote the development of social networking service for college students.
|
|
Received: 23 March 2015
|
|
|
|
|
|
[1] K. Saito, M. Kimura, K. Ohara, and H. Motoda. Efficient estimation of cumulative influence for multiple activation information diffusion model with continuous time delay[J]. In PRACAI 2010: Trends in Artificial Intelligence, Springer, 2010, 6230：244-255.
[2] 肖宇,许炜,张晨,何丹丹. 社交网络中用户区域影响力评估算法研究[J]. 微电子学与计算机, 2012, 29(7):58-63.
[3] X.Song, Y.Chi, K.Hino, and B.L.Tseng. Information flow modeling based on diffusion rate for prediction and ranking[J]. In Procedings of the 16th international conference on World Wide Web, ACM, 2007, 25：191-200.
[4] 李涛等. 数据挖掘的应用与实践——大数据时代的案例分析[M]. 厦门：厦门大学出版社, 2013.216-229.
[5] W.J.Anderson. Continuous-time Markov chains: An applications-oriented approach[M]. Springer-Verlag New York, 1991, volume 7.
[6] Sebastiani F. Machine learning in automated text categorization[J]. ACM Computing Survey, 2002, 34(1):1-47. |
|
|
|
2015, Vol. 32 
