在线信息交互的时间特性研究

北京邮电大学学报(社科版) ›› 2018, Vol. 20 ›› Issue (2): 76-82.

在线信息交互的时间特性研究

1. 北京邮电大学经济管理学院，北京100876；2.重庆邮电大学经济管理学院，重庆400065；
3太原工业学院经济与管理系,山西太原030008

收稿日期:2018-01-19 出版日期:2018-04-30

Time Features of Online Information Interaction

1 School of Economics and Managment, Beijing University of Posts and Telecommunications, Beijing
100876, China;2 School of Economics and Management, Chongqing University of Posts and
Telecommunications, Chongqing 400065, China;3 Department of Economics and Management, Taiyuan
Institute of Technology, Taiyuan 030008, China

Received:2018-01-19 Online:2018-04-30

摘要/Abstract

摘要： 在线信息交互行为的特征研究有助于完善对人类行为动力学规律的理解，为在线信息服务及管理提供理论依据。首先，利用重标极差法计算话题讨论中时间间隔序列的Hurst指数及V统计量，发现各种话题时间间隔序列Hurst指数均大于05，V统计量曲线呈上升趋势，表明序列具有自相似和长程相关的分形特征；然后，分析了序列V统计量趋势转折点与Hurst指数的关系，发现Hurst指数越大的序列，转折点越靠后，即序列的非循环周期更长。研究结果为分析在线信息交互行为及预测新的参与行为的到达，制定基于时间特性的舆论监管策略等提供了新的思路和方法。

关键词: 在线信息交互, Husrt指数, 非循环周期, 分形

Abstract: Researches on human online information interaction are helpful in systematically understanding human behavior dynamics, and also give theoretical foundation for effective regulation and service of human online information Firstly, through rescaled range analysis, Hurst exponent and V-statistic are calculated, and it is found that the values of the Hurst exponent are greater than 05, and V-statistic curves show an upward trend, which indicates that the time series of human online information interaction intervals show fractal with self-similarity and long-range dependence. Then, it is found that there is a positive relation between the turning point of V-statistic curve and Hurst exponent, which means greater Hurst exponent means longer noncyclic period of time series The results can provide new ideas and methods for analysis of human online information interaction, and predicting the arrival of new interaction and making effective supervision of public sentiment based on time feature

Key words: online information interaction, Husrt exponent, noncyclic period, fractal

中图分类号:

张洪1，2，吴洪1，余致远1，3. 在线信息交互的时间特性研究[J]. 北京邮电大学学报(社科版), 2018, 20(2): 76-82.

ZHANG Hong1,2, WU Hong1, YU Zhi-yuan1,3. Time Features of Online Information Interaction[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2018, 20(2): 76-82.

参考文献

［1］周涛, 韩筱璞, 闫小勇, 等. 人类行为时空特性的统计力学［J］. 电子科技大学学报, 2013, 42（4）: 481-540.
［2］方滨兴, 贾焰, 韩毅. 社交网络分析核心科学问题、研究现状及未来展望［J］. 中国科学院院刊, 2015, 30（2）: 187-199.
［3］杨善林, 王佳佳, 代宝, 等. 在线社交网络用户行为研究现状与展望［J］. 中国科学院院刊, 2015, 30（2）: 200-215.
［4］楼凤丹, 周银座, 庄晓丹, 等. 时效网络结构及动力学研究进展综述［J］. 电子科技大学学报, 2017, 46（1）: 109-125.
［5］ Barabási A L. The origin of bursts and heavy tails in human dynamics［J］. Nature, 2005, 435(7039): 207.
［6］ Vázquez A, Oliveira J G, Dezs Z, et al. Modeling bursts and heavy tails in human dynamics［J］. Physical Review E Statistical Nonlinear & Soft Matter Physics, 2005, 73(2): 036127.
［7］ Yan Q, Wu L, Zheng L. Social network based microblog user behavior analysis［J］. Physica a Statistical Mechanics & Its Applications, 2013, 392(7): 1712-1723.
［8］ Yan Q, Yi L, Wu L. Human dynamic model co-driven by interest and social identity in the MicroBlog community［J］. Physica a Statistical Mechanics & Its Applications, 2012, 391(4): 1540-1545.
［9］闫强, 吴联仁, 郑兰. 微博社区中用户行为特征及其机理研究［J］. 电子科技大学学报, 2013, 42（3）: 9-14.
［10］ Chen G, Han X, Wang B. Multi-level scaling properties of instant-message communications［J］. Physics Procedia, 2010, 3(5): 1897-1905.
［11］ Wang P, Zhou T, Han X P, et al. Modeling correlated human dynamics［J］. Physica a Statistical Mechanics & Its Applications, 2010, 398(4): 145-151.
［12］ Oliveira J G, Barabási A L. Human dynamics: the correspondence patterns of Darwin and Einstein［J］. Nature, 2005, 437(7063): 1251.
［13］ Li N N, Zhang N, Zhou T. Empirical analysis on temporal statistics of human correspondence patterns［J］. Complex Systems & Complexity Science, 2008, 387(25): 6391-6394.
［14］ Radicchi F. Human activity in the web［J］. Physical Review E Statistical Nonlinear & Soft MAT, 2009, 80(2 Pt 2): 026118.
［15］ Zhou T, Kiet H T, Kim B J, et al. Role of activity in human dynamics［J］. EPL, 2012, 82(2): 28002.
［16］罗伟平, 杨建梅. Android开源社区的人类行为动力学研究［J］. 华南理工大学学报(社会科学版), 2016, 18（6）: 47-51.
［17］李凯凯, 宋礼鹏. 基于社交网络的用户行为记忆性研究［J］. 微电子学与计算机, 2017, 34（3）: 133-135+14.
［18］罗泰晔. 基于泊松分布和伽马分布的网络舆情热点发现［J］. 现代情报, 2017, 37（1）: 77-80.
［19］刘丽娜, 齐佳音, 齐宏伟, 等. 在线评论中离散情感的分布研究［J］. 情报科学, 2017, 35（8）: 121-128.
［20］ Hurst H E. Long term storage capacities of reservoirs［J］. Transactions of the American Society of Civil ENGI, 1951, 116(12): 776-808.
［21］ Mandelbrot B B, Wallis J R. Robustness of the rescaled range R/S in the measurement of noncyclic long run statistical dependence［J］. Water Resources Research, 1969, 5(5): 967-988.
［22］ Peters E E. Fractal market analysis: applying chaos theory to investment and economics［J］. Chaos Theory, 1994, 34(2): 343-345.