[Objective] This paper aims to explore the information behaviors of mobile social network (WeChat) users. [Methods] We crawled the WeChat users’ published posts in the past 5 years, and analyzed their information behaviors based on their characteristics, information contents, WeChat message posted time, WeChat Like and comment numbers. [Results] User-generated contents were affected by the user’s characteristics. There were significant differences among the numbers of Like and comments on different contents. WeChat users’ information posting intervals showed that most WeChat behaviors occurred within a short period of time. [Limitations] The sample size needs to be expanded to generalize our conclusions. [Conclusions] This study provides theoretical foundations for analyzing the behaviors of mobile social network users.
王飞飞, 张生太. 移动社交网络微信用户信息发布行为统计特征分析*[J]. 数据分析与知识发现, 2018, 2(4): 99-109.
Wang Feifei,Zhang Shengtai. Analyzing Information Behaviors of Mobile Social Network Users. Data Analysis and Knowledge Discovery, 2018, 2(4): 99-109.
(Wang Huan, Zhu Yang.WeChat Dissemination Analysis from Interpersonal Communication Perspective[J]. Journal of Modern Information, 2013, 33(7): 24-27.)
doi: 10.3969/j.issn.1008-0821.2013.07.005
(Fang Xingdong, Shi Xiansheng, Zhang Xiaorong, et al.Research on the Communication Mechanism and Governance of WeChat[J]. Modern Media, 2013, 35(6): 122-127.)
[4]
Gan C.Understanding WeChat Users’ Liking Behavior: An Empirical Study in China[J]. Computers in Human Behavior, 2017, 68:30-39.
doi: 10.1016/j.chb.2016.11.002
(Zhao Dali, Sun Daoyin, Zhang Tieshan.Social Capital Impact on the Willingness of Knowledge Sharing of Users in WeChat Moments[J]. Information Studies: Theory & Application , 2016, 39(3): 102-107.)
doi: 10.16353/j.cnki.1000-7490.2016.03.020
(Li Chen, Huang Can.Research on Motivation of WeChat Users’ Information Sharing Behaviors[J]. Journal of Modern Information, 2015, 35(5):57-62.)
doi: 10.3969/j.issn.1008-0821.2015.05.011
[7]
Weng L.Information Diffusion on Online Social Networks [D]. Indiana University, 2014.
(Tang Xiaobo, Luo Yingli.Integrating Emotional Divergence and User Interests into the Prediction of Microblog Retweeting[J]. Library and Information Service, 2017, 61(9):102-110.)
doi: 10.13266/j.issn.0252-3116.2017.09.013
(Huang Can, Gui Xuewen.Research on Motivation of WeChat Subscriptions Attract Users’ Paying Attention[J]. Journal of Modern Information, 2015, 35(8): 28-34.)
[10]
Fu F, Liu L, Wang L.Empirical Analysis of Online Social Networks in the Age of Web 2.0[J]. Physica A: Statistical Mechanics & Its Applications, 2008, 387(2-3): 675-684.
doi: 10.1016/j.physa.2007.10.006
Vazquez A, Rácz B, Lukács A, et al.Impact of non- Poissonian Activity Patterns on Spreading Processes[J]. Physical Review Letters, 2007, 98(15):158702.
doi: 10.1103/PhysRevLett.98.158702
pmid: 17501392
[13]
Zhao Z D, Xia H, Shang M S, et al.Empirical Analysis on the Human Dynamics of a Large-Scale Short Message Communication System[J]. Chinese Physics Letters, 2011, 28(6):068901.
doi: 10.1088/0256-307X/28/6/068901
[14]
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 and Its Applications, 2012, 391(4): 1540-1545.
doi: 10.1016/j.physa.2011.08.038
[15]
Wang P, Lei T, Chi H Y, et al.Heterogenous Human Dynamics in Intra and Inter-day Time Scale[J]. EPL (EuroPhysics Letters) , 2010, 94(94):18005.
doi: 10.1209/0295-5075/94/18005
[16]
Yan D C, Wei Z W, Han X P, et al.Empirical Analysis on the Human Dynamics of Blogging Behavior on GitHub[J]. Physica A: Statistical Mechanics & Its Applications, 2017, 465: 775-781.
doi: 10.1016/j.physa.2016.08.054
[17]
Barabási A L.The Origin of Bursts and Heavy Tails in Human Dynamics[J]. Nature, 2005, 435(7039): 207-211.
doi: 10.1038/nature03459
pmid: 15889093
[18]
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.
doi: 10.1016/j.physa.2012.12.008
[19]
Leskovec J, Horvitz E.Planetary-scale Views on a Large Instant-messaging Network[C]//Proceedings of the 17th International Conference on World Wide Web. ACM, 2008: 915-924.
(Peng Xixian, Zhu Qinghua, Liu Xuan.Research on Behavior Characteristics and Classification of Micro-blog Users—— Taking “Sina Micro-blog” as an Example[J]. Information Science, 2015, 33(1): 69-75.)
(Guo Aifang, Zhang Dan, Li Xiaofang, et al.An Empirical Study on Influencing Factors of Continuous Attention to WeChat Public Accounts: An Information Characteristics Perspective[J]. Journal of Intelligence, 2017, 36(1): 127-131.)
doi: 10.3969/j.issn.1002-1965.2017.01.022
(Guo Jinli.A Model of Human Behavior Dynamics and Exact Results[J]. Acta Physica Sinica, 2010, 59(6): 3851-3855.)
doi: 10.7498/aps.59.3851
[25]
White E P, Enquist B J, Green J L.On Estimating the Exponent of Power-Law Frequency Distributions[J]. Ecology, 2008, 89(4): 905-912.
doi: 10.1890/07-1288.1
pmid: 18481513
(Zhou Tao, Han Xiaopu, Yan Xiaoyong, et al.Statistical Mechanics on Temporal and Spatial Activities of Human[J]. Journal of University of Electronic Science and Technology of China, 2013, 42(4): 481-540.)
doi: 10.3969/j.issn.1001-0548.2013.04.001