社交网络视频分享测量平台的设计与实现
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摘要
社交网络的出现,将网络上的人际关系聚集在一起,为认识好友的好友,以及好友之间的互动与信息分享提供了可能。目前,视频分享已经成为社交网站中最重要的应用之一,研究视频分享在社交网站中的传播规律,在信息传播领域和网络传输质量方面都有着非常重要的意义。
为了全面地考察社交网站中视频分享的传播规律,本文从视频分享信息的生长规律和传播规律两个方面进行了深入研究。针对研究内容所需的数据,本文设计与实现了一个视频分享测量平台,主要包括两个系统,即视频信息采集系统与视频信息解析系统。其中,视频信息采集系统主要以开心001网站上的热门视频转帖信息作为采集目标,爬取热门视频转帖的诞生时间、转帖次数、浏览次数、互动次数等信息。视频信息解析系统主要从某区域用户浏览人人网的历史数据中,获取用户分享视频的标题、链接、时间等信息。在此基础上,本文对获取到的数据进行了深入地研究和分析。一方面本文考察了开心001网站中热门视频转帖的一些特殊的生长规律。另一方面,以某区域作为研究范围,本文主要从区域外用户向区域内用户传播视频,以及区域内用户之间的视频传播两方面,对人人网中的视频分享传播进行了研究。
本文构建了一个稳定的测量平台,从两方面对社交网络视频分享规律进行了研究,总结出一些非常重要的结论,对社交网络领域的信息传播研究具有重要的指导意义。
With the appearance of SNS, interpersonal relationships congregate on the network, which provides the possibility of recognizing friends of friends, and interaction and information sharing between friends. Recently, video sharing has becoming one of the most important applications in social network sites. Therefore, it’s full of great significance for information dissemination and network transmission quality to study the propagation of video sharing in social network sites.
In order to fully examine propagation law of video sharing in social network sites, this paper conducted in-depth research from two aspects, growth and propagation. To obtain the video sharing information for research, this paper designed and implemented a measurement platform of video sharing, including two systems, namely, video information collection system and video information parsing system. Among them, the video information collection system took Kaxin001 as collection target, crawling information like the birth time, sharing times, view times, interaction times, etc. Video information parsing system parsed data, like video titles, URL links and time, from restore files of Renren viewed by users in a certain area. On that basis, this paper conducted in-depth research and analysis. On the one hand, this paper found some special growth laws of popular videos in Kaxin001. On the other hand, with the region of a university, this paper studied the video propagation laws in Renren through two aspects, video transmission from external users to internal users, and video communication between users inside the area.
This paper studied propagation laws of video sharing in social network sites, and summarized some very important conclusions. It was of important guiding significance for the research on the information dissemination of social network field.
为了全面地考察社交网站中视频分享的传播规律,本文从视频分享信息的生长规律和传播规律两个方面进行了深入研究。针对研究内容所需的数据,本文设计与实现了一个视频分享测量平台,主要包括两个系统,即视频信息采集系统与视频信息解析系统。其中,视频信息采集系统主要以开心001网站上的热门视频转帖信息作为采集目标,爬取热门视频转帖的诞生时间、转帖次数、浏览次数、互动次数等信息。视频信息解析系统主要从某区域用户浏览人人网的历史数据中,获取用户分享视频的标题、链接、时间等信息。在此基础上,本文对获取到的数据进行了深入地研究和分析。一方面本文考察了开心001网站中热门视频转帖的一些特殊的生长规律。另一方面,以某区域作为研究范围,本文主要从区域外用户向区域内用户传播视频,以及区域内用户之间的视频传播两方面,对人人网中的视频分享传播进行了研究。
本文构建了一个稳定的测量平台,从两方面对社交网络视频分享规律进行了研究,总结出一些非常重要的结论,对社交网络领域的信息传播研究具有重要的指导意义。
With the appearance of SNS, interpersonal relationships congregate on the network, which provides the possibility of recognizing friends of friends, and interaction and information sharing between friends. Recently, video sharing has becoming one of the most important applications in social network sites. Therefore, it’s full of great significance for information dissemination and network transmission quality to study the propagation of video sharing in social network sites.
In order to fully examine propagation law of video sharing in social network sites, this paper conducted in-depth research from two aspects, growth and propagation. To obtain the video sharing information for research, this paper designed and implemented a measurement platform of video sharing, including two systems, namely, video information collection system and video information parsing system. Among them, the video information collection system took Kaxin001 as collection target, crawling information like the birth time, sharing times, view times, interaction times, etc. Video information parsing system parsed data, like video titles, URL links and time, from restore files of Renren viewed by users in a certain area. On that basis, this paper conducted in-depth research and analysis. On the one hand, this paper found some special growth laws of popular videos in Kaxin001. On the other hand, with the region of a university, this paper studied the video propagation laws in Renren through two aspects, video transmission from external users to internal users, and video communication between users inside the area.
This paper studied propagation laws of video sharing in social network sites, and summarized some very important conclusions. It was of important guiding significance for the research on the information dissemination of social network field.
引文
[1]侯倩.关于我国SNS网站的研究: [硕士学位论文].陕西:西北大学图书馆, 2010
[2]王亮. SNS社交网络发展现状及趋势.现代电信科技, 2009(6): 9~13
[3]刘文娟,袁文芳.校内网的SNS人际传播特征分析.东南传播, 2009(5): 129~131
[4]艾瑞咨询. 2008-2009年中国网络视频行业发展报告简版. 2009
[5]黄玮.我国视频分享网站发展现状研究——以新浪、优酷、土豆网为对象: [硕士学位论文] .武汉:华中科技大学图书馆, 2008
[6]王晓光.基于社会网络的知识转移研究: [博士学位论文].武汉:武汉大学图书馆, 2007: 12
[7]何明升.叩开网络化生存之门.中国社会科学出版社, 2005, 120~127
[8]谢新洲,肖雯.我国网络信息传播的舆论化趋势及所带来的问题分析.情报理论与实践, 2006(6)
[9]陈远.网络社区信息传播的相关理论评述.图书情报知识, 2008(3)
[10] M. Cha, H. Kwak, P. Rodriguez, et al. I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System. in: Proc. of ACM IMC, 2007
[11] Xu Cheng, Cameron Dale, Jiangchuan Liu. Statistics and Social Network of YouTube Videos. School of Computing Science Simon Fraser University Burnaby, BC, Canada, 2008
[12]宫辉.高校BBS社群结构与信息传播的影响因素.西安交通大学学报, 2007, 21(1): 93~96
[13]李维杰. BBS中信息传播模式的特征分析.计算机工程与应用, 2010, 46(29): 18~22
[14]王方芳. SNS虚拟社区交往结构与信息传播研究: [硕士学位论文].大连:大连理工大学图书馆, 2010
[15]平亮.基于社会网络中心性分析的微博信息传播研究——以Sina微博为例.信息情报与共享, 2010(6): 92~97
[16]赵鹏.复杂网络与互联网个性化信息服务的研究: [博士学位论文].合肥:中国科学技术大学, 2006
[17] Leskovec J, McGlohon M. Patterns of cascading behavior in large bolg graphs. in: Proc.of th e7th SIAM International conference on Data Mining, Minneapolis, Minnesota, USA, 2007
[18] E. Adar, L. A. Adamic. Tracking information epidemics in blogspace. in: IEEE/WIC/ACM International Conference on Web Intelligence, 2005
[19]唐泳,马永开.小世界社会网络中的信息传播.系统仿真学报, 2008, 18(4): 1084~1087
[20]贺悠媛,胡晓峰.基于Agent的web信息传播仿真模型.系统仿真学报, 2010, 22(10): 2426~2431
[21]张嘉龄,李茂青.博客信息传播的网络模型构建.软件导刊, 2008, 7(5): 67~69
[22]陈奋.过滤性网络爬虫的研究与设计: [硕士学位论文].福建:厦门大学, 2007
[23]刘洁清.网站聚集爬虫研究: [硕士学位论文].江西:江西财经大学, 2006
[24]孙立伟,何国辉,吴礼发.网络爬虫技术的研究.电脑知识与技术, 2010, 6(15): 4112~4115
[25]谢国强,蓝立新.基于Web的网络爬虫技术研究.科教文汇, 2008, 4: 198~198
[26]李春艳,徐保民. Web数据抽取技术研究初探.电脑知识与技术, 2009, 5(35): 9920~9922
[27]陈雪杰. Web数据抽取技术研究: [硕士学位论文].哈尔滨:哈尔滨工程大学, 2009
[28]孟红,钟华.基于htmlparser的搜索引擎信息抽取系统设计与实现.第六届全国信息检索学术会议,2010
[29]陈晓锋,张凌,董守斌.基于XPath比较的Web数据抽取方法.郑州大学学报(理学版), 2007, 39(2): 162~166
[30]程冲,黄水清.利用正则表达式解析新闻网页的算法研究.农业图书情报学刊, 2005, 17(4): 5~18
[31]何腾蛟.分布式系统测试模型与框架的研究与应用: [硕士学位论文].成都:电子科技大学, 2009
[32]赵红毅,刘利坚.一种分布式系统进程调度方法研究.电子科技, 2010, 23(6): 55~58
[33]王家昉.多Agent应用系统开发框架的实现: [硕士学位论文].天津:天津大学, 2007
[34] A. Mislove, M. Marcon, K. P. Gummadi, et al. Measurement and Analysis of Online Social Networks. in: Proc. of ACM IMC, 2007
[35] Xu Cheng, Cameron Dale, Jiangchuan Liu. Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study. Computer Science, 2007
[36] S. Acharya, B. Smith, P. Parnes. Characterizing User Access To Videos On The World Wide Web. in: Proc. of ACM/SPIE Multimedia Computing and Networking, 2000
[37] M. Magnani, D. Montesi, L. Rossi. Information Propagation Analysis in a Social Network Site. in: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2010
[38] H. Yu, D. Zheng, B. Y. Zhao, et al. Understanding User Behavior in Large-Scale Video-on-Demand Systems. ACM SIGOPS Operating Systems Review, 2006, 40(4): 333~344
[39] Bai Yu, Hong Fei. Modeling Social Cascade in the Flickr Social Network. in: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, 7: 566~570
[40] Meeyoung Cha, Alan Mislove, Ben Adams, et al. Characterizing Social Cascades in Flickr. in: Proceedings of the 1st ACM SIGCOMM Workshop on Social Networks (WOSN'08), Seattle, WA, 2008
[41] Yong-Yeol Ahn, S.Han, H.Kwak, et al. Analysis of topological characteristics of huge online social networking services. in: Proc. of the International World Wide Web Conference, 2007
[42] Masoud Valafar, Reza Rejaie, Walter Willinger. Beyond Friendship Graphs: A Study of User Interactions in Flickr. in: ACM SIGCOMM Workshop on Online Social Networks (WOSN) , 2009
[43]李雪.我国视频分享网站中的意见领袖作用研究——以土豆网为例.中国商界, 2009(10): 374~375
[44] A. J. Ganesh, L. Massoulie, D. F. Towsley. The Effect of Network Topology on the Spread of Epidemics. in: Proc. of IEEE INFOCOM, 2005
[45] D. J. Watts, S. H. Strogatz. Collective Dynamics of Small-World Networks. Nature, 1998, 393(6684): 440~442
[46] Jure Leskovec, Mary McGlohon, Christos Faloutsos, et al. Cascading Behavior in Large Blog Graphs. in: SIAM International Conference on Data Mining (SDM), 2007
[2]王亮. SNS社交网络发展现状及趋势.现代电信科技, 2009(6): 9~13
[3]刘文娟,袁文芳.校内网的SNS人际传播特征分析.东南传播, 2009(5): 129~131
[4]艾瑞咨询. 2008-2009年中国网络视频行业发展报告简版. 2009
[5]黄玮.我国视频分享网站发展现状研究——以新浪、优酷、土豆网为对象: [硕士学位论文] .武汉:华中科技大学图书馆, 2008
[6]王晓光.基于社会网络的知识转移研究: [博士学位论文].武汉:武汉大学图书馆, 2007: 12
[7]何明升.叩开网络化生存之门.中国社会科学出版社, 2005, 120~127
[8]谢新洲,肖雯.我国网络信息传播的舆论化趋势及所带来的问题分析.情报理论与实践, 2006(6)
[9]陈远.网络社区信息传播的相关理论评述.图书情报知识, 2008(3)
[10] M. Cha, H. Kwak, P. Rodriguez, et al. I Tube, You Tube, Everybody Tubes: Analyzing the World’s Largest User Generated Content Video System. in: Proc. of ACM IMC, 2007
[11] Xu Cheng, Cameron Dale, Jiangchuan Liu. Statistics and Social Network of YouTube Videos. School of Computing Science Simon Fraser University Burnaby, BC, Canada, 2008
[12]宫辉.高校BBS社群结构与信息传播的影响因素.西安交通大学学报, 2007, 21(1): 93~96
[13]李维杰. BBS中信息传播模式的特征分析.计算机工程与应用, 2010, 46(29): 18~22
[14]王方芳. SNS虚拟社区交往结构与信息传播研究: [硕士学位论文].大连:大连理工大学图书馆, 2010
[15]平亮.基于社会网络中心性分析的微博信息传播研究——以Sina微博为例.信息情报与共享, 2010(6): 92~97
[16]赵鹏.复杂网络与互联网个性化信息服务的研究: [博士学位论文].合肥:中国科学技术大学, 2006
[17] Leskovec J, McGlohon M. Patterns of cascading behavior in large bolg graphs. in: Proc.of th e7th SIAM International conference on Data Mining, Minneapolis, Minnesota, USA, 2007
[18] E. Adar, L. A. Adamic. Tracking information epidemics in blogspace. in: IEEE/WIC/ACM International Conference on Web Intelligence, 2005
[19]唐泳,马永开.小世界社会网络中的信息传播.系统仿真学报, 2008, 18(4): 1084~1087
[20]贺悠媛,胡晓峰.基于Agent的web信息传播仿真模型.系统仿真学报, 2010, 22(10): 2426~2431
[21]张嘉龄,李茂青.博客信息传播的网络模型构建.软件导刊, 2008, 7(5): 67~69
[22]陈奋.过滤性网络爬虫的研究与设计: [硕士学位论文].福建:厦门大学, 2007
[23]刘洁清.网站聚集爬虫研究: [硕士学位论文].江西:江西财经大学, 2006
[24]孙立伟,何国辉,吴礼发.网络爬虫技术的研究.电脑知识与技术, 2010, 6(15): 4112~4115
[25]谢国强,蓝立新.基于Web的网络爬虫技术研究.科教文汇, 2008, 4: 198~198
[26]李春艳,徐保民. Web数据抽取技术研究初探.电脑知识与技术, 2009, 5(35): 9920~9922
[27]陈雪杰. Web数据抽取技术研究: [硕士学位论文].哈尔滨:哈尔滨工程大学, 2009
[28]孟红,钟华.基于htmlparser的搜索引擎信息抽取系统设计与实现.第六届全国信息检索学术会议,2010
[29]陈晓锋,张凌,董守斌.基于XPath比较的Web数据抽取方法.郑州大学学报(理学版), 2007, 39(2): 162~166
[30]程冲,黄水清.利用正则表达式解析新闻网页的算法研究.农业图书情报学刊, 2005, 17(4): 5~18
[31]何腾蛟.分布式系统测试模型与框架的研究与应用: [硕士学位论文].成都:电子科技大学, 2009
[32]赵红毅,刘利坚.一种分布式系统进程调度方法研究.电子科技, 2010, 23(6): 55~58
[33]王家昉.多Agent应用系统开发框架的实现: [硕士学位论文].天津:天津大学, 2007
[34] A. Mislove, M. Marcon, K. P. Gummadi, et al. Measurement and Analysis of Online Social Networks. in: Proc. of ACM IMC, 2007
[35] Xu Cheng, Cameron Dale, Jiangchuan Liu. Understanding the Characteristics of Internet Short Video Sharing: YouTube as a Case Study. Computer Science, 2007
[36] S. Acharya, B. Smith, P. Parnes. Characterizing User Access To Videos On The World Wide Web. in: Proc. of ACM/SPIE Multimedia Computing and Networking, 2000
[37] M. Magnani, D. Montesi, L. Rossi. Information Propagation Analysis in a Social Network Site. in: International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 2010
[38] H. Yu, D. Zheng, B. Y. Zhao, et al. Understanding User Behavior in Large-Scale Video-on-Demand Systems. ACM SIGOPS Operating Systems Review, 2006, 40(4): 333~344
[39] Bai Yu, Hong Fei. Modeling Social Cascade in the Flickr Social Network. in: Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009, 7: 566~570
[40] Meeyoung Cha, Alan Mislove, Ben Adams, et al. Characterizing Social Cascades in Flickr. in: Proceedings of the 1st ACM SIGCOMM Workshop on Social Networks (WOSN'08), Seattle, WA, 2008
[41] Yong-Yeol Ahn, S.Han, H.Kwak, et al. Analysis of topological characteristics of huge online social networking services. in: Proc. of the International World Wide Web Conference, 2007
[42] Masoud Valafar, Reza Rejaie, Walter Willinger. Beyond Friendship Graphs: A Study of User Interactions in Flickr. in: ACM SIGCOMM Workshop on Online Social Networks (WOSN) , 2009
[43]李雪.我国视频分享网站中的意见领袖作用研究——以土豆网为例.中国商界, 2009(10): 374~375
[44] A. J. Ganesh, L. Massoulie, D. F. Towsley. The Effect of Network Topology on the Spread of Epidemics. in: Proc. of IEEE INFOCOM, 2005
[45] D. J. Watts, S. H. Strogatz. Collective Dynamics of Small-World Networks. Nature, 1998, 393(6684): 440~442
[46] Jure Leskovec, Mary McGlohon, Christos Faloutsos, et al. Cascading Behavior in Large Blog Graphs. in: SIAM International Conference on Data Mining (SDM), 2007