Crawling Online Social Networks

被引:21
|
作者
Erlandsson, Fredrik [1 ]
Niat, Roozbeh [2 ]
Boldt, Martin [1 ]
Johnson, Henric [1 ]
Wu, S. Felix [2 ]
机构
[1] Blekinge Inst Technol, Karlskrona, Sweden
[2] Univ Calif Davis, Davis, CA 95616 USA
关键词
online social networks; online social media; crawling; mining;
D O I
10.1109/ENIC.2015.10
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Researchers put in tremendous amount of time and effort in order to crawl the information from online social networks. With the variety and the vast amount of information shared on online social networks today, different crawlers have been designed to capture several types of information. We have developed a novel crawler called SINCE. This crawler differs significantly from other existing crawlers in terms of efficiency and crawling depth. We are getting all interactions related to every single post. In addition, are we able to understand interaction dynamics, enabling support for making informed decisions on what content to re-crawl in order to get the most recent snapshot of interactions. Finally we evaluate our crawler against other existing crawlers in terms of completeness and efficiency. Over the last years we have crawled public communities on Facebook, resulting in over 500 million unique Facebook users, 50 million posts, 500 million comments and over 6 billion likes.
引用
收藏
页码:9 / 16
页数:8
相关论文
共 50 条
  • [1] Practical Recommendations on Crawling Online Social Networks
    Gjoka, Minas
    Kurant, Maciej
    Butts, Carter T.
    Markopoulou, Athina
    [J]. IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, 2011, 29 (09) : 1872 - 1892
  • [2] Multitenant approach to crawling of online social networks
    Butakov, Nikolay
    Petrov, Maxim
    Radice, Anton
    [J]. 5TH INTERNATIONAL YOUNG SCIENTIST CONFERENCE ON COMPUTATIONAL SCIENCE, YSC 2016, 2016, 101 : 115 - 124
  • [3] Crawling and Detecting Community Structure in Online Social Networks Using Local Information
    Blenn, Norbert
    Doerr, Christian
    Van Kester, Bas
    Van Mieghem, Piet
    [J]. NETWORKING 2012, PT I, 2012, 7289 : 56 - 67
  • [4] Crawling Credible Online Medical Sentiments for Social Intelligence
    Abbasi, Ahmed
    Fu, Tianjun
    Zeng, Daniel
    Adjeroh, Donald
    [J]. 2013 ASE/IEEE INTERNATIONAL CONFERENCE ON SOCIAL COMPUTING (SOCIALCOM), 2013, : 254 - 263
  • [5] An unbiased crawling strategy for directed social networks
    Yang, Xuehua
    Li, Hongbin
    [J]. Computer Modelling and New Technologies, 2014, 18 (12): : 585 - 589
  • [6] On Crawling Community-aware Online Social Network Data
    Hsu, Bay-Yuan
    Tu, Chia-Lin
    Chang, Ming-Yi
    Shen, Chih-Ya
    [J]. PROCEEDINGS OF THE 30TH ACM CONFERENCE ON HYPERTEXT AND SOCIAL MEDIA (HT '19), 2019, : 265 - 266
  • [7] Moving from social networks to social internetworking scenarios: The crawling perspective
    Buccafurri, Francesco
    Lax, Gianluca
    Nocera, Antonino
    Ursino, Domenico
    [J]. INFORMATION SCIENCES, 2014, 256 : 126 - 137
  • [8] ONLINE SOCIAL NETWORKS
    Cardon, Peter W.
    [J]. BUSINESS AND PROFESSIONAL COMMUNICATION QUARTERLY, 2009, 72 (01) : 96 - 97
  • [9] Online Social Networks
    Faloutsos, Michalis
    Karagiannis, Thomas
    Moon, Sue
    [J]. IEEE NETWORK, 2010, 24 (05): : 4 - 5
  • [10] Online Social Networks
    Fu, Xiaoming
    Passarella, Andrea
    Quercia, Daniele
    Sala, Alessandra
    Strufe, Thorsten
    [J]. COMPUTER COMMUNICATIONS, 2016, 73 : 163 - 166