@inproceedings{doerfel2014social,
  address = {New York, NY, USA},
  author = {Doerfel, Stephan and Zoller, Daniel and Singer, Philipp and Niebler, Thomas and Hotho, Andreas and Strohmaier, Markus},
  booktitle = {Proceedings of the 23rd International World Wide Web Conference},
  interhash = {9223d6d728612c8c05a80b5edceeb78b},
  intrahash = {11fab5468dd4b4e3db662ea5e68df8e0},
  publisher = {ACM},
  series = {WWW 2014},
  title = {How Social is Social Tagging?},
  year = 2014
}

@inproceedings{benevenuto2009characterizing,
  abstract = {Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.},
  acmid = {1644900},
  address = {New York, NY, USA},
  author = {Benevenuto, Fabr\'{\i}cio and Rodrigues, Tiago and Cha, Meeyoung and Almeida, Virg\'{\i}lio},
  booktitle = {Proceedings of the 9th ACM SIGCOMM Conference on Internet Measurement Conference},
  doi = {10.1145/1644893.1644900},
  interhash = {ed9b10d4f36f90ddde9b95ce45b0b0be},
  intrahash = {e5e25244e1ca2316a7871727e2df2bb9},
  isbn = {978-1-60558-771-4},
  location = {Chicago, Illinois, USA},
  numpages = {14},
  pages = {49--62},
  publisher = {ACM},
  series = {IMC '09},
  title = {Characterizing User Behavior in Online Social Networks},
  url = {http://doi.acm.org/10.1145/1644893.1644900},
  year = 2009
}

@article{jiang2013understanding,
  abstract = {Popular online social networks (OSNs) like Facebook and Twitter are changing the way users communicate and interact with the Internet. A deep understanding of user interactions in OSNs can provide important insights into questions of human social behavior and into the design of social platforms and applications. However, recent studies have shown that a majority of user interactions on OSNs are latent interactions, that is, passive actions, such as profile browsing, that cannot be observed by traditional measurement techniques. In this article, we seek a deeper understanding of both active and latent user interactions in OSNs. For quantifiable data on latent user interactions, we perform a detailed measurement study on Renren, the largest OSN in China with more than 220 million users to date. All friendship links in Renren are public, allowing us to exhaustively crawl a connected graph component of 42 million users and 1.66 billion social links in 2009. Renren also keeps detailed, publicly viewable visitor logs for each user profile. We capture detailed histories of profile visits over a period of 90 days for users in the Peking University Renren network and use statistics of profile visits to study issues of user profile popularity, reciprocity of profile visits, and the impact of content updates on user popularity. We find that latent interactions are much more prevalent and frequent than active events, are nonreciprocal in nature, and that profile popularity is correlated with page views of content rather than with quantity of content updates. Finally, we construct latent interaction graphs as models of user browsing behavior and compare their structural properties, evolution, community structure, and mixing times against those of both active interaction graphs and social graphs.},
  acmid = {2517040},
  address = {New York, NY, USA},
  articleno = {18},
  author = {Jiang, Jing and Wilson, Christo and Wang, Xiao and Sha, Wenpeng and Huang, Peng and Dai, Yafei and Zhao, Ben Y.},
  doi = {10.1145/2517040},
  interhash = {af18171c38a0b07fce62fb3fac5c6322},
  intrahash = {aa9695f56135fd58de32b5b4a4c73698},
  issn = {1559-1131},
  issue_date = {October 2013},
  journal = {ACM Trans. Web},
  month = nov,
  number = 4,
  numpages = {39},
  pages = {18:1--18:39},
  publisher = {ACM},
  title = {Understanding Latent Interactions in Online Social Networks},
  url = {http://doi.acm.org/10.1145/2517040},
  volume = 7,
  year = 2013
}