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Learning Network Dynamics from Tumblr®: A Search for Influential Users

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10354))

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Abstract

This work offers an original analysis of a unique data set gathered from the blogging website Tumblr by developing and applying a new data driven method for investigating network dynamics. To our knowledge, this is the first effort to analyze the spread of information on Tumblr on a such a large scale, and our method generally applies to networks where nodes have time-evolving states. We start by testing our method on simulated data, then we follow over 50,000 blogs on Tumblr over a year of activity to determine not only which blogs are influential, but more importantly, how these blogs spread their content.

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Author information

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of California Santa Barbara, Santa Barbara, CA, 92092, USA

    Steven Munn

  2. HRL Laboratories, Malibu, CA, 90265, USA

    Kang-Yu Ni & Jiejun Xu

Authors
  1. Steven Munn
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  2. Kang-Yu Ni
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  3. Jiejun Xu
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Corresponding author

Correspondence to Steven Munn .

Editor information

Editors and Affiliations

  1. Penn State University, State College, Pennsylvania, USA

    Dongwon Lee

  2. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Yu-Ru Lin

  3. University of Pittsburgh, Pittsburgh, Pennsylvania, USA

    Nathaniel Osgood

  4. United States Military Academy, West Point, New York, USA

    Robert Thomson

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© 2017 Springer International Publishing AG

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Munn, S., Ni, KY., Xu, J. (2017). Learning Network Dynamics from Tumblr®: A Search for Influential Users. In: Lee, D., Lin, YR., Osgood, N., Thomson, R. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2017. Lecture Notes in Computer Science(), vol 10354. Springer, Cham. https://doi.org/10.1007/978-3-319-60240-0_24

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  • DOI: https://doi.org/10.1007/978-3-319-60240-0_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60239-4

  • Online ISBN: 978-3-319-60240-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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