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Relational Patterns in Cross-Media Information Diffusion Networks

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Complex Networks & Their Applications VI (COMPLEX NETWORKS 2017)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 689))

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Abstract

This paper describes an approach for identifying patterns of information diffusion across different media types on the web. In this context, a novel sampling and crawling strategy for social media content has been applied to extract contributions relevant to certain news events. Contributions can be any kind of published content in different information channels, including tweets (Twitter), web pages, or revisions of Wikipedia articles. Contributions can be interlinked by hyperlinks, revision links, or retweet relationships, and thus constitute a diffusion network with unidirectional links indicating influence. Our approach reduces the original possibly very large and complex diffusion network to its basic underlying structure by applying non-negative matrix factorization to group the nodes with similar positions in the diffusion network. Beyond focusing only on the spread of a news item through different channels, also the temporal aspect, especially delay, is explicitly taken into account.

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Notes

  1. 1.

    https://dev.twitter.com/streaming/overview, as of 08/29/17.

  2. 2.

    https://www.mediawiki.org/wiki/API:Recent_changes_stream, as of 08/29/17.

  3. 3.

    http://discoursedb.github.io/, as of 08/29/17.

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Author information

Authors and Affiliations

  1. University of Duisburg-Essen, Lotharstr. 63, 47057, Duisburg, Germany

    Tobias Hecking, Laura Steinert, Victor H. Masias & H. Ulrich Hoppe

Authors
  1. Tobias Hecking
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  2. Laura Steinert
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  3. Victor H. Masias
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  4. H. Ulrich Hoppe
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Corresponding author

Correspondence to Tobias Hecking .

Editor information

Editors and Affiliations

  1. University of Lyon 2, Lyon, France

    Chantal Cherifi

  2. University of Burgundy, Dijon, France

    Hocine Cherifi

  3. École Normale Supérieure de Lyon, Lyon, France

    Márton Karsai

  4. University College London, London, United Kingdom

    Mirco Musolesi

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Hecking, T., Steinert, L., Masias, V.H., Ulrich Hoppe, H. (2018). Relational Patterns in Cross-Media Information Diffusion Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_81

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  • DOI: https://doi.org/10.1007/978-3-319-72150-7_81

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-72149-1

  • Online ISBN: 978-3-319-72150-7

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