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Detection of Lurkers in Online Social Networks

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Book cover Cyberspace Safety and Security (CSS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 10581))

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Abstract

In this work, we propose a novel data model that integrates and combines information on users belonging to one or more heterogeneous Online Social Networks (OSNs), together with the content that is generated, shared and used within the related environments, using an hypergraph-based approach. Then, we discuss how the most diffused centrality measures – that have been defined over the introduced model – can be efficiently applied for a number of data privacy issues, such as lurkers detection, especially in “interest-based” social networks. Some preliminary experiments using the Yelp dataset are finally presented.

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Notes

  1. 1.

    Such strategy is necessary to penalize lurkers, i.e., users of an HN that do not directly interact with content but through user to user relationships.

  2. 2.

    We have also considered the most important topics within the reviews.

  3. 3.

    https://www.yelp.com/dataset_challenge.

  4. 4.

    https://databricks.com/.

  5. 5.

    For the Neighborhood centrality, we set \(\lambda =6\) and \(\alpha =1.5\).

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Authors and Affiliations

  1. Dipartimento di Ingegneria Elettrica e Tecnologie dell’Informazione, University of Naples “Federico II”, Via Claudio 21, 80125, Naples, Italy

    Flora Amato, Aniello Castiglione, Vincenzo Moscato, Antonio Picariello & Giancarlo Sperlì

  2. CINI - ITEM National Lab, Complesso Universitario Monte Santangelo, 80125, Naples, Italy

    Vincenzo Moscato & Antonio Picariello

  3. University of Salerno, Via Giovanni Paolo II, 132, 84084, Fisciano, SA, Italy

    Aniello Castiglione

Authors
  1. Flora Amato
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  2. Aniello Castiglione
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  3. Vincenzo Moscato
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  4. Antonio Picariello
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  5. Giancarlo Sperlì
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Corresponding author

Correspondence to Giancarlo Sperlì .

Editor information

Editors and Affiliations

  1. Deakin University , Geelong, China

    Sheng Wen

  2. Fujian Normal University, Fuzhou Shi, China

    Wei Wu

  3. University of Salerno, Fisciano, Italy

    Aniello Castiglione

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Amato, F., Castiglione, A., Moscato, V., Picariello, A., Sperlì, G. (2017). Detection of Lurkers in Online Social Networks. In: Wen, S., Wu, W., Castiglione, A. (eds) Cyberspace Safety and Security. CSS 2017. Lecture Notes in Computer Science(), vol 10581. Springer, Cham. https://doi.org/10.1007/978-3-319-69471-9_1

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  • DOI: https://doi.org/10.1007/978-3-319-69471-9_1

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