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Dynamic Directed Influence Networks: A Study of Campaigns on Twitter

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Social, Cultural, and Behavioral Modeling (SBP-BRiMS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9708))

Abstract

Studying the flow of influence in social media can allow insight into the nature of the agents involved and the corresponding actions that they take. In this paper, we study the influence of content among social media users with a concept called directed information (DI). Originally found in information theory, DI measures the amount of causal influence that an agent’s actions have on others. By estimating these quantities, influence networks are built that show the leaders and followers of a social circle. In order to demonstrate this technique, we extract tweets from the US presidential candidates and build an influence network. The time-varying influence is extracted using an extension of DI called adaptive directed influence (ADI), which is able to identify changes in influence over different timescales. Using the example of presidential candidates, we are able to show the power of building an influence network using DI and ADI and we compare and contrast with other relevant metrics.

This work was partially supported by ARO under grant #W911NF-12-1-0443.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Computer Science, University of Michigan, Ann Arbor, MI, USA

    Brandon Oselio & Alfred Hero

Authors
  1. Brandon Oselio
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  2. Alfred Hero
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Corresponding author

Correspondence to Brandon Oselio .

Editor information

Editors and Affiliations

  1. University of Toledo, Toledo, Ohio, USA

    Kevin S. Xu

  2. Penn State University, University Park, Pennsylvania, USA

    David Reitter

  3. Penn State University, University Park, Pennsylvania, USA

    Dongwon Lee

  4. University of Saskatchewan, Saskatoon, Saskatchewan, Canada

    Nathaniel Osgood

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© 2016 Springer International Publishing Switzerland

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Oselio, B., Hero, A. (2016). Dynamic Directed Influence Networks: A Study of Campaigns on Twitter. In: Xu, K., Reitter, D., Lee, D., Osgood, N. (eds) Social, Cultural, and Behavioral Modeling. SBP-BRiMS 2016. Lecture Notes in Computer Science(), vol 9708. Springer, Cham. https://doi.org/10.1007/978-3-319-39931-7_15

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  • DOI: https://doi.org/10.1007/978-3-319-39931-7_15

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-39930-0

  • Online ISBN: 978-3-319-39931-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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