Influence cascades: Entropy-based characterization of behavioral influence patterns in social media

Chathurani Senevirathna, Chathika Gunaratne, William Rand, Chathura Jayalath, Ivan Garibay

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Influence cascades are typically analyzed using a single metric approach, i.e., all influence is measured using one number. However, social influence is not monolithic; different users exercise different influences in different ways, and influence is correlated with the user and content-specific attributes. One such attribute could be whether the action is an initiation of a new post, a contribution to a post, or a sharing of an existing post. In this paper, we present a novel method for tracking these influence relationships over time, which we call influence cascades, and present a visualization technique to better understand these cascades. We investigate these influence patterns within and across online social media platforms using empirical data and comparing to a scale-free network as a null model. Our results show that characteristics of influence cascades and patterns of influence are, in fact, affected by the platform and the community of the users.

Original languageEnglish
Article number160
Pages (from-to)1-26
Number of pages26
JournalEntropy
Volume23
Issue number2
DOIs
StatePublished - Feb 2021
Externally publishedYes

Funding

Funding: This research was funded by the DARPA program grant number HR001117S0018.

FundersFunder number
Defense Advanced Research Projects AgencyHR001117S0018

    Keywords

    • Cross platforms
    • Cryptocur-rency
    • Cyber-vulnerability
    • Influence cascades
    • Online social networks
    • Transfer entropy

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