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 language | English |
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Article number | 160 |
Pages (from-to) | 1-26 |
Number of pages | 26 |
Journal | Entropy |
Volume | 23 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2021 |
Externally published | Yes |
Funding
Funding: This research was funded by the DARPA program grant number HR001117S0018.
Funders | Funder number |
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Defense Advanced Research Projects Agency | HR001117S0018 |
Keywords
- Cross platforms
- Cryptocur-rency
- Cyber-vulnerability
- Influence cascades
- Online social networks
- Transfer entropy