Abstract
Communication networks are known to exhibit asymmetric influence structures, constructed of a spectrum from highly influential individuals to highly influenced individuals. Information Processing Capacity (IPC) determines the level of responsiveness expressed by individuals when communicating with others in such networks. In this study, we explore the asymmetric influence structure of GitHub’s cryptocurrency developer community and show how it affects the IPC of the users in such networks. We use an agent-based model of information diffusion and conversation based on dynamic individual-level probabilities extracted from data on activity from cryptocurrency-related GitHub repositories. In this model, users that receive notifications from their neighbors at a rate above their IPC enter an overloaded state. We show that users who are influenced substantially more than they influence other users are typically expected to be overloaded and constantly experience lower IPC. In other words, these users are influenced more than they are able to express this magnitude of influence toward their neighbors. These results have potential implications in the design of viral marketing and reducing the harm of misinformation campaigns.
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Notes
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Fork and Watch actions.
- 2.
Issues, IssueComment, Push, PullRequest, PullRequestReviewComment, CommitComment,Gollum actions and comment.
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We thank Leidos for providing data and DARPA SocialSim grant (FA8650-18-C-7823) for funding us to perform this study.
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Baral, N., Gunaratne, C., Jayalath, C., Rand, W., Senevirathna, C., Garibay, I. (2021). Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks. In: Yang, Z., von Briesen, E. (eds) Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. CSSSA 2020. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-030-77517-9_16
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