Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks

Nisha Baral; Chathika Gunaratne; Chathura Jayalath; William Rand; Chathurani Senevirathna; Ivan Garibay

doi:10.1007/978-3-030-77517-9_16

Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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.

Original language	English
Title of host publication	Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas
Editors	Zining Yang, Elizabeth von Briesen
Publisher	Springer Science and Business Media B.V.
Pages	265-275
Number of pages	11
ISBN (Print)	9783030775162
DOIs	https://doi.org/10.1007/978-3-030-77517-9_16
State	Published - 2021
Externally published	Yes
Event	International Conference of the Computational Social Science Society of the Americas, CSSSA 2019 - Santa Fe, United States Duration: Oct 24 2019 → Oct 27 2019

Publication series

Name	Springer Proceedings in Complexity
ISSN (Print)	2213-8684
ISSN (Electronic)	2213-8692

Conference

Conference	International Conference of the Computational Social Science Society of the Americas, CSSSA 2019
Country/Territory	United States
City	Santa Fe
Period	10/24/19 → 10/27/19

Funding

Acknowledgements We thank Leidos for providing data and DARPA SocialSim grant (FA8650-18-C-7823) for funding us to perform this study.

Keywords

Cryptocurrency
GitHub
Influence
Information diffusion
Information overload
Information processing capacity

Access to Document

10.1007/978-3-030-77517-9_16

Cite this

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 Z. Yang, & E. von Briesen (Eds.), Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas (pp. 265-275). (Springer Proceedings in Complexity). Springer Science and Business Media B.V.. https://doi.org/10.1007/978-3-030-77517-9_16

Baral, Nisha ; Gunaratne, Chathika ; Jayalath, Chathura et al. / Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks. Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. editor / Zining Yang ; Elizabeth von Briesen. Springer Science and Business Media B.V., 2021. pp. 265-275 (Springer Proceedings in Complexity).

@inproceedings{595099fe27894e5d9cace3190c9bbaa9,

title = "Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks",

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{\textquoteright}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.",

keywords = "Cryptocurrency, GitHub, Influence, Information diffusion, Information overload, Information processing capacity",

author = "Nisha Baral and Chathika Gunaratne and Chathura Jayalath and William Rand and Chathurani Senevirathna and Ivan Garibay",

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year = "2021",

doi = "10.1007/978-3-030-77517-9_16",

language = "English",

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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 Z Yang & E von Briesen (eds), Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. Springer Proceedings in Complexity, Springer Science and Business Media B.V., pp. 265-275, International Conference of the Computational Social Science Society of the Americas, CSSSA 2019, Santa Fe, United States, 10/24/19. https://doi.org/10.1007/978-3-030-77517-9_16

Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks. / Baral, Nisha; Gunaratne, Chathika; Jayalath, Chathura et al.
Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. ed. / Zining Yang; Elizabeth von Briesen. Springer Science and Business Media B.V., 2021. p. 265-275 (Springer Proceedings in Complexity).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Senevirathna, Chathurani

AU - Garibay, Ivan

PY - 2021

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N2 - 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.

AB - 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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Baral N, Gunaratne C, Jayalath C, Rand W, Senevirathna C, Garibay I. Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks. In Yang Z, von Briesen E, editors, Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas. Springer Science and Business Media B.V. 2021. p. 265-275. (Springer Proceedings in Complexity). doi: 10.1007/978-3-030-77517-9_16

Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks

Abstract

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Keywords

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