TY - GEN
T1 - Negative Influence Gradients Lead to Lowered Information Processing Capacity on Social Networks
AU - Baral, Nisha
AU - Gunaratne, Chathika
AU - Jayalath, Chathura
AU - Rand, William
AU - Senevirathna, Chathurani
AU - Garibay, Ivan
N1 - Publisher Copyright:
© 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
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.
KW - Cryptocurrency
KW - GitHub
KW - Influence
KW - Information diffusion
KW - Information overload
KW - Information processing capacity
UR - http://www.scopus.com/inward/record.url?scp=85117085490&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-77517-9_16
DO - 10.1007/978-3-030-77517-9_16
M3 - Conference contribution
AN - SCOPUS:85117085490
SN - 9783030775162
T3 - Springer Proceedings in Complexity
SP - 265
EP - 275
BT - Proceedings of the 2019 International Conference of The Computational Social Science Society of the Americas
A2 - Yang, Zining
A2 - von Briesen, Elizabeth
PB - Springer Science and Business Media B.V.
T2 - International Conference of the Computational Social Science Society of the Americas, CSSSA 2019
Y2 - 24 October 2019 through 27 October 2019
ER -