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ABSTRACT
This article reports the findings from simulating the spatial diffusion processes of memes over social media networks by using the approach of agent-based modeling. Different from other studies, this article examines how space and distance affect the diffusion of memes. Simulations were carried out to emulate and to allow assessment of the different levels of efficiency that memes spread spatially and temporally. Analyzed network structures include random networks and preferential attachment networks. Simulated spatial processes for meme diffusion include independent cascade models and linear threshold models. Both simulated and real-world social networks were used in the analysis. Findings indicate that the numbers of information sources and opinion leaders affect the processes of meme diffusion. In addition, geography is still important in the processes of spatial diffusion of memes over social media networks.
Acknowledgments
This research was supported partially by the National Science Foundation (NSF) through the award NSF #1416509 ‘IBSS: Spatiotemporal Modeling of Human Dynamics across Social Media and Social Networks’. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation.
Disclosure statement
No potential conflict of interest was reported by the authors.
Additional information
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Notes on contributors
Lanxue Dang
Dr. Lanxue Dang is an Associate Professor of Computer Science at Henan University. He received a M.S. in Computer Science and a Ph.D. in Geography from Henan University in 2006 and 2014, respectively. In the past years, he published more than 20 articles in the field of Computer Application or Geographic Information System. His research interests include spatio-temporal analysis, data mining technology and operational research.
Zhuo Chen
Zhuo Chen is a Ph.D. student of Geography at Kent State University. His research interest lies on social network analysis and geospatial analytics. More recently, he is particularly interested in GeoAI techniques and its integration with built environment and environmental health.
Jay Lee
Dr. Jay Lee is professor of Geography at Kent State University. His research works stem from a broad interest in bridging operations research and GIS. Over the last several years, he has focused on extending spatial analytics to spatiotemporal analytics.
Ming-Hsiang Tsou
Dr. Ming-Hsiang Tsou is a Professor in the Department of Geography, San Diego State University (SDSU) and the Founding Director of the Center for Human Dynamics in the Mobile Age (HDMA). He received a Ph.D. (2001) in Geography from the University of Colorado at Boulder. His research interests are in Human Dynamics, Social Media, Big Data, Visualization and Cartography, and Web GIS. He is co-author of Internet GIS, a scholarly book published in 2003 by Wiley and served on the editorial boards of the Annals of GIS, Cartography and GIScience and the Professional Geographers. Tsou was the Principal Investigator (PI) of, “Mapping ideas from Cyberspace to Realspace” research project (2010-2014, $1.3 millions) funded by National Science Foundation. This NSF-CDI project integrates GIS, computational linguistics, web search engines, and social media APIs to track and analyze public-accessible websites and social media (tweets) for visualizing and analyzing the diffusion of information and ideas in cyberspace. In Spring 2014, Tsou established a new research center, Human Dynamics in the Mobile Age (HDMA), a transdisciplinary research area of excellence at San Diego State University to integrate research works from GIScience, Public Health, Social Science, Sociology, and Communication.
Xinyue Ye
Dr. Xinyue Ye is Associate Professor of Spatial Data Science, College of Computing at New Jersey Institute of Technology, where Dr. Ye directs Urban Informatics and Spatial Computing Lab. He integrates social science and computational science towards information visualization, urban informatics and spatial social network analysis – the mapping of relationships among individuals in networks, integrated with spatial and environmental factors. His works has been funded by National Science Foundation, National Institute of Justice, Department of Commerce, and Department of Energy.