Li, Huacheng, Xia, Chunhe, Wang, Tianbo, Wen, Sheng, Chen, Chao, and Xiang, Yang (2021) Capturing dynamics of information diffusion in SNS: a survey of methodology and techniques. ACM Computing Surveys, 55 (1). 22.

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Abstract

Studying information diffusion in SNS (Social Networks Service) has remarkable significance in both academia and industry. Theoretically, it boosts the development of other subjects such as statistics, sociology, and data mining. Practically, diffusion modeling provides fundamental support for many downstream applications (e.g., public opinion monitoring, rumor source identification, and viral marketing). Tremendous efforts have been devoted to this area to understand and quantify information diffusion dynamics. This survey investigates and summarizes the emerging distinguished works in diffusion modeling. We first put forward a unified information diffusion concept in terms of three components: information, user decision, and social vectors, followed by a detailed introduction of the methodologies for diffusion modeling. And then, a new taxonomy adopting hybrid philosophy (i.e., granularity and techniques) is proposed, and we made a series of comparative studies on elementary diffusion models under our taxonomy from the aspects of assumptions, methods, and pros and cons. We further summarized representative diffusion modeling in special scenarios and significant downstream tasks based on these elementary models. Finally, open issues in this field following the methodology of diffusion modeling are discussed.

Item ID:	71008
Item Type:	Article (Research - C1)
ISSN:	1577-7341
Keywords:	Social network, diffusion models, propagation prediction, taxonomy
Copyright Information:	© 2021 Association for Computing Machinery.
Date Deposited:	30 Nov 2021 22:02
FoR Codes:	46 INFORMATION AND COMPUTING SCIENCES > 4602 Artificial intelligence > 460207 Modelling and simulation @ 100%
SEO Codes:	22 INFORMATION AND COMMUNICATION SERVICES > 2204 Information systems, technologies and services > 220499 Information systems, technologies and services not elsewhere classified @ 100%
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