Journal article
Modeling the co-diffusion of competing memes in online social networks
Decision Support Systems, Vol.187, 114324
12/2024
DOI: 10.1016/j.dss.2024.114324
Abstract
Online social networks have greatly facilitated the spread of information of all sorts. Meanwhile, the abundance of information in today's world also means different pieces of information will increasingly compete for people's finite attention. When different pieces of information spread together in an online social network, why would some become trendy while others fail to emerge? Existing research either models the diffusion of each piece of information independently, or fails to consider users' inactivity in online social networks. Modeling each piece of information as a meme, this paper addresses this gap by proposing a unified model for the co-diffusion of competing memes simultaneously spreading across an online social network. We are the first to identify a ubiquitous threshold for competing meme. The threshold also functions as an effective predictor that contributes to better performance in determining the outcome of meme competitions. Outcomes from this study have important implications for online campaigns and mobilizations as well as the fight against misinformation.
•Unified model for competing memes' spread in online networks.•First to identify a universal meme competition threshold.•Threshold predicts meme competition outcomes effectively.
Details
- Title: Subtitle
- Modeling the co-diffusion of competing memes in online social networks
- Creators
- Saike He - Chinese Academy of SciencesWeiguang Zhang - Chinese Academy of SciencesJun Luo - XiaomiPeijie Zhang - Chinese Academy of SciencesKang Zhao - Department of Business Analytics, Tippie College of Business, The University of Iowa, Iowa City, IA 52242, United States of AmericaDaniel Dajun Zeng - Institute of Automation
- Resource Type
- Journal article
- Publication Details
- Decision Support Systems, Vol.187, 114324
- Publisher
- Elsevier B.V
- DOI
- 10.1016/j.dss.2024.114324
- ISSN
- 0167-9236
- eISSN
- 1873-5797
- Grant note
- National Natural Science Foundation of China: 72293575, 71974187
This work was supported in part by the following grants: the National Natural Science Foundation of China under Grant Nos. 72293575 and 71974187.
- Language
- English
- Date published
- 12/2024
- Academic Unit
- Business Analytics
- Record Identifier
- 9984702829802771
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