Work

Modeling the co-diffusion of competing memes in online social networks

Year: 2024

Type: article

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.

Source: Decision Support Systems

DOI: 10.1016/j.dss.2024.114324

Authors: Saike He, Weiguang Zhang, Jun Luo, Peijie Zhang, Kang Zhao, Daniel Dajun Zeng

Institutions: Chinese Academy of Sciences, Institute of Automation, University of Chinese Academy of Sciences, University of Iowa

Language: English

Topic: Complex Network Analysis Techniques

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Estimated APC paid: $3,410

Funder: National Natural Science Foundation of China