Cumulative Effect in Information Diffusion: A Comprehensive Empirical Study on Microblogging Network

Abstract

Cumulative effect in social contagions underlies many studies on the spread of innovation, behaviors, and influence. However, few large-scale empirical studies are conducted to validate the existence of cumulative effect in the information diffusion on social networks. In this paper, using the population-scale dataset from the largest Chinese microblogging website, we conduct a comprehensive study on the cumulative effect in information diffusion. We base our study on the diffusion network of each message, where nodes are the involved users and links are the following relationships among them. We find that multiple exposures to the same message indeed increase the possibility of forwarding it. However, additional exposures cannot further improve the chance of forwarding when the number of exposures crosses its peak at two. This finding questions the cumulative effect hypothesis in information diffusion. Furthermore, to clarify the forwarding preference among users, we investigate both the structural motif of the diffusion network and the temporal pattern of information diffusion process among users. The patterns provide vital insight for understanding the variation of message popularity and explain the characteristics of diffusion networks.

References in corpus (10)

• Emergence of scaling in random networks
• Community structure in social and biological networks
• The origin of bursts and heavy tails in human dynamics
• The Dynamics of Viral Marketing
• Robust dynamic classes revealed by measuring the response function of a social system
• The energy landscape of social balance
• Potential Theory for Directed Networks
• Bridgeness: A Local Index on Edge Significance in Maintaining Global Connectivity
• Exploring the structural regularities in networks
• Emergent self-organized complex network topology out of stability constraints

Cited by in corpus (10)

• A Survey of Information Cascade Analysis: Models, Predictions, and Recent Advances
• Modeling and Predicting Popularity Dynamics via Reinforced Poisson Processes
• Modeling of Information Diffusion on Social Networks with Applications to WeChat
• A generalized voter model with time-decaying memory on a multilayer network
• Learning user-specific latent influence and susceptibility from information cascades
• InfoNCE is a Free Lunch for Semantically guided Graph Contrastive Learning
• Use of a controlled experiment and computational models to measure the impact of sequential peer exposures on decision making
• WeSeer: Visual Analysis for Better Information Cascade Prediction of WeChat Articles
• Competition Between Homophily and Information Entropy Maximization in Social Networks
• Can social influence be exploited to compromise security: An online experimental evaluation