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

TL;DR

This comprehensive empirical study on a large Chinese microblogging platform investigates the cumulative effect in information diffusion, revealing that multiple exposures increase forwarding probability up to a point, but additional exposures beyond two do not further enhance it.

Contribution

The study provides large-scale empirical evidence challenging the traditional cumulative effect hypothesis in social media information diffusion.

Findings

Multiple exposures increase forwarding likelihood up to two exposures.

Additional exposures beyond two do not significantly increase forwarding probability.

Diffusion network structures and temporal patterns explain message popularity variations.

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.