Leveraging the Flow of Collective Attention for Computational Communication Research

TL;DR

This study constructs an attention flow network from smartphone data to analyze collective attention patterns, revealing concentration, fragmentation, and centralization effects, with implications for understanding online behavior and communication.

Contribution

It introduces a novel flow network approach to quantify collective attention dynamics using large-scale smartphone data, highlighting structural properties and practical applications.

Findings

Strong concentration and fragmentation of attention among users

Large traffic websites can control collective attention flow

Flow network analysis explains page views and sales volume

Abstract

Human attention becomes an increasingly important resource for our understanding or collective human behaviors in the age of information explosion. To better understand the flow of collective attention, we construct the attention flow network using anonymous smartphone data of 100,000 users in a major city of China. In the constructed network, nodes are websites visited by users, and links denote the switch of users between two websites. We quantify the flow of collective attention by computing the flow network statistics, such as flow impact, flow dissipation, and flow distance. The findings reveal a strong concentration and fragmentation of collective attention for smartphone users, while the duplication of attention cross websites proves to be unfounded in mobile using. We further confirmed the law of dissipation and the allowmetric scaling of flow impact. Surprisingly, there is a centralized flow structure, suggesting that the website with large traffic can easily control the circulated collective attention. Additionally, we find that flow network analysis can effectively explain the page views and sale volume of products. Finally, we discuss the benefits and limitations of using the flow network analysis for computational communication research.