2017/10/21 by Cheng-Jun Wang, Chengjun Wang, Zhi-Cong Chen +8
Computer Science · Physics and Astronomy · Psychology · Social Sciences · #Complex Network Analysis Techniques #Computer science #Computers and Society (cs.CY) #FOS: Computer and information sciences #Flow (mathematics) #Mechanics #Opinion Dynamics and Social Influence #Physics #Psychology #Social Media and Politics #Social and Information Networks (cs.SI) #cs.CY #cs.SI
paper · pdf · doi:10.48550/arxiv.1710.07761
16 pages, 5 figures
arxiv created 2017/10/21 · openalex publication_date 2017/10/21 · arxiv updated 2017/10/24 · openalex created_date 2017/11/10 · openalex updated_date 2026/07/28
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.