Journal
IEEE ACCESS
Volume 6, Issue -, Pages 4547-4559Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/ACCESS.2018.2789915
Keywords
Social network analytics; Swarm app; social graph; user-generated contents
Categories
Funding
- National Natural Science Foundation of China [61602122, 71731004]
- Natural Science Foundation of Shanghai [16ZR1402200]
- Shanghai Pujiang Program [16PJ1400700]
- Academy of Finland [268096]
- General Research Fund from the Research Grants Council of Hong Kong [26211515, 16214817]
- Academy of Finland (AKA) [268096, 268096] Funding Source: Academy of Finland (AKA)
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Social graphs have been widely used for representing the relationship among users in online social networks (OSNs). As crawling an entire OSN is resource-and time-consuming, most of the existing works only pick a sampled subgraph for study. However, this may introduce serious inaccuracy into the analytic results, not to mention that some important metrics cannot even be calculated. In this paper, we crawl the entire social network of Swarm, a leading mobile social app with more than 60 million users, using a distributed approach. Based on the crawled massive user data, we conduct a data-driven study to get a comprehensive picture of the whole Swarm social network. This paper provides a deep analysis of social interactions between Swarm users, and reveals the relationship between social connectivity and check-in activities.
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