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Understanding Service Integration of Online Social Networks: A Data-Driven Study
Fudan University, China.
Fudan University, China.
Fudan University, China.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-7866-143x
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2018 (English)In: 2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018, 2018, p. 848-853Conference paper, Published paper (Refereed)
Abstract [en]

The cross-site linking function is widely adopted by online social networks (OSNs). This function allows a user to link her account on one OSN to her accounts on other OSNs. Thus, users are able to sign in with the linked accounts, share contents among these accounts and import friends from them. It leads to the service integration of different OSNs. This integration not only provides convenience for users to manage accounts of different OSNs, but also introduces usefulness to OSNs that adopt the cross-site linking function. In this paper, we investigate this usefulness based on users' data collected from a popular OSN called Medium. We conduct a thorough analysis on its social graph, and find that the service integration brought by the crosssite linking function is able to change Medium's social graph structure and attract a large number of new users. However, almost none of the new users would become high PageRank users (PageRank is used to measure a user's influence in an OSN). To solve this problem, we build a machine-learning-based model to predict high PageRank users in Medium based on their Twitter data only. This model achieves a high F1-score of 0.942 and a high area under the curve (AUC) of 0.986. Based on it, we design a system to assist new OSNs to identify and attract high PageRank users from other well-established OSNs through the cross-site linking function.

Place, publisher, year, edition, pages
2018. p. 848-853
Keywords [en]
Cross-site Linking, High PageRank Users, Medium, Online Social Networks, prediction, Service Integration, Forecasting, Integral equations, Learning systems, Ubiquitous computing, On-line social networks, PageRank, Social networking (online)
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:ri:diva-37284DOI: 10.1109/PERCOMW.2018.8480137Scopus ID: 2-s2.0-85056476540ISBN: 9781538632277 (print)OAI: oai:DiVA.org:ri-37284DiVA, id: diva2:1280359
Conference
2018 IEEE International Conference on Pervasive Computing and Communications Workshops, PerCom Workshops 2018, 19 March 2018 through 23 March 2018
Note

 Funding details: Natural Science Foundation of Shanghai, 16PJ1400700; Funding details: Natural Science Foundation of Shanghai, 16ZR1402200; Funding details: National Natural Science Foundation of China, NSFC, 71731004; Funding details: National Natural Science Foundation of China, NSFC, 61602122; Funding text 1: This work is sponsored by National Natural Science Foundation of China (No. 61602122, No. 71731004), Natural Science Foundation of Shanghai (No. 16ZR1402200), Shanghai Pujiang Program (No. 16PJ1400700).

Available from: 2019-01-18 Created: 2019-01-18 Last updated: 2019-01-21Bibliographically approved

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Ben Abdesslem, FehmiLindgren, Anders

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