Abstract
Online social networks (OSNs) have become a commodity in our daily-life. Besides the dominant platforms such as Facebook and Twitter, several emerging OSNs have been launched recently, where users may generate less activity data than on dominant ones. Identifying influential users is critical for the advertisement and the initial development of the emerging OSNs. In this work, we investigate the identification of potential influential users in these emerging OSNs. We build a supervised machine learning-based system by leveraging the widely adopted cross-site linking function, which could overcome the limitations of referring to the user data of a single OSN. Based on the collected real data from Twitter (a dominant OSN) and Medium (an emerging OSN), we show that our system is able to achieve an F1-score of 0.701 and an AUC of 0.755 in identifying influential users on Medium using the Twitter data only.
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Acknowledgement
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), EU FP7 IRSES MobileCloud project (No. 612212) and Lindemann Foundation (No. 12-2016), Projects 26211515 and 16214817 from the Research Grants Council of Hong Kong. Yang Chen is the corresponding author.
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Gong, Q. et al. (2019). Identification of Influential Users in Emerging Online Social Networks Using Cross-site Linking. In: Sun, Y., Lu, T., Xie, X., Gao, L., Fan, H. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2018. Communications in Computer and Information Science, vol 917. Springer, Singapore. https://doi.org/10.1007/978-981-13-3044-5_24
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DOI: https://doi.org/10.1007/978-981-13-3044-5_24
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