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Web"],"published-print":{"date-parts":[[2021,5,31]]},"abstract":"<jats:p>Online social networks (OSNs) have become a commodity in our daily life. As an important concept in sociology and viral marketing, the study of social influence has received a lot of attentions in academia. Most of the existing proposals work well on dominant OSNs, such as Twitter, since these sites are mature and many users have generated a large amount of data for the calculation of social influence. Unfortunately, cold-start users on emerging OSNs generate much less activity data, which makes it challenging to identify potential influential users among them. In this work, we propose a practical solution to predict whether a cold-start user will become an influential user on an emerging OSN, by opportunistically leveraging the user\u2019s information on dominant OSNs. A supervised machine learning-based approach is adopted, transferring the knowledge of both the descriptive information and dynamic activities on dominant OSNs. Descriptive features are extracted from the public data on a user\u2019s homepage. In particular, to extract useful information from the fine-grained dynamic activities that cannot be represented by the statistical indices, we use deep learning technologies to deal with the sequential activity data. Using the real data of millions of users collected from Twitter (a dominant OSN) and Medium (an emerging OSN), we evaluate the performance of our proposed framework to predict prospective influential users. Our system achieves a high prediction performance based on different social influence definitions.<\/jats:p>","DOI":"10.1145\/3409108","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T10:18:37Z","timestamp":1620814717000},"page":"1-23","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":21,"title":["Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks"],"prefix":"10.1145","volume":"15","author":[{"given":"Qingyuan","family":"Gong","sequence":"first","affiliation":[{"name":"School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Fudan University, China and Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Fudan University, China and Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinlei","family":"He","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Fudan University, China and Peng Cheng Laboratory, Shenzhen, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Xiao","sequence":"additional","affiliation":[{"name":"Department of Communications and Networking, Aalto University, Espoo, Finland"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pan","family":"Hui","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Helsinki, Finland and CSE Department, Hong Kong University of Science and Technology, Kowloon, Hong Kong"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer Science, Fudan University, China and Shanghai Key Lab of Intelligent Information Processing, Fudan University, Shanghai, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaoming","family":"Fu","sequence":"additional","affiliation":[{"name":"Institute of Computer Science, University of G\u00f6ttingen, G\u00f6ttingen, Germany"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"e_1_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1935826.1935845"},{"key":"e_1_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/2187836.2187907"},{"key":"e_1_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1010933404324"},{"key":"e_1_2_1_4_1","volume-title":"Proceedings of the AAAI ICWSM.","author":"Cha Meeyoung","unstructured":"Meeyoung Cha , Hamed Haddadi , Fabr\u00edcio Benevenuto , and P. 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