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Worship prediction: identify followers in celebrity-dived networks

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Abstract

We in this paper explore a new link prediction paradigm, called ‘worship’ prediction, to discover worship links between users and celebrities on social networks. The prediction of ‘worship’ links enables valuable social services, such as viral marketing, popularity estimation, and celebrity recommendation. However, as the concern of business security and personal privacy, only public-accessible statistical social properties, instead of the detailed information of users, can be utilized to predict the ‘worship’ labels. In addition, we observe that friendship properties are not effective to predict the desired links, meaning that most of previous work which rely on the friendship properties cannot be successfully applied in the prediction of worship link. To address these issues, a novel learning framework is devised, including a factor graph with new discovered statistical properties and a Gaussian estimation based learning algorithm with active learning. Our experimental studies on real data, including Instagram, Twitter and DBLP, show that the proposed learning framework can overcome the problem of missing labels and efficiently discover worship links.

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Notes

  1. We refer to https://www.instagram.com/developer/ for the Instagram API.

  2. Please refer to https://www.dropbox.com/s/tbgfthextty6m4c/Instagram.zip?dl=0 for our collected Instagram data.

  3. We refer to http://wiki.dbpedia.org/Applications for the DBpedia API

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Acknowledgements

This study was supported in part by the Ministry of Science and Technology (MOST) of Taiwan, R.O.C., under Contracts 104-2628-E-001-005-MY3, 105-2628-E-001-002-MY2, 106-3114-E-002-008, and 105-2221-E-006-140-MY2. All opinions, findings, conclusions, and recommendations in this paper are those of the authors and do not necessarily reflect the views of the funding agencies.

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Cheng Kung University, Tainan, Taiwan

    Shan-Yun Teng, Lo-Pang-Yun Ting & Kun-Ta Chuang

  2. Institute of Information Science, Academia Sinica, Taipei, Taiwan

    Mi-Yen Yeh

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  1. Shan-Yun Teng
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  2. Lo-Pang-Yun Ting
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Correspondence to Kun-Ta Chuang.

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Teng, SY., Ting, LPY., Yeh, MY. et al. Worship prediction: identify followers in celebrity-dived networks. World Wide Web 22, 347–373 (2019). https://doi.org/10.1007/s11280-018-0569-y

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