skip to main content
research-article

Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks

Authors Info & Claims
Published:12 May 2021Publication History
Skip Abstract Section

Abstract

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’s 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’s 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.

References

  1. Eytan Bakshy, Winter A. Mason, Jake M. Hofman, and Duncan J. Watts. 2011. Everyone is an influencer: Quantifying influence on Twitter. In Proceedings of the ACM WSDM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Eytan Bakshy, Itamar Rosenn, Cameron Marlow, and Lada Adamic. 2012. The role of social networks in information diffusion. In Proceedings of the WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Leo Breiman. 2001. Random forests. Mach. Learn. 45, 1 (2001), 5--32. Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Meeyoung Cha, Hamed Haddadi, Fabrício Benevenuto, and P. Krishna Gummadi. 2010. Measuring user influence in Twitter: The million follower fallacy. In Proceedings of the AAAI ICWSM.Google ScholarGoogle Scholar
  5. Shiyu Chang, Yang Zhang, Wei Han, Mo Yu, Xiaoxiao Guo, Wei Tan, Xiaodong Cui, Michael J. Witbrock, Mark A. Hasegawa-Johnson, and Thomas S. Huang. 2017. Dilated recurrent neural networks. In Proceedings of the NIPS. 76--86. Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. Tianqi Chen and Carlos Guestrin. 2016. XGBoost: A scalable tree boosting system. In Proceedings of the ACM KDD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. Yang Chen, Jiyao Hu, Yu Xiao, Xiang Li, and Pan Hui. 2020. Understanding the user behavior of Foursquare: A data-driven study on a global scale. IEEE Trans. Comput. Soc. Syst. 7, 4 (2020), 1019--1032.Google ScholarGoogle ScholarCross RefCross Ref
  8. Y. Chen, J. Hu, H. Zhao, Y. Xiao, and P. Hui. 2018. Measurement and analysis of the swarm social network with tens of millions of nodes. IEEE Access 6 (2018), 4547--4559.Google ScholarGoogle ScholarCross RefCross Ref
  9. Kyunghyun Cho, Bart van Merrienboer, Çaglar Gülçehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proceedings of the EMNLP.Google ScholarGoogle ScholarCross RefCross Ref
  10. Robert B. Cialdini. 2008. Influence: Science and Practice (5th ed.). Prentice Hall.Google ScholarGoogle Scholar
  11. Marijke De Veirman, Veroline Cauberghe, and Liselot Hudders. 2017. Marketing through Instagram influencers: the impact of number of followers and product divergence on brand attitude. Int. J. Advert. 36, 5 (2017), 798--828.Google ScholarGoogle ScholarCross RefCross Ref
  12. A. Farseev, L. Nie, M. Akbari, and T. Chua. 2015. Harvesting multiple sources for user profile learning: A big data study. In Proceedings of the ACM ICMR. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. Tom Fawcett. 2006. An introduction to ROC analysis. Pattern Recog. Lett. 27, 8 (2006), 861--874. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. Maksym Gabielkov, Ashwin Rao, and Arnaud Legout. 2014. Studying social networks at scale: Macroscopic anatomy of the Twitter social graph. In Proceedings of the ACM SIGMETRICS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Oana Goga, Patrick Loiseau, Robin Sommer, Renata Teixeira, and Krishna P. Gummadi. 2015. On the reliability of profile matching across large online social networks. In Proceedings of the ACM KDD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. Neil Zhenqiang Gong, Wenchang Xu, Ling Huang, et al. 2012. Evolution of social-attribute networks: Measurements, modeling, and implications using Google+. In Proceedings of the ACM IMC. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Qingyuan Gong, Yang Chen, Xinlei He, Zhou Zhuang, Tianyi Wang, Hong Huang, Xin Wang, and Xiaoming Fu. 2018. DeepScan: Exploiting deep learning for malicious account detection in location-based social networks. IEEE Commun. Mag. 56, 11 (2018), 21--27.Google ScholarGoogle ScholarCross RefCross Ref
  18. Qingyuan Gong, Yang Chen, Jiyao Hu, Qiang Cao, Pan Hui, and Xin Wang. 2018. Understanding cross-site linking in online social networks. ACM Trans. Web 12, 4 (2018), 25:1--25:29. Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Qingyuan Gong, Yang Chen, Xiaolong Yu, Chao Xu, Zhichun Guo, Yu Xiao, Fehmi Ben Abdesslem, Xin Wang, and Pan Hui. 2019. Exploring the power of social hub services. World Wide Web: Internet Web Inf. Syst. 22, 6 (2019), 2825--2852.Google ScholarGoogle ScholarCross RefCross Ref
  20. Qingyuan Gong, Jiayun Zhang, Yang Chen, Qi Li, Yu Xiao, Xin Wang, and Pan Hui. 2019. Detecting malicious accounts in online developer communities using deep learning. In Proceedings of the ACM CIKM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Roberto Gonzalez, Ruben Cuevas, Reza Motamedi, Reza Rejaie, and Angel Cuevas. 2013. Google+ or Google-?: Dissecting the evolution of the new OSN in its first year. In Proceedings of the WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. A. Graves and J. Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM networks. In Proceedings of the IEEE International Joint Conference on Neural Networks.Google ScholarGoogle Scholar
  23. Jinyoung Han, Daejin Choi, Byung-Gon Chun, et al. 2014. Collecting, organizing, and sharing pins in Pinterest: Interest-driven or social-driven? In Proceedings of the ACM SIGMETRICS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. Jorge E. Hirsch. 2005. An index to quantify an individual’s scientific research output. Proc. Nat. Acad. Sci. United States Amer. 102, 46 (2005), 16569--16572.Google ScholarGoogle ScholarCross RefCross Ref
  25. Sepp Hochreiter and Jürgen Schmidhuber. 1997. Long short-term memory. Neural Comput. 9, 8 (Nov. 1997), 1735--1780. Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Paridhi Jain, Ponnurangam Kumaraguru, and Anupam Joshi. 2016. Other times, other values: Leveraging attribute history to link user profiles across online social networks. Soc. Netw. Anal. Mining 6, 1 (2016), 85.Google ScholarGoogle ScholarCross RefCross Ref
  27. Yongpo Jia, Xuemeng Song, Jingbo Zhou, Li Liu, Liqiang Nie, and David S. Rosenblum. 2016. Fusing social networks with deep learning for volunteerism tendency prediction. In Proceedings of the AAAI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. Long Jin, Yang Chen, Tianyi Wang, Pan Hui, and Athanasios V. Vasilakos. 2013. Understanding user behavior in online social networks: A survey. IEEE Commun. Mag. 51, 9 (2013), 144--150.Google ScholarGoogle ScholarCross RefCross Ref
  29. Rafal Jozefowicz, Wojciech Zaremba, and Ilya Sutskever. 2015. An empirical exploration of recurrent network architectures. In Proceedings of the ICML. Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. X. Kong, J. Zhang, and P. Yu. 2013. Inferring anchor links across multiple heterogeneous social networks. In Proceedings of the ACM CIKM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Haewoon Kwak, Changhyun Lee, Hosung Park, and Sue Moon. 2010. What is Twitter, a social network or a news media? In Proceedings of the WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Jianxin Li, Xinjue Wang, Ke Deng, Xiaochun Yang, Timos Sellis, and Jeffrey Xu Yu. 2017. Most influential community search over large social networks. In Proceedings of the IEEE ICDE.Google ScholarGoogle ScholarCross RefCross Ref
  33. Qi Liu, Biao Xiang, Nicholas Jing Yuan, Enhong Chen, Hui Xiong, Yi Zheng, and Yu Yang. 2017. An influence propagation view of pagerank. ACM Trans. Knowl. Discov. Data 11, 3 (Aug. 2017), 30:1--30:30. Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Siyuan Liu, Shuhui Wang, Feida Zhu, Jinbo Zhang, and Ramayya Krishnan. 2014. HYDRA: Large-scale social identity linkage via heterogeneous behavior modeling. In Proceedings of the ACM SIGMOD. Google ScholarGoogle ScholarDigital LibraryDigital Library
  35. Jing Ma, Wei Gao, Prasenjit Mitra, Sejeong Kwon, Bernard J. Jansen, Kam-Fai Wong, and Meeyoung Cha. 2016. Detecting rumors from microblogs with recurrent neural networks. In Proceedings of the IJCAI. Google ScholarGoogle ScholarDigital LibraryDigital Library
  36. Larry Medsker and Lakhmi C. Jain. 1999. Recurrent Neural Networks: Design and Applications (1st ed.). CRC Press, Inc., Boca Raton, FL. Google ScholarGoogle ScholarDigital LibraryDigital Library
  37. P. Meo, E. Ferrara, F. Abel, et al. 2014. Analyzing user behavior across social sharing environments. ACM Trans. Intell. Syst. Technol. 5, 1 (2014), 14:1--14:31. Google ScholarGoogle ScholarDigital LibraryDigital Library
  38. Daniel Neil, Michael Pfeiffer, and Shih-Chii Liu. 2016. Phased LSTM: Accelerating recurrent network training for long or event-based sequences. In Proceedings of the NIPS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Liudmila Ostroumova Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin. 2018. CatBoost: Unbiased boosting with categorical features. In Proceedings of the NeurIPS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  40. J. Ross Quinlan. 1993. C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco, CA. Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Daniel M. Romero, Wojciech Galuba, Sitaram Asur, and Bernardo A. Huberman. 2011. Influence and passivity in social media. In Proceedings of the Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer. Google ScholarGoogle ScholarDigital LibraryDigital Library
  42. Guojie Song, Yuanhao Li, Xiaodong Chen, Xinran He, and Jie Tang. 2017. Influential node tracking on dynamic social network: An interchange greedy approach. IEEE Trans. Knowl. Data Eng. 29, 2 (2017), 359--372. Google ScholarGoogle ScholarDigital LibraryDigital Library
  43. Charles Spearman. 1904. The proof and measurement of association between two things. Amer. J. Psychol. 15, 1 (1904), 72--101.Google ScholarGoogle ScholarCross RefCross Ref
  44. Jie Tang, Tiancheng Lou, and Jon Kleinberg. 2012. Inferring social ties across heterogenous networks. In Proceedings of the ACM WSDM. Google ScholarGoogle ScholarDigital LibraryDigital Library
  45. Sonja Utz. 2010. Show me your friends and I will tell you what type of person you are: How one’s profile, number of friends, and type of friends influence impression formation on social network sites. J. Comput.-mediat. Commun. 15, 2 (2010), 314--335.Google ScholarGoogle Scholar
  46. Giridhari Venkatadri, Oana Goga, Changtao Zhong, Bimal Viswanath, Krishna P. Gummadi, and Nishanth Sastry. 2016. Strengthening weak identities through inter-domain trust transfer. In Proceedings of the WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library
  47. Gang Wang, Tianyi Wang, Haitao Zheng, and Ben Y. Zhao. 2014. Man vs. machine: Practical adversarial detection of malicious crowdsourcing workers. In Proceedings of the USENIX Security. Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. Xinghua Wang, Zhaohui Peng, Senzhang Wang, Philip S. Yu, Wenjing Fu, and Xiaoguang Hong. 2018. Cross-domain recommendation for cold-start users via neighborhood based feature mapping. In Proceedings of the DASFAA.Google ScholarGoogle ScholarCross RefCross Ref
  49. Bernard Lewis Welch. 1951. On the comparison of several mean values: An alternative approach. Biometrika 38, 3/4 (1951), 330--336.Google ScholarGoogle ScholarCross RefCross Ref
  50. Christo Wilson, Bryce Boe, Alessandra Sala, et al. 2009. User interactions in social networks and their implications. In Proceedings of the ACM EuroSys. Google ScholarGoogle ScholarDigital LibraryDigital Library
  51. Rong Xie, Yang Chen, Qinge Xie, Yu Xiao, and Xin Wang. 2018. We know your preferences in new cities: Mining and modeling the behavior of travelers. IEEE Commun. Mag. 56, 11 (2018), 28--35.Google ScholarGoogle ScholarCross RefCross Ref
  52. Wenzheng Xu, Mojtaba Rezvani, Weifa Liang, Jeffrey Xu Yu, and Chengfei Liu. 2017. Efficient algorithms for the identification of top-k structural hole spanners in large social networks. IEEE Trans. Knowl. Data Eng. 29, 5 (2017), 1017--1030. Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. Yiming Yang and Jan O. Pedersen. 1997. A comparative study on feature selection in text categorization. In Proceedings of the ICML. Google ScholarGoogle ScholarDigital LibraryDigital Library
  54. Yuanshun Yao, Bimal Viswanath, Jenna Cryan, Haitao Zheng, and Ben Y. Zhao. 2017. Automated crowdturfing attacks and defenses in online review systems. In Proceedings of the ACM CCS. Google ScholarGoogle ScholarDigital LibraryDigital Library
  55. Shaozhi Ye and Felix Wu. 2013. Measuring message propagation and social influence on twitter.com. Int. J. Commun. Netw. Distrib. Syst. 11, 1 (2013), 59--76. Google ScholarGoogle ScholarDigital LibraryDigital Library
  56. Hema Yoganarasimhan. 2012. Impact of social network structure on content propagation: A study using YouTube data. Quant. Market. Econ. 10, 1 (2012), 111--150.Google ScholarGoogle ScholarCross RefCross Ref
  57. Peng Zhang, Haiyi Zhu, Tun Lu, Hansu Gu, Wenjian Huang, and Ning Gu. 2017. Understanding relationship overlapping on social network sites: A case study of Weibo and Douban. Proc. ACM Hum.-comput. Interact. 1, CSCW (2017), 120:1--120:18. Google ScholarGoogle ScholarDigital LibraryDigital Library
  58. Changtao Zhong, Mostafa Salehi, Sunil Shah, Marius Cobzarenco, Nishanth Sastry, and Meeyoung Cha. 2014. Social bootstrapping: How Pinterest and Last.fm social communities benefit by borrowing links from Facebook. In Proceedings of the WWW. Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Cross-site Prediction on Social Influence for Cold-start Users in Online Social Networks

      Recommendations

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      Full Access

      • Published in

        cover image ACM Transactions on the Web
        ACM Transactions on the Web  Volume 15, Issue 2
        May 2021
        117 pages
        ISSN:1559-1131
        EISSN:1559-114X
        DOI:10.1145/3462271
        Issue’s Table of Contents

        Copyright © 2021 ACM

        Publisher

        Association for Computing Machinery

        New York, NY, United States

        Publication History

        • Published: 12 May 2021
        • Accepted: 1 June 2020
        • Revised: 1 February 2020
        • Received: 1 May 2019
        Published in tweb Volume 15, Issue 2

        Permissions

        Request permissions about this article.

        Request Permissions

        Check for updates

        Qualifiers

        • research-article
        • Research
        • Refereed

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      HTML Format

      View this article in HTML Format .

      View HTML Format
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!