Advertisement

Cross-Network Social Multimedia Computing

  • Jitao SangEmail author
Chapter
Part of the Springer Theses book series (Springer Theses)

Abstract

Social multimedia contributes significantly to the arrival of the Big Data era. The distribution of social multimedia content and users’ social multimedia activities among various social media networks motivate us to investigate social multimedia computing under the cross-network circumstances. We interpret cross-network as the “variety” of social multimedia: the heterogeneous data in various social media networks. In this chapter, basic tasks of user-centric social multimedia computing are extended under the cross-network circumstances, by exploiting the overlapped users among social media networks.

Keywords

Social Networking Site Latent Dirichlet Allocation Topic Distribution Normalize Discount Cumulative Gain Social Media Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Abel, F., Araújo, S., Gao, Q., Houben. G.-J.: Analyzing cross-system user modeling on the social web. In: Web Engineering, pp. 28–43. Springer (2011)Google Scholar
  2. 2.
    Althoff, T., Borth, D., Hees, J., Dengel. A.: Analysis and forecasting of trending topics in online media streams. In: Proceedings of the 21st ACM International Conference on Multimedia, pp. 907–916 (2013)Google Scholar
  3. 3.
    Becker, H., Iter, D., Naaman, M., Gravano. L.: Identifying content for planned events across social media sites. In: Proceedings of the Fifth ACM International Conference on Web Search and Data Mining, pp. 533–542 (2012)Google Scholar
  4. 4.
    Blei, D.M., Jordan, M. I.: Modeling annotated data. In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval, pp. 127–134 (2003)Google Scholar
  5. 5.
    Carmagnola, F., Cena, F.: User identification for cross-system personalisation. Inform. Sci. 179(1), 16–32 (2009)CrossRefGoogle Scholar
  6. 6.
    Cha ,M., Pérez, J., Haddadi .H.: Flash floods and ripples: the spread of media content through the blogosphere. In: ICWSM 2009: Proceedings of the 3rd AAAI International Conference on Weblogs and Social Media (2009)Google Scholar
  7. 7.
    De Choudhury, M., Sundaram, H.: Why do we converse on social media?: An analysis of intrinsic and extrinsic network factors. In: Proceedings of the 3rd ACM SIGMM International Workshop on Social Media, pp. 53–58 (2011)Google Scholar
  8. 8.
    Deng, .Z., Sang, J., Xu, C.: Personalized video recommendation based on cross-platform user modeling. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6. IEEE (2013)Google Scholar
  9. 9.
    Ding, X., Zhang, L., Wan, Z., Gu, M.: De-anonymizing dynamic social networks. In: Global Telecommunications Conference (GLOBECOM 2011), pp. 1–6. IEEE (2011)Google Scholar
  10. 10.
    Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., et al.: Least angle regression. The Ann. Stat. 32(2), 407–499 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Gomez Rodriguez, M., Leskovec, J., Schölkopf, B.: Structure and dynamics of information pathways in online media. In: Proceedings of the Sixth ACM International Conference on Web Search and Data Mining, pp. 23–32 (2013)Google Scholar
  12. 12.
    Guo, L., Tan, E., Chen, S., Zhang, X., Zhao, Y. E.: Analyzing patterns of user content generation in online social networks. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 369–378 (2009)Google Scholar
  13. 13.
    Hu, M., Liu, S., Wei, F., Wu, Y., Stasko, J., Ma, K.-L.: Breaking news on twitter. In: CHI, pp. 2751–2754 (2012)Google Scholar
  14. 14.
    Iofciu, T., Fankhauser, P., Abel, F., Bischoff, K.: Identifying users across social tagging systems. In: ICWSM (2011)Google Scholar
  15. 15.
    Kim, M., Newth, D., Christen, P.: Trends of news diffusion in social media based on crowd phenomena. In: World Wide Web Companion, pp. 753–758 (2014)Google Scholar
  16. 16.
    Lerman, K., Ghosh, R.: Information contagion: An empirical study of the spread of news on digg and twitter social networks. In: ICWSM, pp. 90–97 (2010)Google Scholar
  17. 17.
    Leskovec, J., McGlohon, M., Faloutsos, C., Glance, N.S., Hurst, M.: Patterns of cascading behavior in large blog graphs. SDM 7, 551–556 (2007)Google Scholar
  18. 18.
    Magnani, M., Rossi, L.: The ml-model for multi-layer social networks. In: 2011 International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 5–12 (2011)Google Scholar
  19. 19.
    Mislove, A., Marcon, M., Gummadi, K. P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 7th ACM SIGCOMM Conference on Internet Measurement, pp. 29–42 (2007)Google Scholar
  20. 20.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 2009 30th IEEE Symposium on Security and Privacy, pp. 173–187 (2009)Google Scholar
  21. 21.
    Osborne, M., Petrovic, S., McCreadie, R., Macdonald, C., Ounis, I.: Bieber no more: first story detection using twitter and wikipedia. In: Proceedings of the Workshop on Time-aware Information Access. TAIA, vol. 12 (2012)Google Scholar
  22. 22.
    Roy, S. D., Mei, T., Zeng, W., Li, S.: Empowering cross-domain internet media with real-time topic learning from social streams. In: 2012 IEEE International Conference on Multimedia and Expo (ICME), pp. 49–54 (2012)Google Scholar
  23. 23.
    Roy, S. D., Mei, T., Zeng, W., Li, S.: Socialtransfer: cross-domain transfer learning from social streams for media applications. In: ACM Multimedia, pp. 649–658. ACM (2012)Google Scholar
  24. 24.
    Roy, S.D., Mei, T., Zeng, W., Li, S.: Towards Cross-domain Learning for Social Video Popularity Prediction. IEEE Trans. Multimedia 15(6), 1255–1267 (2013)CrossRefGoogle Scholar
  25. 25.
    Sundaram, H.: Experiential media systems. ACM Trans. Multimedia Comput. Commun. and Appl. (TOMCCAP) 9(1s), 49 (2013)Google Scholar
  26. 26.
    Tsagkias, M., de Rijke, M., Weerkamp, W.: Linking online news and social media. In: Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, pp. 565–574 (2011)Google Scholar
  27. 27.
    Wang, P., He, W., Zhao, J.: A tale of three social networks: User activity comparisons across facebook, twitter, and foursquare. Internet Comput. IEEE 18(2), 10–15 (2014)CrossRefGoogle Scholar
  28. 28.
    Xiang, L., Yuan, Q., Zhao, S., Chen, L., Zhang, X., Yang, Q., Sun, J.: Temporal recommendation on graphs via long-and short-term preference fusion. In: SIGKDD, pp. 723–732 (2010)Google Scholar
  29. 29.
    Yan, M., Sang, J., Mei, T., Xu, C.: Friend transfer: cold-start friend recommendation with cross-platform transfer learning of social knowledge. In: 2013 IEEE International Conference on Multimedia and Expo (ICME), pp. 1–6 (2013)Google Scholar
  30. 30.
    Yuan, N. J., Zhang, F., Lian, D., Zheng, K., Yu, S., Xie, X.: We know how you live: exploring the spectrum of urban lifestyles. In: Proceedings of the First ACM Conference on Online Social Networks, pp. 3–14 (2013)Google Scholar
  31. 31.
    Zafarani, R., Liu, H., Connecting users across social media sites: a behavioral-modeling approach. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 41–49 (2013)Google Scholar
  32. 32.
    Zhang, F., Yuan, N. J., Lian, D., Xie, X.: Mining novelty-seeking trait across heterogeneous domains. In: Proceedings of the 23rd International Conference on World Wide Web, pp. 373–384 (2014)Google Scholar
  33. 33.
    Zhao, W.X., Jiang, J., Weng, J., He, J., Lim, E.-P., Yan, H., Li, X.: Comparing twitter and traditional media using topic models. In: Advances in Information Retrieval, pp. 338–349 (2011)Google Scholar
  34. 34.
    Zhong, E., Fan, W., Zhu, Y., Yang, Q.: Modeling the dynamics of composite social networks. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 937–945 (2013)Google Scholar
  35. 35.
    Zhong, E., Fan, W., Yang, Q.: User behavior learning and transfer in composite social networks. ACM Trans. Knowl. Discov. Data (TKDD) 8(1), 6 (2014)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.National Lab of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations