Advertisement

Provenance of Explicit and Implicit Interactions on Social Media with W3C PROV-DM

  • Io TaxidouEmail author
  • Tom De Nies
  • Peter M. Fischer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11406)

Abstract

In recent years, research in information diffusion in social media has attracted a lot of attention, since the data produced is fast, massive and viral. The provenance of such data is equally important because it helps to judge the relevance and trustworthiness of the information enclosed in the data. However, social media currently provide insufficient mechanisms for provenance, while models of information diffusion use their own concepts and notations, targeted to specific use cases. In this paper, we present and extend our model for information diffusion and provenance, based on the W3C PROV Data Model. PROV provides a Web-native and interoperable format that allows easy publication of provenance data, and minimizes the integration effort among different systems making use of PROV. We provide computational methods for provenance reconstruction of user interactions based on the investigation of human behaviour on social media.

Keywords

Provenance Information diffusion User interactions PROV-DM 

References

  1. 1.
    Al Hasan, M., Salem, S., Zaki, M.J.: SimClus: an effective algorithm for clustering with a lower bound on similarity. Knowl. Inf. Syst. 28(3), 665–685 (2011)CrossRefGoogle Scholar
  2. 2.
    Bakshy, E., Hofman, J.M., Mason, W.A., Watts, D.J.: Everyone’s an influencer: quantifying influence on Twitter. In: WSDM, pp. 65–74 (2011)Google Scholar
  3. 3.
    Baños, R.A., Borge-Holthoefer, J., Moreno, Y.: The role of hidden influentials in the diffusion of online information cascades. EPJ Data Sci. 2(1), 1–16 (2013)CrossRefGoogle Scholar
  4. 4.
    Barbier, G., Feng, Z., Gundecha, P., Liu, H.: Provenance data in social media. Synth. Lect. Data Min. Knowl. Discov. 4(1), 1–84 (2013) CrossRefGoogle Scholar
  5. 5.
    Barbosa, S., Cesar-Jr, R.M., Cosley, D.: Using text similarity to detect social interactions not captured by formal reply mechanisms. In: 2015 IEEE 11th International Conference on e-Science (e-Science), pp. 36–46. IEEE (2015)Google Scholar
  6. 6.
    Cheney, J., Chiticariu, L., Tan, W.-C.: Provenance in Databases: Why, How, and Where, vol. 4. Now Publishers, Inc., Hanover (2009)Google Scholar
  7. 7.
    Cheney, J.: W3C Provenance Working Group: Semantics of the PROV Data Model. W3C Note, 30 April 2013Google Scholar
  8. 8.
    De Nies, T., Coppens, S., Mannens, E. Van de Walle, R.: Modeling uncertain provenance and provenance of uncertainty in W3C PROV. In: WWW (Companion Volume), pp. 167–168 (2013)Google Scholar
  9. 9.
    De Nies, T., Coppens, S., Van Deursen, D., Mannens, E., Van de Walle, R.: Automatic discovery of high-level provenance using semantic similarity. In: Groth, P., Frew, J. (eds.) IPAW 2012. LNCS, vol. 7525, pp. 97–110. Springer, Heidelberg (2012).  https://doi.org/10.1007/978-3-642-34222-6_8CrossRefGoogle Scholar
  10. 10.
    De Nies, T., et al.: Git2PROV: exposing version control system content as W3C PROV. In: ISWC (Posters & Demos), pp. 125–128 (2013)Google Scholar
  11. 11.
    De Nies, T., et al.: Towards multi-level provenance reconstruction of information diffusion on social media. In: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, pp. 1823–1826 (2015)Google Scholar
  12. 12.
    Dimou, A., Vander Sande, M., Colpaert, P., Verborgh, R., Mannens, E., Van de Walle, R.: RML: a generic language for integrated RDF mappings of heterogeneous data. In: Proceedings of the 7th Workshop on Linked Data on the Web (LDOW 2014), Seoul, Korea (2014)Google Scholar
  13. 13.
    Feng, Z., Gundecha, P., Liu, H.: Recovering information recipients in social media via provenance. In: ASONAM, pp. 706–711 (2013)Google Scholar
  14. 14.
    Glavic, B., Sheykh Esmaili, K., Fischer, P.M., Tatbul, N.: Ariadne: managing fine-grained provenance on data streams. In: Distributed Event-Based Systems, pp. 39–50 (2013)Google Scholar
  15. 15.
    Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. ACM SIGMOD Rec. 42(2), 17–28 (2013)CrossRefGoogle Scholar
  16. 16.
    Gundecha, P., Feng, Z., Liu, H.: Seeking provenance of information using social media. In: CIKM (2013)Google Scholar
  17. 17.
    Gundecha, P., Ranganath, S., Feng, Z., Liu, H.: A tool for collecting provenance data in social media. In: KDD, pp. 1462–1465 (2013)Google Scholar
  18. 18.
    Huynh, T., Groth, P., Zednik, S. (eds.) and W3C Provenance Working Group: PROV Implementation Report. W3C Working Group Note, 30 April 2013Google Scholar
  19. 19.
    Kempe, D., Kleinberg, J., Tardos, É.: Maximizing the spread of influence through a social network. In: SIGKDD pp. 137–146 (2003)Google Scholar
  20. 20.
    Kovács, F., Legány, C., Babos, A.: Cluster validity measurement techniques. In: 6th International Symposium of Hungarian Researchers on Computational Intelligence. Citeseer (2005)Google Scholar
  21. 21.
    Kwon, S., Cha, M., Jung, K., Chen, W., Wang, Y.: Prominent features of rumor propagation in online social media. In: 2013 IEEE 13th International Conference on Data Mining (ICDM), pp. 1103–1108. IEEE (2013)Google Scholar
  22. 22.
    Leskovec, J., Backstrom, L., Kleinberg, J.: Meme-tracking and the dynamics of the news cycle. In: KDD, pp. 497–506 (2009)Google Scholar
  23. 23.
    Magliacane, S., Groth, P.T., et al.: Towards reconstructing the provenance of clinical guidelines. In: SWAT4LS (2012)Google Scholar
  24. 24.
    Maumet, C., Flandin, G., Nichols, B., Steffener, J., Helmer, K., et al.: Extending NI-DM to share the results and provenance of a neuroimaging study: implementation within SPM and FSL. Front. Neuroinform. (2014) Google Scholar
  25. 25.
    Missier, P., Chen, Z.: Extracting PROV provenance traces from Wikipedia history pages. In: EDBT/ICDT (Workshops), pp. 327–330 (2013)Google Scholar
  26. 26.
    Moreau, L., Missier, P. (eds.) and W3C Provenance Working Group: PROV-DM: The PROV Data Model. W3C (2013)Google Scholar
  27. 27.
    Myers, S.A., Zhu, C., Leskovec, J.: Information diffusion and external influence in networks. In: SIGKDD, pp. 33–41 (2012)Google Scholar
  28. 28.
    Simmons, M.P., Adamic, L.A., Adar, E.: Memes online: extracted, subtracted, injected, and recollected. ICWSM 11, 17–21 (2011)Google Scholar
  29. 29.
    Taxidou, I., De Nies, T., Verborgh, R., Fischer, P.M., Mannens, E., Van de Walle, R.: Modeling information diffusion in social media as provenance with W3C PROV. In: Proceedings of the 24th International Conference on World Wide Web, pp. 819–824 (2015)Google Scholar
  30. 30.
    Taxidou, I., Fischer, P.M.: Online analysis of information diffusion in Twitter. In: Proceedings of the 23rd International Conference on WWW Companion, pp. 1313–1318 (2014)Google Scholar
  31. 31.
    Taxidou, I., Fischer, P.M.: Online analysis of information diffusion in Twitter. In: WWW (Companion Volume), pp. 1313–1318 (2014)Google Scholar
  32. 32.
    Taxidou, I., Fischer, P.M., De Nies, T., Mannens, E., Van de Walle, R.: Information diffusion and provenance of interactions in Twitter: is it only about retweets? In: Proceedings of the 25th International Conference Companion on World Wide Web, pp. 113–114 (2016)Google Scholar
  33. 33.
    Taxidou, I., Lieber, S., Fischer, P.M., De Nies, T., Verborgh, R.: Web-scale provenance reconstruction of implicit information diffusion on social media. Under Review (2017)Google Scholar
  34. 34.
    Wilson, C., Sala, A., Puttaswamy, K.P., Zhao, B.Y.: Beyond social graphs: user interactions in online social networks and their implications. ACM Trans. Web 6(4), 17 (2012)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of FreiburgFreiburgGermany
  2. 2.Ghent University - imec - IDLabGhentBelgium

Personalised recommendations