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Profile Reconciliation Through Dynamic Activities Across Social Networks

  • Suela IsajEmail author
  • Nacéra Bennacer Seghouani
  • Gianluca Quercini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11483)

Abstract

Since today’s online social media serve diverse purposes such as social and professional networking, photo and blog sharing, it is not uncommon for people to have multiple profiles across different social networks. Finding or reconciling these profiles would allow the creation of a holistic view of different facets of a person’s life that can be used by recommender systems, human resource management, marketing activities and also raise awareness about the potential threats to one person’s privacy. In this paper, we propose a new approach for reconciling profiles based on their temporal activity (i.e., timestamped posts) shared across similar-scope social networks. The timestamped posts are compared by considering different dynamic attributes originating from what the user shares (geographical data, text, tags, and photos) and static attributes (username and real name). Our evaluation on Flickr and Twitter social networks datasets shows that the temporal activity is a good predictor of two profiles referring or not to the same user.

Keywords

Social networks Reconciliation Entity resolution 

References

  1. 1.
    Bartunov, S., Korshunov, A., Park, S.T., Ryu, W., Lee, H.: Joint link-attribute user identity resolution in online social networks. In: SNA-KDD. ACM (2012)Google Scholar
  2. 2.
    Buccafurri, F., Lax, G., Nocera, A., Ursino, D.: Discovering missing me edges across social networks. Inf. Sci. 319, 18–37 (2015)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chiang, Y.H., Doan, A., Naughton, J.F.: Modeling entity evolution for temporal record matching. In: SIGMOD, pp. 1175–1186. ACM (2014)Google Scholar
  4. 4.
    Edwards, M., Wattam, S., Rayson, P., Rashid, A.: Sampling labelled profile data for identity resolution. In: IEEE Big Data, pp. 540–547. IEEE (2016)Google Scholar
  5. 5.
    Goga, O., Lei, H., Parthasarathi, S.H.K., Friedland, G., Sommer, R., Teixeira, R.: Exploiting innocuous activity for correlating users across sites. In: WWW, pp. 447–458. ACM (2013)Google Scholar
  6. 6.
    Golbeck, J., Rothstein, M.: Linking social networks on the web with FOAF: a semantic web case study. AAAI 8, 1138–1143 (2008)Google Scholar
  7. 7.
    Greenwood, S., Perrin, A., Duggan, M.: Social media update 2016. Pew Research Center, November 2016Google Scholar
  8. 8.
    Gross, R., Acquisti, A.: Information revelation and privacy in online social networks. In: WPES Workshop, pp. 71–80. ACM (2005)Google Scholar
  9. 9.
    Hassaballah, M., Abdelmgeid, A.A., Alshazly, H.A.: Image features detection, description and matching. In: Awad, A.I., Hassaballah, M. (eds.) Image Feature Detectors and Descriptors. SCI, vol. 630, pp. 11–45. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-28854-3_2CrossRefGoogle Scholar
  10. 10.
    Iofciu, T., Fankhauser, P., Abel, F., Bischoff, K.: Identifying users across social tagging systems. In: ICWSM (2011)Google Scholar
  11. 11.
    Kong, X., Zhang, J., Yu, P.S.: Inferring anchor links across multiple heterogeneous social networks. In: CIKM, pp. 179–188. ACM (2013)Google Scholar
  12. 12.
    Korula, N., Lattanzi, S.: An efficient reconciliation algorithm for social networks. Proc. VLDB Endow. 7(5), 377–388 (2014)CrossRefGoogle Scholar
  13. 13.
    Laganière, R.: OpenCV Computer Vision Application Programming Cookbook, 2nd edn. Packt Publishing Ltd., Birmingham (2014)Google Scholar
  14. 14.
    Liu, L., Cheung, W.K., Li, X., Liao, L.: Aligning users across social networks using network embedding. In: IJCAI, pp. 1774–1780 (2016)Google Scholar
  15. 15.
    Liu, S., Wang, S., Zhu, F., Zhang, J., Krishnan, R.: HYDRA: large-scale social identity linkage via heterogeneous behavior modeling. In: SIGMOD, pp. 51–62. ACM (2014)Google Scholar
  16. 16.
    Malhotra, A., Totti, L., Meira, W., Kumaraguru, P., Almeida, V.: Studying user footprints in different online social networks. In: ASONAM. ACM (2012)Google Scholar
  17. 17.
    Man, T., Shen, H., Liu, S., Jin, X., Cheng, X.: Predict anchor links across social networks via an embedding approach. In: IJCAI, pp. 1823–1829 (2016)Google Scholar
  18. 18.
    Minder, P., Bernstein, A.: Social network aggregation using face-recognition. In: ISWC 2011 Workshop: Social Data on the Web. Citeseer (2011)Google Scholar
  19. 19.
    Narayanan, A., Shmatikov, V.: De-anonymizing social networks. In: 30th IEEE Symposium on Security and Privacy, pp. 173–187. IEEE (2009)Google Scholar
  20. 20.
    Panchenko, A., Babaev, D., Obiedkov, S.: Large-scale parallel matching of social network profiles. In: Khachay, M.Y., Konstantinova, N., Panchenko, A., Ignatov, D.I., Labunets, V.G. (eds.) AIST 2015. CCIS, vol. 542, pp. 275–285. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-26123-2_27CrossRefGoogle Scholar
  21. 21.
    Papadakis, G., Ioannou, E., Niederée, C., Palpanas, T., Nejdl, W.: Beyond 100 million entities: large-scale blocking-based resolution for heterogeneous data. In: WSDM, pp. 53–62. ACM (2012)Google Scholar
  22. 22.
    Perito, D., Castelluccia, C., Kaafar, M.A., Manils, P.: How unique and traceable are usernames? In: Fischer-Hübner, S., Hopper, N. (eds.) PETS 2011. LNCS, vol. 6794, pp. 1–17. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-22263-4_1CrossRefGoogle Scholar
  23. 23.
    Quercini, G., Bennacer, N., Ghufran, M., Nana Jipmo, C.: LIAISON: reconciLIAtion of Individuals profiles across SOcial Networks. In: Guillet, F., Pinaud, B., Venturini, G. (eds.) Advances in Knowledge Discovery and Management. SCI, vol. 665, pp. 229–253. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-45763-5_12CrossRefGoogle Scholar
  24. 24.
    Riederer, C., Kim, Y., Chaintreau, A., Korula, N., Lattanzi, S.: Linking users across domains with location data: theory and validation. In: WWW, pp. 707–719 (2016)Google Scholar
  25. 25.
    Shu, K., Wang, S., Tang, J., Zafarani, R., Liu, H.: User identity linkage across online social networks: a review. SIGKDD Explor. Newslett. 18(2), 5–17 (2017)CrossRefGoogle Scholar
  26. 26.
    Vosoughi, S., Zhou, H., Roy, D.: Digital stylometry: linking profiles across social networks. SocInfo 2015. LNCS, vol. 9471, pp. 164–177. Springer, Cham (2015).  https://doi.org/10.1007/978-3-319-27433-1_12CrossRefGoogle Scholar
  27. 27.
    Zafarani, R., Liu, H.: Connecting users across social media sites: a behavioral-modeling approach. In: KDD, pp. 41–49. ACM (2013)Google Scholar
  28. 28.
    Zhang, Y., Tang, J., Yang, Z., Pei, J., Yu, P.S.: COSNET: connecting heterogeneous social networks with local and global consistency. In: KDD, pp. 1485–1494. ACM (2015)Google Scholar
  29. 29.
    Zhou, X., Liang, X., Zhang, H., Ma, Y.: Cross-platform identification of anonymous identical users in multiple social media networks. TKDE 28(2), 411–424 (2016)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Suela Isaj
    • 1
    • 2
    Email author
  • Nacéra Bennacer Seghouani
    • 1
  • Gianluca Quercini
    • 1
  1. 1.Laboratoire de Recherche en Informatique LRIGif-sur-YvetteFrance
  2. 2.Aalborg UniversityAalborgDenmark

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