Skip to main content

The Only Link You’ll Ever Need: How Social Media Reference Landing Pages Speed Up Profile Matching

  • Conference paper
  • First Online:
Information and Software Technologies (ICIST 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1665))

Included in the following conference series:

  • 494 Accesses

Abstract

The Web is characterized by user interaction on Online Social Networks, the exchange of content on a large scale, and the presentation of one’s own life on several digital channels using different media. Users strive to reach as many people as possible with their content while also distributing traffic across the various networks. To simplify this, there are Social Media Reference Landing Pages where users can bring together their numerous social media profiles. Our research project investigates the threat to users posed by the shared content, such as blackmailing or doxing. An important step is finding and merging different user profiles, primarily based on hints, similar user names, or links. In this paper, we show how Reference Landing Pages make it easier to create comprehensive Digital Twins, which we can use to compute and make tangible the risk of thoughtless sharing of information to users.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    See https://frame-for-business.de/?page_id=14485 (2022-03-14).

  2. 2.

    Authority-Dependent Risk Identification and Analysis in online Networks.

  3. 3.

    See https://pypi.org/project/beautifulsoup4/ (2022-03-14).

  4. 4.

    See https://huggingface.co/flair/ner-english (2022-03-14).

  5. 5.

    See https://github.com/derek73/python-nameparser (2022-03-14).

  6. 6.

    See https://github.com/philipperemy/name-dataset (2022-03-14).

  7. 7.

    See https://huggingface.co/sentence-transformers/all-mpnet-base-v2 (2022-03-14).

References

  1. Agarwal, A., Toshniwal, D.: SmPFT: social media based profile fusion technique for data enrichment. Comput. Netw. 158, 123–131 (2019)

    Article  Google Scholar 

  2. Ahmad, W., Ali, R.: User identification across multiple online social networks using cross link attribute and network relationship. J. Interdiscip. Math. 23 (2020). https://doi.org/10.1080/09720502.2020.1721713

  3. Barricelli, B.R., Casiraghi, E., Fogli, D.: A survey on digital twin: definitions, characteristics, applications, and design implications. IEEE Access 7, 167653–167671 (2019). https://doi.org/10.1109/ACCESS.2019.2953499

    Article  Google Scholar 

  4. Bäumer, F.S., Grote, N., Kersting, J., Geierhos, M.: Privacy matters: detecting nocuous patient data exposure in online physician reviews. In: Damaševičius, R., Mikašytė, V. (eds.) ICIST 2017. CCIS, vol. 756, pp. 77–89. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67642-5_7

    Chapter  Google Scholar 

  5. Bäumer, F.S., Kersting, J., Orlikowski, M., Geierhos, M.: Towards a multi-stage approach to detect privacy breaches in physician reviews. In: SEMANTICS Posters & Demos (2018)

    Google Scholar 

  6. Bennacer, N., Nana Jipmo, C., Penta, A., Quercini, G.: Matching user profiles across social networks. In: Jarke, M., et al. (eds.) CAiSE 2014. LNCS, vol. 8484, pp. 424–438. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07881-6_29

    Chapter  Google Scholar 

  7. Bettendorf, S.: Hilfreiche Programme. In: Instagram-Journalismus für die Praxis, pp. 97–101. Springer, Wiesbaden (2020). https://doi.org/10.1007/978-3-658-31484-2_14

    Chapter  Google Scholar 

  8. Bäumer, F.S., Denisov, S., Su Lee, Y., Geierhos, M.: Towards authority-dependent risk identification and analysis in online networks. In: Halimi, A., Ayday, E. (eds.) Proceedings of the IST-190 Research Symposium (RSY) on AI, ML and BD for Hybrid Military Operations (AI4HMO), October 2021

    Google Scholar 

  9. Cai, C., Li, L., Chen, W., Zeng, D.D.: Capturing deep dynamic information for mapping users across social networks. In: 2019 IEEE International Conference on Intelligence and Security Informatics, ISI 2019, May 2019. https://doi.org/10.1109/ISI.2019.8823341

  10. Data Portal, January 2022. https://datareportal.com/reports/digital-2022-global-overview-report

  11. Goga, O., Lei, H., Parthasarathi, S.H.K., Friedland, G., Sommer, R., Teixeira, R.: Exploiting innocuous activity for correlating users across sites. In: Proceedings of the 22nd International Conference on World Wide Web, pp. 447–458 (2013)

    Google Scholar 

  12. Halimi, A., Ayday, E.: Efficient quantification of profile matching risk in social networks using belief propagation. In: Chen, L., Li, N., Liang, K., Schneider, S. (eds.) ESORICS 2020. LNCS, vol. 12308, pp. 110–130. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58951-6_6

    Chapter  Google Scholar 

  13. Kammakomati, M., Battula, S.V.: MergeURL: an effective URL merging and shortening service (2020)

    Google Scholar 

  14. Kasbekar, P., Potika, K., Pollett, C.: Find me if you can: aligning users in different social networks. In: Proceedings of the 2020 IEEE 6th International Conference on Big Data Computing Service and Applications, BigDataService 2020, August 2020, pp. 46–53. https://doi.org/10.1109/BigDataService49289.2020.00015

  15. Li, Y., Ji, W., Gao, X., Deng, Y., Dong, W., Li, D.: Matching user accounts with spatio-temporal awareness across social networks. Inf. Sci. 570, 1–15 (2021)

    Article  MathSciNet  Google Scholar 

  16. Li, Y., Peng, Y., Zhang, Z., Yin, H., Xu, Q.: Matching user accounts across social networks based on username and display name. World Wide Web 22(3), 1075–1097 (2018). https://doi.org/10.1007/s11280-018-0571-4

    Article  Google Scholar 

  17. Linktree: Linktr.ee: About (2022). https://linktr.ee/s/about/

  18. Metzger, M.J.: Effects of site, vendor, and consumer characteristics on web site trust and disclosure. Commun. Res. 33(3), 155–179 (2006). https://doi.org/10.1177/0093650206287076

    Article  Google Scholar 

  19. Müngen, A., Gündoğan, E., Kaya, M.: Identifying multiple social network accounts belonging to the same users. Soc. Netw. Anal. Min. 11, 29 (2021)

    Article  Google Scholar 

  20. Sheehan, K.B., Hoy, M.G.: Dimensions of privacy concern among online consumers. J. Public Policy Mark. 19(1), 62–73 (2000). http://www.jstor.org/stable/30000488

  21. Shoeibi, N., Shoeibi, N., Chamoso, P., AlizadehSani, Z., Corchado, J.: Similarity approximation of twitter profiles (2021)

    Google Scholar 

  22. Sokhin, T., Butakov, N., Nasonov, D.: User profiles matching for different social networks based on faces identification. Hybrid Artif. Intell. Syst. 551–562 (2019). https://doi.org/10.1007/978-3-030-29859-3_47. http://dx.doi.org/10.1007/978-3-030-29859-3_47

  23. Soltani, R., Abhari, A.: Identity matching in social media platforms. In: 2013 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS), pp. 64–70 (2013)

    Google Scholar 

  24. Xing, L., Deng, K., Wu, H., Xie, P., Gao, J.: Behavioral habits-based user identification across social networks. Symmetry 11, 1134 (2019). https://doi.org/10.3390/sym11091134

    Article  Google Scholar 

  25. Xing, L., Deng, K., Wu, H., Xie, P., Zhang, M., Wu, Q.: Exploiting two-level information entropy across social networks for user identification. Wirel. Commun. Mob. Comput. 2021, 1–15 (2021). https://doi.org/10.1155/2021/1082391

    Article  Google Scholar 

Download references

Acknowledgements

This research is funded by dtec.bw – Digitalization and Technology Research Center of the Bundeswehr.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sergej Denisov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Denisov, S., Bäumer, F.S. (2022). The Only Link You’ll Ever Need: How Social Media Reference Landing Pages Speed Up Profile Matching. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2022. Communications in Computer and Information Science, vol 1665. Springer, Cham. https://doi.org/10.1007/978-3-031-16302-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-16302-9_10

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-16301-2

  • Online ISBN: 978-3-031-16302-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics