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.
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Notes
- 1.
See https://frame-for-business.de/?page_id=14485 (2022-03-14).
- 2.
Authority-Dependent Risk Identification and Analysis in online Networks.
- 3.
See https://pypi.org/project/beautifulsoup4/ (2022-03-14).
- 4.
See https://huggingface.co/flair/ner-english (2022-03-14).
- 5.
See https://github.com/derek73/python-nameparser (2022-03-14).
- 6.
See https://github.com/philipperemy/name-dataset (2022-03-14).
- 7.
See https://huggingface.co/sentence-transformers/all-mpnet-base-v2 (2022-03-14).
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This research is funded by dtec.bw – Digitalization and Technology Research Center of the Bundeswehr.
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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
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