The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
The paper introduces Section and Subsection Tag Embedding (SSTE), a text-only method that detects fake and ChatGPT-generated LinkedIn profiles at registration time with roughly 95% accuracy, and releases a new 3600-profile LinkedIn dataset.- doi
- 10.1145/3603163.3609064
- isbn
- 979-8-4007-0232-7
- name
- The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention
- source
- acm_url
- https://dl.acm.org/doi/3603163.3609064
- authors
- Navid Ayoobi, Arjun Mukherjee, Sadat Shahriar
- doi_url
- https://doi.org/10.1145/3603163.3609064
- license
- © Copyright held by the owner/author(s). Publication rights licensed to ACM.
- summary
- The paper introduces Section and Subsection Tag Embedding (SSTE), a text-only method that detects fake and ChatGPT-generated LinkedIn profiles at registration time with roughly 95% accuracy, and releases a new 3600-profile LinkedIn dataset.
- arxiv_url
- https://arxiv.org/abs/2307.11864
- published
- 2023-09-04
- conference
- HT '23: 34th ACM Conference on Hypertext and Social Media, Rome, Italy, September 4-8, 2023
- open_access
- false
- acm_html_url
- https://dl.acm.org/doi/full/3603163.3609064
- displayAuthor
- Navid Ayoobi (University of Houston, Houston, Texas, USA), Sadat Shahriar (University of Houston, Houston, Texas, USA), and Arjun Mukherjee (University of Houston, Houston, Texas, USA)
- displayPublishTime
- 2023-09-04
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