# The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and Prevention

arXiv: https://arxiv.org/abs/2307.11864 · HTML: https://mlsanity.com/paper/2307.11864/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 21 Jul 2023.

## Builds on (most cited first, 6 of 6)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [Fame for sale: efficient detection of fake Twitter followers](https://mlsanity.com/paper/1509.04098/) (2015)
- [Friend or Faux: Graph-Based Early Detection of Fake Accounts on Social Networks](https://mlsanity.com/paper/2004.04834/) (2020)
- [Identifying Fake Profiles in LinkedIn](https://mlsanity.com/paper/2006.01381/) (2020)

## Built on by (most cited first, 18 of 18)

- [Can LLM-Generated Misinformation Be Detected?](https://mlsanity.com/paper/2309.13788/) (2023)
- [A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions](https://mlsanity.com/paper/2310.14724/) (2023)
- [Detecting Multimedia Generated by Large AI Models: A Survey](https://mlsanity.com/paper/2402.00045/) (2024)
- [Adaptive Ensembles of Fine-Tuned Transformers for LLM-Generated Text Detection](https://mlsanity.com/paper/2403.13335/) (2024)
- [DeceptionBench: A Comprehensive Benchmark for AI Deception Behaviors in Real-world Scenarios](https://mlsanity.com/paper/2510.15501/) (2025)
- [User Modeling in the Era of Large Language Models: Current Research and Future Directions](https://mlsanity.com/paper/2312.11518/) (2023)
- [Lateral Phishing With Large Language Models: A Large Organization Comparative Study](https://mlsanity.com/paper/2401.09727/) (2024)
- [When Autonomy Goes Rogue: Preparing for Risks of Multi-Agent Collusion in Social Systems](https://mlsanity.com/paper/2507.14660/) (2025)
- [Beyond Reactivity: Measuring Proactive Problem Solving in LLM Agents](https://mlsanity.com/paper/2510.19771/) (2025)
- [ESPERANTO: Evaluating Synthesized Phrases to Enhance Robustness in AI Detection for Text Origination](https://mlsanity.com/paper/2409.14285/) (2024)
- [Deciphering Textual Authenticity: A Generalized Strategy through the Lens of Large Language Semantics for Detecting Human vs. Machine-Generated Text](https://mlsanity.com/paper/2401.09407/) (2024)
- [Seeing Through AI's Lens: Enhancing Human Skepticism Towards LLM-Generated Fake News](https://mlsanity.com/paper/2406.14012/) (2024)
- [From Texts to Shields: Convergence of Large Language Models and Cybersecurity](https://mlsanity.com/paper/2505.00841/) (2025)
- [A Preliminary Exploration of YouTubers' Use of Generative-AI in Content Creation](https://mlsanity.com/paper/2403.06039/) (2024)
- [CATMark: A Context-Aware Thresholding Framework for Robust Cross-Task Watermarking in Large Language Models](https://mlsanity.com/paper/2510.02342/) (2025)
- [Application of AI-based Models for Online Fraud Detection and Analysis](https://mlsanity.com/paper/2409.19022/) (2024)
- [Black-Box Detection of LLM-Generated Text Using Generalized Jensen-Shannon Divergence](https://mlsanity.com/paper/2510.07500/) (2025)
- [Weak Links in LinkedIn: Enhancing Fake Profile Detection in the Age of LLMs](https://mlsanity.com/paper/2507.16860/) (2025)

## Abstract (arXiv)

In this paper, we present a novel method for detecting fake and Large Language Model (LLM)-generated profiles in the LinkedIn Online Social Network immediately upon registration and before establishing connections. Early fake profile identification is crucial to maintaining the platform's integrity since it prevents imposters from acquiring the private and sensitive information of legitimate users and from gaining an opportunity to increase their credibility for future phishing and scamming activities. This work uses textual information provided in LinkedIn profiles and introduces the Section and Subsection Tag Embedding (SSTE) method to enhance the discriminative characteristics of these data for distinguishing between legitimate profiles and those created by imposters manually or by using an LLM. Additionally, the dearth of a large publicly available LinkedIn dataset motivated us to collect 3600 LinkedIn profiles for our research. We will release our dataset publicly for research purposes. This is, to the best of our knowledge, the first large publicly available LinkedIn dataset for fake LinkedIn account detection. Within our paradigm, we assess static and contextualized word embeddings, including GloVe, Flair, BERT, and RoBERTa. We show that the suggested method can distinguish between legitimate and fake profiles with an accuracy of about 95% across all word embeddings. In addition, we show that SSTE has a promising accuracy for identifying LLM-generated profiles, despite the fact that no LLM-generated profiles were employed during the training phase, and can achieve an accuracy of approximately 90% when only 20 LLM-generated profiles are added to the training set. It is a significant finding since the proliferation of several LLMs in the near future makes it extremely challenging to design a single system that can identify profiles created with various LLMs.