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Social Media as a Vector for Escort Ads:A Study on OnlyFans advertisements on Twitter

Published: 30 April 2023 Publication History

Abstract

Online sex trafficking is on the rise and a majority of trafficking victims report being advertised online. The use of OnlyFans as a platform for adult content is also increasing, with Twitter as its main advertising tool. Furthermore, we know that traffickers usually work within a network and control multiple victims. Consequently, we suspect that there may be networks of traffickers promoting multiple OnlyFans accounts belonging to their victims. To this end, we present the first study of OnlyFans advertisements on Twitter in the context of finding organized activities.
Preliminary analysis of this space shows that most tweets related to OnlyFans contain generic text, making text-based methods less reliable. Instead, focusing on what ties the authors of these tweets together, we propose a novel method for uncovering coordinated networks of users based on their behaviour. Our method, called Multi-Level Clustering (MLC), combines two levels of clustering that considers both the network structure as well as embedded node attribute information. It focuses jointly on user connections (through mentions) and content (through shared URLs). We apply MLC to real-world data of 2 million tweets pertaining to OnlyFans and analyse the detected groups. We also evaluate our method on synthetically generated data (with injected ground truth) and show its superior performance compared to competitive baselines. Finally, we discuss examples of organized clusters as case studies and provide interesting conclusions to our study.

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cover image ACM Conferences
WebSci '23: Proceedings of the 15th ACM Web Science Conference 2023
April 2023
373 pages
ISBN:9798400700897
DOI:10.1145/3578503
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Publication History

Published: 30 April 2023

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Author Tags

  1. clustering
  2. community detection
  3. dense block detection
  4. social media networks

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WebSci '23
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WebSci '23: 15th ACM Web Science Conference 2023
April 30 - May 1, 2023
TX, Austin, USA

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