Mila is hosting its first quantum computing hackathon on November 21, a unique day to explore quantum and AI prototyping, collaborate on Quandela and IBM platforms, and learn, share, and network in a stimulating environment at the heart of Quebec’s AI and quantum ecosystem.
This new initiative aims to strengthen connections between Mila’s research community, its partners, and AI experts across Quebec and Canada through in-person meetings and events focused on AI adoption in industry.
We use cookies to analyze the browsing and usage of our website and to personalize your experience. You can disable these technologies at any time, but this may limit certain functionalities of the site. Read our Privacy Policy for more information.
Setting cookies
You can enable and disable the types of cookies you wish to accept. However certain choices you make could affect the services offered on our sites (e.g. suggestions, personalised ads, etc.).
Essential cookies
These cookies are necessary for the operation of the site and cannot be deactivated. (Still active)
Analytics cookies
Do you accept the use of cookies to measure the audience of our sites?
Multimedia Player
Do you accept the use of cookies to display and allow you to watch the video content hosted by our partners (YouTube, etc.)?
Maricarmen Arenas
Alumni
Publications
Social Media as a Vector for Escort Ads:A Study on OnlyFans advertisements on Twitter
Online sex trafficking is on the rise and a majority of trafficking victims report being advertised online. The use of OnlyFans as a platfor… (see more)m 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.
2023-04-30
Proceedings of the 15th ACM Web Science Conference 2023 (published)