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Showing posts with the label Coyote

2024-12-30: Unveiling Coyote Ads: Detecting Human Smuggling Advertisements on Social Media

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Figure 1:  An example of a Coyote ad on social media in Spanish. (Figure 1 in  Kodandaram et al. )   Introduction Social media has revolutionized how information flows, connecting people across the globe at an unprecedented scale. However, this same connectivity has made these platforms fertile ground for illicit activities, including human smuggling. This issue, though not immediately visible to most people, poses a severe humanitarian crisis. We explore in our paper published at the 35th ACM Conference on Hypertext and Social Media 2024 titled " Unveiling Coyote Ads: Detecting Human Smuggling Advertisements on Social Media, " how traffickers exploit these platforms to lure vulnerable individuals, particularly those seeking better opportunities or fleeing dangerous situations. The study focuses on identifying and analyzing “ coyote ads, ” which are advertisements posted by human smugglers. Our goal was clear: to develop an effective detection mechanism that exposes thes...

2024-10-18: ACM SIGWEB conference on Hypertext and Social Media (HT) 2024 - Poznań, Poland Trip Report

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Presenting research on the deceptive practices of coyote advertisements in illegal migration I had the pleasure of attending and presenting at the ACM Hypertext 2024 conference in PoznaÅ„, Poland. The conference, known for its focus on the study of hypertext and web communities, provided an exciting platform for researchers to share innovative ideas. The event was filled with insightful discussions, networking opportunities, and, of course, the presentation of cutting-edge papers. ACM Hypertext '24 is almost here. Will you be there? #HT2024 @ACMHT pic.twitter.com/eJbNeDYEeI — Frode A. Hegland (@liquidizer) September 8, 2024 One of the highlights for me was presenting “ Unveiling Coyote Ads: Detecting Human Smuggling Advertisements on Social Media ”, for which I am a co-author. Our paper explores the detection of human smuggling advertisements using social media platforms. The research delves into machine learning techniques to identify these harmful ads, presenting a novel ...