Elijah Bouma-Sims, Bradley Reaves (North Carolina State University)

YouTube has become the second most popular website according to Alexa, and it represents an enticing platform for scammers to attract victims. Because of the computational difficulty of classifying multimedia, identifying scams on YouTube is more difficult than text-based media. As a consequence, the research community to-date has provided little insight into the prevalence, lifetime, and operational patterns of scammers on YouTube. In this short paper, we present a preliminary exploration of scam videos on YouTube. We begin by identifying 74 search queries likely to lead to scam videos based on the authors’ experience seeing scams during routine browsing. We then manually review and characterize the results to identify 668 scams in 3,700 videos. In a detailed analysis of our classifications and metadata, we find that these scam videos have a median lifetime of nearly nine months, and many rely on external websites for monetization. We also explore the potential of detecting scams from metadata alone, finding that metadata does not have enough predictive power to distinguish scams from legitimate videos. Our work demonstrates that scams are a real problem for YouTube users, motivating future work on this topic.

View More Papers

Practical Blind Membership Inference Attack via Differential Comparisons

Bo Hui (The Johns Hopkins University), Yuchen Yang (The Johns Hopkins University), Haolin Yuan (The Johns Hopkins University), Philippe Burlina...

Read More

Characterizing the Adoption of Security.txt Files and their Applications...

William Findlay (Carleton University) and AbdelRahman Abdou (Carleton University)

Read More

Exploring The Design Space of Sharing and Privacy Mechanisms...

Abdulmajeed Alqhatani, Heather R. Lipford (University of North Carolina at Charlotte)

Read More

From WHOIS to WHOWAS: A Large-Scale Measurement Study of...

Chaoyi Lu (Tsinghua University; Beijing National Research Center for Information Science and Technology), Baojun Liu (Tsinghua University; Beijing National Research...

Read More