A First Look at Scams on YouTube

TL;DR

This paper provides an initial exploration of YouTube scams, identifying their characteristics, longevity, and the challenges in detecting them using metadata alone, highlighting the need for further research.

Contribution

It offers a preliminary analysis of scam videos on YouTube, including identification, characterization, and the limitations of metadata-based detection methods.

Findings

Scam videos have a median lifetime of nearly nine months.

Many scams rely on external websites for monetization.

Metadata alone is insufficient to reliably detect scams.

Abstract

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.