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The ever-increasing amount of user-generated content online has led, inrecent years, to an expansion in research and investment in automated contentanalysis tools. Scrutiny of automated content analysis has accelerated duringthe COVID-19 pandemic, as social networking services have placed a greaterreliance on these tools due to concerns about health risks to their moderationstaff from in-person work. At the same time, there are important policy debatesaround the world about how to improve content moderation while protecting freeexpression and privacy. In order to advance these debates, we need tounderstand the potential role of automated content analysis tools.  This paper explains the capabilities and limitations of tools for analyzingonline multimedia content and highlights the potential risks of using thesetools at scale without accounting for their limitations. It focuses on two maincategories of tools: matching models and computer prediction models. Matchingmodels include cryptographic and perceptual hashing, which compareuser-generated content with existing and known content. Predictive models(including computer vision and computer audition) are machine learningtechniques that aim to identify characteristics of new or previously unknowncontent.

Do You See What I See? Capabilities and Limits of Automated Multimedia Content Analysis