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The massive spread of visual content through the web and social media posesboth challenges and opportunities. Tracking visually-similar content is animportant task for studying and analyzing social phenomena related to thespread of such content. In this paper, we address this need by building adataset of social media images and evaluating visual near-duplicates retrievalmethods based on image retrieval and several advanced visual feature extractionmethods. We evaluate the methods using a large-scale dataset of images we crawlfrom social media and their manipulated versions we generated, presentingpromising results in terms of recall. We demonstrate the potential of thismethod in two case studies: one that shows the value of creating systemssupporting manual content review, and another that demonstrates the usefulnessof automatic large-scale data analysis.

Dataset and Case Studies for Visual Near-Duplicates Detection in the Context of Social Media