  The massive spread of visual content through the web and social media poses
both challenges and opportunities. Tracking visually-similar content is an
important task for studying and analyzing social phenomena related to the
spread of such content. In this paper, we address this need by building a
dataset of social media images and evaluating visual near-duplicates retrieval
methods based on image retrieval and several advanced visual feature extraction
methods. We evaluate the methods using a large-scale dataset of images we crawl
from social media and their manipulated versions we generated, presenting
promising results in terms of recall. We demonstrate the potential of this
method in two case studies: one that shows the value of creating systems
supporting manual content review, and another that demonstrates the usefulness
of automatic large-scale data analysis.
