  Memes are becoming a useful source of data for analyzing behavior on social
media. However, a problem to tackle is how to correctly identify a meme. As the
number of memes published every day on social media is huge, there is a need
for automatic methods for classifying and searching in large meme datasets.
This paper proposes and compares several methods for automatically classifying
images as memes. Also, we propose a method that allows us to implement a system
for retrieving memes from a dataset using a textual query. We experimentally
evaluate the methods using a large dataset of memes collected from Twitter
users in Chile, which was annotated by a group of experts. Though some of the
evaluated methods are effective, there is still room for improvement.
