Semantic Search of Memes on Twitter
This addresses the need for efficient meme analysis tools for social media researchers, though it is incremental in nature.
The paper tackles the problem of automatically classifying and retrieving memes from large social media datasets, proposing and comparing several methods, with experimental evaluation on a dataset from Twitter users in Chile showing effectiveness but room for improvement.
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.