Semantic Search of Memes on Twitter
Abstract
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.
Cite
@article{arxiv.2002.01462,
title = {Semantic Search of Memes on Twitter},
author = {Jesus Perez-Martin and Benjamin Bustos and Magdalena Saldana},
journal= {arXiv preprint arXiv:2002.01462},
year = {2020}
}
Comments
Computational Methods Interest Group of the 70th International Communication Association Conference, May 2020 Virtual conference presentation link: https://player.vimeo.com/video/418320378