2020/02/04 by Jesus Perez-Martin, Perez-Martin, Jesus, Benjamin Bustos +5 · 1 citation
Computer Science · Physics and Astronomy · Social Sciences · #Complex Network Analysis Techniques #Computation and Language (cs.CL) #Computer science #Data science #FOS: Computer and information sciences #Information retrieval #Misinformation and Its Impacts #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Social media #World Wide Web #cs.CL #cs.SI
paper · pdf · doi:10.48550/arxiv.2002.01462
published in arXiv (Cornell University) (Cornell University) · Computational Methods Interest Group of the 70th International Communication Association Conference, May 2020 Virtual conference presentation link: https://player.vimeo.com/video/418320378
openalex publication_date 2020/02/04 · arxiv created 2020/05/20 · arxiv updated 2020/05/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.