MemeSequencer: Sparse Matching for Embedding Image Macros
Name
p1225-dubey.pdf
Description
Published version
Size
4.4 MB
Format
Adobe PDF
Checksum (MD5)
0d1dbe042b5d2359c55750b1f0be613c
Author(s)
Dubey, Abhimanyu
Moro, Esteban
Cebrian, Manuel
Rahwan, Iyad
Date Issued
2018
Publisher
ACM Press
Citation
Dubey, Abhimanyu, Moro, Esteban, Cebrian, Manuel and Rahwan, Iyad. 2018. "MemeSequencer: Sparse Matching for Embedding Image Macros."
Version
Final published version
Abstract
The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or ‘memes’. However, a pitfall in proceeding with such an investigation is the current incapability to produce a robust semantic space for such imagery, capable of understanding differences in Image Macros. In this study, we provide a first step in the systematic study of image evolution on the Internet, by proposing an algorithm based on sparse representations and deep learning to decouple various types of content in such images and produce a rich semantic embedding. We demonstrate the benefits of our approach on a variety of tasks pertaining to memes and Image Macros, such as image clustering, image retrieval, topic prediction and virality prediction, surpassing the existing methods on each.In addition to its utility on quantitative tasks, our method opens up the possibility of obtaining the first large-scale understanding of the evolution and propagation of memetic imagery.
MIT Department
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1145/3178876.3186021
