Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
12-2023
Abstract
Memes are widely used to convey cultural and societal issues and have a significant impact on public opinion. However, little work has been done on understanding and explaining the semantics expressed in multimodal memes. To fill this research gap, we introduce MERMAID, a dataset consisting of 3,633 memes annotated with their entities and relations, and propose a novel MERF pipeline that extracts entities and their relationships in memes. Our framework combines state-of-the-art techniques from natural language processing and computer vision to extract text and image features and infer relationships between entities in memes. We evaluate the proposed framework on a real-world meme dataset and establish the benchmark for the new multimodal meme semantic understanding task. Our evaluation also includes a low-resource setting, where we assess the applicability of our framework to low-resource settings, which is a common problem due to the high cost and lack of labeled data for relations in memes. Overall, our work contributes to the understanding of the semantics of memes, a crucial form of communication in today's society.
Keywords
Memes, Multimodal, Semantic Extraction
Discipline
Databases and Information Systems | Graphics and Human Computer Interfaces | Mental and Social Health
Publication
2023 IEEE International Conference on Big Data: Sorrento, Italy, December 15-18: Proceedings
First Page
433
Last Page
442
ISBN
9798350324457
Identifier
10.1109/BigData59044.2023.10386279
Publisher
IEEE
City or Country
Piscataway, NJ
Citation
TOH, Shaun; KUEK, Adriel; CHONG, Wen Haw; and LEE, Roy Ka Wei.
MERMAID: A dataset and framework for multimodal meme semantic understanding. (2023). 2023 IEEE International Conference on Big Data: Sorrento, Italy, December 15-18: Proceedings. 433-442.
Available at: https://ink.library.smu.edu.sg/sis_research/8746
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1109/BigData59044.2023.1038627
Included in
Databases and Information Systems Commons, Graphics and Human Computer Interfaces Commons, Mental and Social Health Commons