metadata
license: unknown
language:
- eng
pretty_name: Total Defense Meme
task_categories:
- optical-character-recognition
- image-classification-multilabel
tags:
- optical-character-recognition
- image-classification-multilabel
This is a large-scale multimodal and multi-attribute dataset containing memes about Singapore's Total Defence policy from different social media platforms. The type (Singaporean or generic), pillars (military, civil, economic, social, psychological, digital, others), topics and stances (against, neutral, supportive) of each meme are manually identified by annotators.
Languages
eng
Supported Tasks
Optical Character Recognition, Image Classification Multilabel
Dataset Usage
Using datasets library
from datasets import load_dataset
dset = datasets.load_dataset("SEACrowd/total_defense_meme", trust_remote_code=True)
Using seacrowd library
# Load the dataset using the default config
dset = sc.load_dataset("total_defense_meme", schema="seacrowd")
# Check all available subsets (config names) of the dataset
print(sc.available_config_names("total_defense_meme"))
# Load the dataset using a specific config
dset = sc.load_dataset_by_config_name(config_name="<config_name>")
More details on how to load the seacrowd library can be found here.
Dataset Homepage
https://gitlab.com/bottle_shop/meme/TotalDefMemes
Dataset Version
Source: 1.0.0. SEACrowd: 2024.06.20.
Dataset License
Unknown (unknown)
Citation
If you are using the Total Defense Meme dataloader in your work, please cite the following:
@inproceedings{10.1145/3587819.3592545,
author = {Prakash, Nirmalendu and Hee, Ming Shan and Lee, Roy Ka-Wei},
title = {TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore},
year = {2023},
isbn = {9798400701481},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3587819.3592545},
doi = {10.1145/3587819.3592545},
booktitle = {Proceedings of the 14th Conference on ACM Multimedia Systems},
pages = {369–375},
numpages = {7},
keywords = {multimodal, meme, dataset, topic clustering, stance classification},
location = {Vancouver, BC, Canada},
series = {MMSys '23}
}
@article{lovenia2024seacrowd,
title={SEACrowd: A Multilingual Multimodal Data Hub and Benchmark Suite for Southeast Asian Languages},
author={Holy Lovenia and Rahmad Mahendra and Salsabil Maulana Akbar and Lester James V. Miranda and Jennifer Santoso and Elyanah Aco and Akhdan Fadhilah and Jonibek Mansurov and Joseph Marvin Imperial and Onno P. Kampman and Joel Ruben Antony Moniz and Muhammad Ravi Shulthan Habibi and Frederikus Hudi and Railey Montalan and Ryan Ignatius and Joanito Agili Lopo and William Nixon and Börje F. Karlsson and James Jaya and Ryandito Diandaru and Yuze Gao and Patrick Amadeus and Bin Wang and Jan Christian Blaise Cruz and Chenxi Whitehouse and Ivan Halim Parmonangan and Maria Khelli and Wenyu Zhang and Lucky Susanto and Reynard Adha Ryanda and Sonny Lazuardi Hermawan and Dan John Velasco and Muhammad Dehan Al Kautsar and Willy Fitra Hendria and Yasmin Moslem and Noah Flynn and Muhammad Farid Adilazuarda and Haochen Li and Johanes Lee and R. Damanhuri and Shuo Sun and Muhammad Reza Qorib and Amirbek Djanibekov and Wei Qi Leong and Quyet V. Do and Niklas Muennighoff and Tanrada Pansuwan and Ilham Firdausi Putra and Yan Xu and Ngee Chia Tai and Ayu Purwarianti and Sebastian Ruder and William Tjhi and Peerat Limkonchotiwat and Alham Fikri Aji and Sedrick Keh and Genta Indra Winata and Ruochen Zhang and Fajri Koto and Zheng-Xin Yong and Samuel Cahyawijaya},
year={2024},
eprint={2406.10118},
journal={arXiv preprint arXiv: 2406.10118}
}