# TotalDefMeme: A Multi-Attribute Meme dataset on Total Defence in Singapore

arXiv: https://arxiv.org/abs/2305.17911 · HTML: https://mlsanity.com/paper/2305.17911/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 24 references that are in the map.
- First posted on arXiv 29 May 2023.

## Builds on (most cited first, 16 of 16)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [VisualBERT: A Simple and Performant Baseline for Vision and Language](https://mlsanity.com/paper/1908.03557/) (2019)
- [The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes](https://mlsanity.com/paper/2005.04790/) (2020)
- [SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!](https://mlsanity.com/paper/2008.03781/) (2020)
- [Detecting Harmful Memes and Their Targets](https://mlsanity.com/paper/2110.00413/) (2021)
- [Prompting for Multimodal Hateful Meme Classification](https://mlsanity.com/paper/2302.04156/) (2023)
- [On the Origins of Memes by Means of Fringe Web Communities](https://mlsanity.com/paper/1805.12512/) (2018)
- [Disentangling Hate in Online Memes](https://mlsanity.com/paper/2108.06207/) (2021)
- [On Explaining Multimodal Hateful Meme Detection Models](https://mlsanity.com/paper/2204.01734/) (2022)
- [DisinfoMeme: A Multimodal Dataset for Detecting Meme Intentionally Spreading Out Disinformation](https://mlsanity.com/paper/2205.12617/) (2022)
- [MemeSequencer: Sparse Matching for Embedding Image Macros](https://mlsanity.com/paper/1802.04936/) (2018)
- [Exercise? I thought you said 'Extra Fries': Leveraging Sentence Demarcations and Multi-hop Attention for Meme Affect Analysis](https://mlsanity.com/paper/2103.12377/) (2021)
- [Clustering Memes in Social Media](https://mlsanity.com/paper/1310.2665/) (2013)
- [Clustering memes in social media streams](https://mlsanity.com/paper/1411.0652/) (2014)

## Built on by (most cited first, 12 of 12)

- [Improving Hateful Meme Detection through Retrieval-Guided Contrastive Learning](https://mlsanity.com/paper/2311.08110/) (2023)
- [MemeMQA: Multimodal Question Answering for Memes via Rationale-Based Inferencing](https://mlsanity.com/paper/2405.11215/) (2024)
- [Robust Adaptation of Large Multimodal Models for Retrieval Augmented Hateful Meme Detection](https://mlsanity.com/paper/2502.13061/) (2025)
- [Crowdsource, Crawl, or Generate? Creating SEA-VL, a Multicultural Vision-Language Dataset for Southeast Asia](https://mlsanity.com/paper/2503.07920/) (2025)
- [ExPO-HM: Learning to Explain-then-Detect for Hateful Meme Detection](https://mlsanity.com/paper/2510.08630/) (2025)
- [PromptMTopic: Unsupervised Multimodal Topic Modeling of Memes using Large Language Models](https://mlsanity.com/paper/2312.06093/) (2023)
- [LionGuard: Building a Contextualized Moderation Classifier to Tackle Localized Unsafe Content](https://mlsanity.com/paper/2407.10995/) (2024)
- [Humor in Pixels: Benchmarking Large Multimodal Models Understanding of Online Comics](https://mlsanity.com/paper/2509.12248/) (2025)
- [SAFE-MEME: Structured Reasoning Framework for Robust Hate Speech Detection in Memes](https://mlsanity.com/paper/2412.20541/) (2024)
- [What Makes a Meme a Meme? Identifying Memes for Memetics-Aware Dataset Creation](https://mlsanity.com/paper/2407.11861/) (2024)
- [MATK: The Meme Analytical Tool Kit](https://mlsanity.com/paper/2312.06094/) (2023)
- [OSPC: Artificial VLM Features for Hateful Meme Detection](https://mlsanity.com/paper/2407.12836/) (2024)

## Abstract (arXiv)

Total Defence is a defence policy combining and extending the concept of military defence and civil defence. While several countries have adopted total defence as their defence policy, very few studies have investigated its effectiveness. With the rapid proliferation of social media and digitalisation, many social studies have been focused on investigating policy effectiveness through specially curated surveys and questionnaires either through digital media or traditional forms. However, such references may not truly reflect the underlying sentiments about the target policies or initiatives of interest. People are more likely to express their sentiment using communication mediums such as starting topic thread on forums or sharing memes on social media. Using Singapore as a case reference, this study aims to address this research gap by proposing TotalDefMeme, a large-scale multi-modal and multi-attribute meme dataset that captures public sentiments toward Singapore's Total Defence policy. Besides supporting social informatics and public policy analysis of the Total Defence policy, TotalDefMeme can also support many downstream multi-modal machine learning tasks, such as aspect-based stance classification and multi-modal meme clustering. We perform baseline machine learning experiments on TotalDefMeme and evaluate its technical validity, and present possible future interdisciplinary research directions and application scenarios using the dataset as a baseline.