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.

翻译：全面防御是一种结合并扩展军事防御与民防概念的国防政策。尽管多个国家已采用全面防御作为其国防政策，但关于其有效性的研究极少。随着社交媒体和数字化的快速普及，许多社会科学研究通过数字媒体或传统形式精心设计的调查问卷来评估政策有效性。然而，此类参考可能无法真实反映目标政策或关注举措的潜在情绪。人们更倾向于通过论坛发起话题讨论或在社交媒体分享模因等沟通媒介表达情感。本研究以新加坡为案例，提出大规模多模态、多属性的模因数据集TotalDefMeme，以弥补这一研究空白，捕捉公众对新加坡全面防御政策的情感倾向。除支持全面防御政策的社会信息学与公共政策分析外，TotalDefMeme还可支撑诸多下游多模态机器学习任务，如基于方面的立场分类与多模态模因聚类。我们基于TotalDefMeme开展基线机器学习实验以验证其技术有效性，并探讨以该数据集为基线的未来跨学科研究方向与应用场景。