Warning: This paper contains memes that may be offensive to some readers. Multimodal Internet Memes are now a ubiquitous fixture in online discourse. One strand of meme-based research is the classification of memes according to various affects, such as sentiment and hate, supported by manually compiled meme datasets. Understanding the unique characteristics of memes is crucial for meme classification. Unlike other user-generated content, memes spread via memetics, i.e. the process by which memes are imitated and transformed into symbols used to create new memes. In effect, there exists an ever-evolving pool of visual and linguistic symbols that underpin meme culture and are crucial to interpreting the meaning of individual memes. The current approach of training supervised learning models on static datasets, without taking memetics into account, limits the depth and accuracy of meme interpretation. We argue that meme datasets must contain genuine memes, as defined via memetics, so that effective meme classifiers can be built. In this work, we develop a meme identification protocol which distinguishes meme from non-memetic content by recognising the memetics within it. We apply our protocol to random samplings of the leading 7 meme classification datasets and observe that more than half (50. 4\%) of the evaluated samples were found to contain no signs of memetics. Our work also provides a meme typology grounded in memetics, providing the basis for more effective approaches to the interpretation of memes and the creation of meme datasets.

翻译：警告：本文包含可能冒犯部分读者的网络迷因。多模态网络迷因已成为网络话语中无处不在的组成部分。当前基于迷因的研究分支之一，是在人工构建的迷因数据集支持下，根据情感、仇恨等多种情感维度对迷因进行分类。理解迷因的独特属性对于迷因分类至关重要。与其他用户生成内容不同，迷因通过迷因学机制传播，即通过模仿现有迷因并将其转化为符号以创造新迷因的过程。实际上，存在一个不断演变的视觉与语言符号库，它们构成了迷因文化的基石，对解读单个迷因的含义至关重要。当前在静态数据集上训练监督学习模型的方法未考虑迷因学特性，这限制了迷因解读的深度与准确性。我们认为，迷因数据集必须包含符合迷因学定义的真实迷因，才能构建有效的迷因分类器。本研究开发了一套迷因识别协议，通过识别内容中的迷因学特征来区分迷因与非迷因内容。我们将该协议应用于7个主流迷因分类数据集的随机抽样，发现超过半数（50.4%）的评估样本未呈现任何迷因学特征。本研究还提出了一套基于迷因学的迷因类型学框架，为更有效的迷因解读方法及迷因数据集构建奠定了基础。