Internet memes have emerged as a novel format for communication and expressing ideas on the web. Their fluidity and creative nature are reflected in their widespread use, often across platforms and occasionally for unethical or harmful purposes. While computational work has already analyzed their high-level virality over time and developed specialized classifiers for hate speech detection, there have been no efforts to date that aim to holistically track, identify, and map internet memes posted on social media. To bridge this gap, we investigate whether internet memes across social media platforms can be contextualized by using a semantic repository of knowledge, namely, a knowledge graph. We collect thousands of potential internet meme posts from two social media platforms, namely Reddit and Discord, and develop an extract-transform-load procedure to create a data lake with candidate meme posts. By using vision transformer-based similarity, we match these candidates against the memes cataloged in IMKG -- a recently released knowledge graph of internet memes. We leverage this grounding to highlight the potential of our proposed framework to study the prevalence of memes on different platforms, map them to IMKG, and provide context about memes on social media.

翻译：互联网模因已成为网络上一种新颖的交流与思想表达形式。其流动性与创造性特质体现在跨平台的广泛传播中，有时也被用于不道德或有害目的。尽管已有计算工作分析其随时间演变的高层次病毒式传播特性，并开发出针对仇恨言论检测的专用分类器，但迄今尚无研究致力于全面追踪、识别并映射社交媒体上发布的互联网模因。为填补这一空白，我们探究是否可通过语义知识库（即知识图谱）对不同社交媒体平台上的互联网模因进行情境化处理。我们从Reddit与Discord两个社交媒体平台收集数千条潜在的互联网模因帖子，并开发一套提取-转换-加载流程，构建包含候选模因帖子的数据湖。通过基于视觉Transformer的相似度计算，我们将这些候选模因与IMKG（近期发布的互联网模因知识图谱）中编录的模因进行匹配。借助这一基准映射，我们凸显了所提出框架在以下方面的潜力：研究模因在不同平台上的流行度、将其映射至IMKG，并为社交媒体上的模因提供情境化解释。