Analyzing memes on the internet has emerged as a crucial endeavor due to the impact this multi-modal form of content wields in shaping online discourse. Memes have become a powerful tool for expressing emotions and sentiments, possibly even spreading hate and misinformation, through humor and sarcasm. In this paper, we present the overview of the Memotion 3 shared task, as part of the DeFactify 2 workshop at AAAI-23. The task released an annotated dataset of Hindi-English code-mixed memes based on their Sentiment (Task A), Emotion (Task B), and Emotion intensity (Task C). Each of these is defined as an individual task and the participants are ranked separately for each task. Over 50 teams registered for the shared task and 5 made final submissions to the test set of the Memotion 3 dataset. CLIP, BERT modifications, ViT etc. were the most popular models among the participants along with approaches such as Student-Teacher model, Fusion, and Ensembling. The best final F1 score for Task A is 34.41, Task B is 79.77 and Task C is 59.82.

翻译：互联网模因作为一种多模态内容形式，对在线话语的塑造具有重要影响，因此分析网络模因已成为一项关键任务。模因已成为通过幽默和讽刺表达情感与情绪，甚至传播仇恨和虚假信息的强大工具。本文介绍了作为AAAI-23 DeFactify 2研讨会一部分的Memotion 3共享任务的综述。该任务发布了一个基于情感（任务A）、情绪（任务B）和情绪强度（任务C）进行标注的印地语-英语混合编码模因数据集。每项任务均独立定义，参与者分别按各任务进行排名。超过50支团队注册了该共享任务，其中5支团队向Memotion 3数据集的测试集提交了最终结果。CLIP、BERT变体、ViT等模型以及师生模型、融合和集成方法成为参与者中最流行的技术方案。任务A的最佳最终F1分数为34.41，任务B为79.77，任务C为59.82。