  Memes are one of the most popular types of content used in an online
disinformation campaign. They are primarily effective on social media platforms
since they can easily reach many users. Memes in a disinformation campaign
achieve their goal of influencing the users through several rhetorical and
psychological techniques, such as causal oversimplification, name-calling, and
smear. The SemEval 2024 Task 4 \textit{Multilingual Detection of Persuasion
Technique in Memes} on identifying such techniques in the memes is divided
across three sub-tasks: ($\mathbf{1}$) Hierarchical multi-label classification
using only textual content of the meme, ($\mathbf{2}$) Hierarchical multi-label
classification using both, textual and visual content of the meme and
($\mathbf{3}$) Binary classification of whether the meme contains a persuasion
technique or not using it's textual and visual content. This paper proposes an
ensemble of Class Definition Prediction (CDP) and hyperbolic embeddings-based
approaches for this task. We enhance meme classification accuracy and
comprehensiveness by integrating HypEmo's hierarchical label embeddings (Chen
et al., 2023) and a multi-task learning framework for emotion prediction. We
achieve a hierarchical F1-score of 0.60, 0.67, and 0.48 on the respective
sub-tasks.
