An Evaluation of Meta-Features for Automated Detection of Persuasion in Texts of Political Memes - Azevedo, Ana B. S. de; Gonçalves, Eduardo C. (Anais do Symposium on Knowledge Discovery, Mining and Learning (KDMiLe); 2025: Anais do XIII Symposium on Knowledge Discovery, Mining and LearningDO - 10.5753/kdmile.2025.247776, 2025)
This work proposes a feature-engineering-based approach for detecting persuasive strategies in political memes. Four groups of meta-features were designed: (i) rhetorical, (ii) sentiment and hate speech, (iii) structural, and (iv) contextual features. Experiments used the SemEval-2024 Task 4 dataset with 7,000 training and 1,000 testing instances. Random Forest and Logistic Regression were evaluated with and without SMOTE to handle class imbalance present on the training dataset. The best result, with a macro-F1 value of 0.698, was achieved by combining rhetorical and structural features. The proposed approach offers a computationally efficient and interpretable alternative to neural-based models.