Exploring meta-features for classifying persuasion in texts of political memes
DOI:
https://doi.org/10.5753/reic.2026.6962Keywords:
meta-features, persuasion, meme classification, short text classification, machine learningAbstract
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 were performed using the SemEval-2024 Task 4 challenge dataset with 7,000 training and 1,000 testing instances. Random Forest and Logistic Regression were evaluated with and without handling the imbalance between classes in the training dataset. The best result, with a macro-F1 value of 0.701, was achieved by combining rhetorical and structural meta-features. The proposed approach offers a computationally efficient and interpretable alternative to deep learning models.
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