Persuasive visuals play a central role in advertising, public communication, and online media, making it increasingly important to understand whether an image is persuasive and why. However, current Multimodal Large Language Models (MLLMs) have limited ability to reason about visual persuasion. Our analysis reveals that models often rely on a shortcut---selectively citing easily recognizable visual elements and treating their mere presence as evidence of persuasiveness---rather than reasoning over the diverse visual cues in an image to determine how and why they contribute to persuasiveness. To address this limitation, we propose (1) a fine-tuning approach that trains models on rationales reflecting diverse perspectives on visual persuasiveness, and (2) an evaluation framework that measures the faithfulness of models' rationales through three complementary metrics. Fine-tuning on multi-perspective rationales improves persuasiveness prediction and reasoning effectiveness. However, our evaluation framework reveals a discrepancy between prediction performance and rationale faithfulness, showing that higher prediction performance does not necessarily correspond to more faithful reasoning. These findings highlight the need to evaluate effectiveness and faithfulness separately and inform future directions for improving both the training and evaluation of visual persuasion reasoning.

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