2026 EACL EACL 2026

Predicting Convincingness in Political Speech: How Emotional Tone Shapes Persuasive Strength

Abstract

AbstractEmotional tone plays a central role in persuasion, yet its impact on computational assessments of political argument quality in real world election campaign speeches remains understudied. In this work, we investigate whether positive emotional framing correlates with higher perceived convincingness in political arguments. We fine-tune language models on argument quality datasets and test their ability to transfer convincingness predictions to real-world campaign speeches. Using a corpus of U.S. presidential campaign speeches, we analyze emotional polarity in relation to predicted persuasive strength to test whether positively framed arguments are judged more convincing than neutral or negative ones. Our empirical analysis shows that political parties rely heavily on argumentation during their election campaigns. Also, we found the evidence that politicians strategically employ emotional cues within their arguments during these campaign speeches, with positive emotions being more strongly associated with persuasive strength, for example in topics such as USMCA’s Effect on American Jobs and Agriculture, Border Control Policies, Progressive Tax Reforms. At the same time, we find that negative emotions have a weaker yet still non-negligible influence on voter persuasion in topics such as City Crime and Civil Unrest and White Supremacist Violence (Charlottesville Incident).

🧭 Keyword Pioneer — persuasive strength
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Natural Language Processing, Speech & Audio