# M2P2: Multimodal Persuasion Prediction using Adaptive Fusion

arXiv: https://arxiv.org/abs/2006.11405 · HTML: https://mlsanity.com/paper/2006.11405/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2020.

## Built on by (most cited first, 12 of 12)

- [Must Read: A Comprehensive Survey of Computational Persuasion](https://mlsanity.com/paper/2505.07775/) (2025)
- [Analyzing Online Political Advertisements](https://mlsanity.com/paper/2105.04047/) (2021)
- [SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities](https://mlsanity.com/paper/2205.00302/) (2022)
- [A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot](https://mlsanity.com/paper/2305.09758/) (2023)
- [Communication is All You Need: Persuasion Dataset Construction via Multi-LLM Communication](https://mlsanity.com/paper/2502.08896/) (2025)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [Persuasion Strategies in Advertisements](https://mlsanity.com/paper/2208.09626/) (2022)
- [Unimodal Face Classification with Multimodal Training](https://mlsanity.com/paper/2112.04182/) (2021)
- [Mixed Signals: Understanding Model Disagreement in Multimodal Empathy Detection](https://mlsanity.com/paper/2505.13979/) (2025)
- [M-PACE: Mother Child Framework for Multimodal Compliance](https://mlsanity.com/paper/2509.15241/) (2025)
- [Towards High-Level Semantic Intelligence](https://mlsanity.com/paper/2607.24082/) (2026)
- [CogenPVG: Cognitive-Enhanced Reflective Multi-Agent Framework for Persuasive Video Generation](https://mlsanity.com/paper/2609.25821/) (2026)

## Abstract (arXiv)

Identifying persuasive speakers in an adversarial environment is a critical task. In a national election, politicians would like to have persuasive speakers campaign on their behalf. When a company faces adverse publicity, they would like to engage persuasive advocates for their position in the presence of adversaries who are critical of them. Debates represent a common platform for these forms of adversarial persuasion. This paper solves two problems: the Debate Outcome Prediction (DOP) problem predicts who wins a debate while the Intensity of Persuasion Prediction (IPP) problem predicts the change in the number of votes before and after a speaker speaks. Though DOP has been previously studied, we are the first to study IPP. Past studies on DOP fail to leverage two important aspects of multimodal data: 1) multiple modalities are often semantically aligned, and 2) different modalities may provide diverse information for prediction. Our M2P2 (Multimodal Persuasion Prediction) framework is the first to use multimodal (acoustic, visual, language) data to solve the IPP problem. To leverage the alignment of different modalities while maintaining the diversity of the cues they provide, M2P2 devises a novel adaptive fusion learning framework which fuses embeddings obtained from two modules -- an alignment module that extracts shared information between modalities and a heterogeneity module that learns the weights of different modalities with guidance from three separately trained unimodal reference models. We test M2P2 on the popular IQ2US dataset designed for DOP. We also introduce a new dataset called QPS (from Qipashuo, a popular Chinese debate TV show ) for IPP. M2P2 significantly outperforms 4 recent baselines on both datasets.