# ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining

arXiv: https://arxiv.org/abs/2209.06416 · HTML: https://mlsanity.com/paper/2209.06416/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 49 references that are in the map.
- First posted on arXiv 14 Sep 2022.

## Builds on (most cited first, 21 of 21)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [VQA: Visual Question Answering](https://mlsanity.com/paper/1505.00468/) (2015)
- [LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://mlsanity.com/paper/1908.07490/) (2019)
- [12-in-1: Multi-Task Vision and Language Representation Learning](https://mlsanity.com/paper/1912.02315/) (2019)
- [Self-Supervised Learning by Cross-Modal Audio-Video Clustering](https://mlsanity.com/paper/1911.12667/) (2019)
- [SoundNet: Learning Sound Representations from Unlabeled Video](https://mlsanity.com/paper/1610.09001/) (2016)
- [Learning Factorized Multimodal Representations](https://mlsanity.com/paper/1806.06176/) (2018)
- [Argumentation Mining in User-Generated Web Discourse](https://mlsanity.com/paper/1601.02403/) (2016)
- [Automatic Understanding of Image and Video Advertisements](https://mlsanity.com/paper/1707.03067/) (2017)
- [Stance and Sentiment in Tweets](https://mlsanity.com/paper/1605.01655/) (2016)
- [Cross-topic Argument Mining from Heterogeneous Sources Using Attention-based Neural Networks](https://mlsanity.com/paper/1802.05758/) (2018)
- [AMPERSAND: Argument Mining for PERSuAsive oNline Discussions](https://mlsanity.com/paper/2004.14677/) (2020)
- [Integrating Text and Image: Determining Multimodal Document Intent in Instagram Posts](https://mlsanity.com/paper/1904.09073/) (2019)
- [Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion](https://mlsanity.com/paper/1708.09085/) (2017)
- [Equal But Not The Same: Understanding the Implicit Relationship Between Persuasive Images and Text](https://mlsanity.com/paper/1807.08205/) (2018)
- [CITE: A Corpus of Image-Text Discourse Relations](https://mlsanity.com/paper/1904.06286/) (2019)
- [The evolution of argumentation mining: From models to social media and emerging tools](https://mlsanity.com/paper/1907.02258/) (2019)
- [Dense Multimodal Fusion for Hierarchically Joint Representation](https://mlsanity.com/paper/1810.03414/) (2018)
- [Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models](https://mlsanity.com/paper/2011.14293/) (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)
- [Towards a Perspectivist Turn in Argument Quality Assessment](https://mlsanity.com/paper/2502.14501/) (2025)
- [Overview of ImageArg-2023: The First Shared Task in Multimodal Argument Mining](https://mlsanity.com/paper/2310.12172/) (2023)
- [Argument Quality Assessment in the Age of Instruction-Following Large Language Models](https://mlsanity.com/paper/2403.16084/) (2024)
- [PVP: An Image Dataset for Personalized Visual Persuasion with Persuasion Strategies, Viewer Characteristics, and Persuasiveness Ratings](https://mlsanity.com/paper/2506.00481/) (2025)
- [TILFA: A Unified Framework for Text, Image, and Layout Fusion in Argument Mining](https://mlsanity.com/paper/2310.05210/) (2023)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [KNOWCOMP POKEMON Team at DialAM-2024: A Two-Stage Pipeline for Detecting Relations in Dialogical Argument Mining](https://mlsanity.com/paper/2407.19740/) (2024)
- [Argumentative Stance Prediction: An Exploratory Study on Multimodality and Few-Shot Learning](https://mlsanity.com/paper/2310.07093/) (2023)
- [Can MLLMs Reason About Visual Persuasion? Evaluating the Efficacy and Faithfulness of Reasoning](https://mlsanity.com/paper/2605.08965/) (2026)
- [Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks](https://mlsanity.com/paper/2608.01238/) (2026)
- [CogenPVG: Cognitive-Enhanced Reflective Multi-Agent Framework for Persuasive Video Generation](https://mlsanity.com/paper/2609.25821/) (2026)

## Abstract (arXiv)

The growing interest in developing corpora of persuasive texts has promoted applications in automated systems, e.g., debating and essay scoring systems; however, there is little prior work mining image persuasiveness from an argumentative perspective. To expand persuasiveness mining into a multi-modal realm, we present a multi-modal dataset, ImageArg, consisting of annotations of image persuasiveness in tweets. The annotations are based on a persuasion taxonomy we developed to explore image functionalities and the means of persuasion. We benchmark image persuasiveness tasks on ImageArg using widely-used multi-modal learning methods. The experimental results show that our dataset offers a useful resource for this rich and challenging topic, and there is ample room for modeling improvement.