# Predicting the Politics of an Image Using Webly Supervised Data

arXiv: https://arxiv.org/abs/1911.00147 · HTML: https://mlsanity.com/paper/1911.00147/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 31 Oct 2019.

## Built on by (most cited first, 10 of 10)

- [A Survey on Bias in Visual Datasets](https://mlsanity.com/paper/2107.07919/) (2021)
- [Ethics and Creativity in Computer Vision](https://mlsanity.com/paper/2112.03111/) (2021)
- [Exploring Visual Engagement Signals for Representation Learning](https://mlsanity.com/paper/2104.07767/) (2021)
- [Seeing the Intangible: Survey of Image Classification into High-Level and Abstract Categories](https://mlsanity.com/paper/2308.10562/) (2023)
- [The ethical ambiguity of AI data enrichment: Measuring gaps in research ethics norms and practices](https://mlsanity.com/paper/2306.01800/) (2023)
- [Looking Beyond the Obvious: A Survey on Abstract Concept Recognition for Video Understanding](https://mlsanity.com/paper/2508.20765/) (2025)
- [Visual Persuasion in COVID-19 Social Media Content: A Multi-Modal Characterization](https://mlsanity.com/paper/2112.13910/) (2021)
- [Late Fusion with Triplet Margin Objective for Multimodal Ideology Prediction and Analysis](https://mlsanity.com/paper/2211.02269/) (2022)
- [Structuring Quantitative Image Analysis with Object Prominence](https://mlsanity.com/paper/2409.00216/) (2024)
- [Multimodal Political Bias Identification and Neutralization](https://mlsanity.com/paper/2506.17372/) (2025)

## Abstract (arXiv)

The news media shape public opinion, and often, the visual bias they contain is evident for human observers. This bias can be inferred from how different media sources portray different subjects or topics. In this paper, we model visual political bias in contemporary media sources at scale, using webly supervised data. We collect a dataset of over one million unique images and associated news articles from left- and right-leaning news sources, and develop a method to predict the image's political leaning. This problem is particularly challenging because of the enormous intra-class visual and semantic diversity of our data. We propose a two-stage method to tackle this problem. In the first stage, the model is forced to learn relevant visual concepts that, when joined with document embeddings computed from articles paired with the images, enable the model to predict bias. In the second stage, we remove the requirement of the text domain and train a visual classifier from the features of the former model. We show this two-stage approach facilitates learning and outperforms several strong baselines. We also present extensive qualitative results demonstrating the nuances of the data.