# Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models

arXiv: https://arxiv.org/abs/2011.14293 · HTML: https://mlsanity.com/paper/2011.14293/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Nov 2020.

## Built on by (most cited first, 3 of 3)

- [Faster Adaptive Federated Learning](https://mlsanity.com/paper/2212.00974/) (2022)
- [ImageArg: A Multi-modal Tweet Dataset for Image Persuasiveness Mining](https://mlsanity.com/paper/2209.06416/) (2022)
- [Evaluating VLMs on Multimodal Aristotelian Persuasion Tasks](https://mlsanity.com/paper/2608.01238/) (2026)

## Abstract (arXiv)

We investigate the impact of political ideology biases in training data. Through a set of comparison studies, we examine the propagation of biases in several widely-used NLP models and its effect on the overall retrieval accuracy. Our work highlights the susceptibility of large, complex models to propagating the biases from human-selected input, which may lead to a deterioration of retrieval accuracy, and the importance of controlling for these biases. Finally, as a way to mitigate the bias, we propose to learn a text representation that is invariant to political ideology while still judging topic relevance.