Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models
Meiqi Guo, Rebecca Hwa, Yu‐Ru Lin, Wen‐Ting Chung · 2020
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.