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

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.

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