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

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