Inflating Topic Relevance with Ideology: A Case Study of Political Ideology Bias in Social Topic Detection Models (2020)

Abstract: 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.

## introduction due to the extensive reaches of its network and the breadth of information enmeshed in it, social media has become an invaluable data source for empirical studies in the social sciences. yet, identifying all and only relevant information out of a vast data stream remains an untamed challenge. while topic detection methods may help researchers extract some relevant text about a topic of interest (e.g., immigration policies), they may miss other equally relevant text while including some irrelevant ones. crucially, because most topic detection methods are trained, they may unintentionally contain or propagate certain biases (e.g., extracting more instances written by women where gender balance is expected), resulting in a skewed data collection that may lead social scientists to draw incorrect conclusions. this paper explores the interactions between social biases and automatic topic detection models, and their impact on the resulting data collection. our goal is to help social scientists gain insights about biases in text analytic so as to mitigate such biases in their data collections. more specifically, we examine the role of political ideology biases (liberal-leaning, denoted as blue, and conservative-leaning, denoted as red) in the process of collecting data about certain social topics (immigration and gun control) from twitter. we observe that biases may be introduced at three major junctures of the data collection pipeline. first, it may be introduced in the data source itself (e.g., certain forums may have strong political leanings), but social scientists typically choose their data intentionally and are aware of pre-existing biases therein @xcite @xcite . second, biases may be introduced in the way in which "topic relevance" is defined. for example, domain experts may be consulted to identify a set of keywords or sample instances that are indicative or representative of the topic of interest. thus, any unconscious bias on the part of the domain experts would be encoded into these keywords and examples @xcite , which would then serve as a noisy training corpus for developing a topic classifier. third, the choice of the computational models for performing relevance classification may amplify or mitigate the impact of the biases. through a suite of empirical analyses, this work studies the effect of biased keywords (blue-leaning, red-leaning) on downstream training and retrieval: 1) to what extent does a trained classifier propagate the bias seen in the training data? can it learn to generalize and blunt some of the bias? 2) to what extent do biases in the training corpus degrade the overall retrieval ability of the classifier? specifically, we generate strongly blue-leaning and red-leaning noisy training sets, and we compare the impact of these training sets on three common off-the-shelf models: glove @xcite , elmo @xcite , and bert @xcite . the three models are chosen to span a range of model sizes and representational power. we find that of the three off-the-shelf models, bert more frequently suffers a significant drop in retrieval quality and propagates more bias when trained on biased data. we then propose a method to mitigate the bias. that is, we want a classifier that is oblivious to an instance's group affiliation to blue or red, yet still performs the main task of judging the instance's relevance to the topic. our approach adapts domain-adversarial training @xcite for the three off-the-shelf models. experimental results show that the proposed approach mitigates the unintended bias at no or little cost of retrieval accuracy as compared to the original models; in fact, the retrieval accuracy for the modified bert is slightly boosted. the code and data for this project is available. foot_0 ## political ideology bias on social topic detection we investigate the impact of political ideology biases on extracting tweets relevant to specific social topics. unlike gender or racial bias, which has been widely studied in language representation, machine translation or relation extraction @xcite @xcite , there exists fewer work on political ideology biases. political ideology biases on social topic detection may arise from the difference in language usages between political ideological groups. prior studies have compared different language usages between political ideological groups such as conservatives and liberals in the us. such differences are reflected in general linguistics patterns such as language complexity @xcite and emotions associated with language @xcite . moreover, while talking about the same topic, language devices, such as specific types of metaphors, are often found to be different, which are associated with the groups' distinct political background and moral concerns @xcite . these observations indicate that the information producers come from diverse political ideological backgrounds, and the selection of keywords is critical for obtaining balanced and representative data points. therefore, we first examine the political ideology biases introduced from in human-selected keywords. ## bias propagation through models unlike well-curated and annotated benchmark datasets, raw social media data is sprawling and unorganized. contributors come from diverse backgrounds, with different racial origins, personalities, education levels, etc.; they may hold many kinds of implicit biases, some of which may not have been identified by the social scientists carrying out the experiment. under this setting, prior work for addressing the bias and ethical issues such as data statements (bender and friedman, 2018) may not be applicable. nonetheless, data sources such as twitter remain a powerful resource that researchers are willing to tap into. therefore, it is important to compare how different nlp systems perform on potentially biased training data and to develop approaches for mitigating bias propagation through models. we consider bias propagation in two dimensions: 1) to what extent does a model trained on biased examples tend to detect more instances with the same bias? 2) how does the learned bias interact with relevance? (does a biased classifier simply retrieve fewer instances of the other group, or does it actually retrieve less relevant instances for that group?). we also want to determine whether certain types of nlp systems are more likely to propagate the bias. given that nlp models are built with a diverse of architectures (transformers, rnn, etc.) and the number of trainable parameters varies from hundreds to billions, we define the type of nlp systems along their context representations and sizes. prior work shows that complex models, such as bert, do quite well for many nlp applications. multi-head attention allows bert to be able to capture complex and fine-grained patterns for the target prediction. on the other hand, big complex models with numerous training parameters are more likely to be overfitted when there are not enough training data @xcite . therefore, it is not obvious what might happen with biased-trained large complex models: do they succeed in using "real" patterns for the target task (e.g., predict relevant tweets in our case); or do they make use of the bias seen in the training data (e.g., capitalize on superficial patterns in the biased data) for reaching a minimum loss? our work aims to answer this question by examining three representative nlp models under multiple, differently biased training sets. ## experiments to address the central questions raised in this work -how biases are propagated in several widely-used nlp models and their effect on the overall retrieval accuracy, we conduct experiments to quantify the impact of biased training. to do so, we need to generate training sets for which we can measure the degree of bias along the blue-red spectrum. we also need metrics for determining the quality and bias of retrieved tweets. with this evaluation framework, we compare how different nlp models perform under multiple, differently biased training sets on two social topics -immigration and gun control. then we evaluate the effectiveness of our proposed bias mitigating approach under the same setting. ## conclusion we have studied the impact of political ideology biases in different types of topic detection models and demonstrated a domain adaptation approach as an effective way of mitigating the bias. our experimental results suggest that an elmo-based model is more robust to training bias, while a bert-based model is more negatively affected by the training bias. since the elmo-based model has nearly 40 times fewer trainable parameters than bert, we conjecture that big complex models are more likely to propagate the bias seen in the training set. although we have found the proposed adaptation architecture to be helpful for the three models, especially bert, in terms of mitigating some of the training bias, the approach still relies on some knowledge of the existence of the bias. this work offers a comparison point for future studies to evaluate the effect of bias in various predictive models and opens the door for further reducing the bias in topic detection applications.