Pro-Woman, Anti-Man? Identifying Gender Bias in Stance Detection (2024)

Abstract: Gender bias has been widely observed in NLP models, which has the potential to perpetuate harmful stereotypes and discrimination. In this paper, we construct a dataset GenderStance of 36k samples to measure gender bias in stance detection, determining whether models consistently predict the same stance for a particular gender group. We find that all models are gender-biased and prone to classify sentences that contain male nouns as Against and those with female nouns as Favor. Moreover, extensive experiments indicate that sources of gender bias stem from the fine-tuning data and the foundation model itself. We will publicly release our code and dataset.

## introduction the prevalence of unintended social biases in nlp models has been recently identified as a major concern for the field @xcite @xcite @xcite . these biases have been found in many subtasks of nlp, ranging from learned word embeddings @xcite @xcite , coreference resolution @xcite @xcite , natural language inference @xcite @xcite , dialog @xcite @xcite and machine translation @xcite @xcite . stance detection aims to identify the attitude (e.g., favor, against or none, etc.) of a given text with respect to a specific target of interest @xcite @xcite @xcite , which can provide valuable insights into decision-making @xcite (e.g., presidential elections, marketing strategies and social media monitoring, etc.). despite a plethora of studies showing presence of systematic gender bias in prolifically applied nlp methods, little attention has been paid to the role of gender in stance detection. @xcite demonstrate that most datasets inevitably inherit the biases of their annotators and overfitting on these dataset biases can result in low robustness in stance detection models. @xcite identify the dataset biases as potential spurious correlations of sentiment-stance relations and target-independent lexical choices associated with stance. however, these studies have not taken gender bias into consideration. as compared to previous work, we explore whether stance detection systems tend to associate the stance label with a certain gender, predominantly supporting or opposing the opinions of a certain group of people, and thereby negatively impacting the decision-making. to identify gender bias as well as understand how it arises in stance detection, we construct a challenging dataset, genderstance, to explore the predictive differences of models on samples that differ only by gender. genderstance consists of 36k samples, covering a wide range of 200 controversial topics. experimental results indicate that state-of-the-art models @xcite @xcite are all gender-biased in stance detection, inclined to label sentences containing male nouns as against and label those with female nouns as favor. moreover, we explore how bias can enter into stance detection systems. results suggest that sources of bias stem from the fine-tuning data and the foundation model itself. to our knowledge, we are the first to evaluate gender bias in stance detection. we argue that current stance detection systems run the risk of making unlicensed inferences, with inherent gender bias possibly resulting in the underrepresentation of different gender groups @xcite . our findings highlight the importance of incorporating gender fairness into the design and evaluation of stance detection systems. ## experimental results the main results of gender bias evaluations are shown in second, third, in terms of training data, although both vast and semeval-2016 show similar trends with respect to most metrics, results from to gain a deeper insight into the bias introduced by the training set, we propose a simple rule-based approach to balance noun phrases for each gender within the training data of semeval-2016. we first identify the gendered terms in each sample with a list of gender pairs and then insert their opposites (e.g., "he" ⇔ "she") at a random position within the sample. from results in ## implications of gender bias in stance detection we have so far evaluated gender bias of different models on our genderstance dataset. in this section, we briefly outline potential implications of our findings in the area of stance detection. first, the model's behavior misrepresents gender groups by associating male or female nouns with specific stances. this misrepresentation can distort the por-trayal of genders, suggesting that men are more likely to oppose controversial topics while women are more likely to support them. such a skewed portrayal contributes to reinforcing gender stereotypes. second, since most models run the risk of making biased inferences, individuals might potentially influence the decision-making process, such as altering the support or opposition rates of a particular policy, by purposefully spreading posts with male or female nouns online. ## conclusion in this paper, we construct a dataset genderstance to test the presence of gender bias in state-of-theart models of stance detection. we consider three groups of models and evaluate them using our dataset. results show that all models tend to associate gender terms with the stance label, leading to biased predictions. the data used for fine-tuning can be seen as the potential source of gender bias. in addition, stance detection models also contribute to the propagation of gender bias, thereby resulting in unfair treatment of male and female groups. our dataset can add to the spectrum of nlp benchmarks for evaluating gender bias on relevant application tasks.