How Does Stereotype Content Differ across Data Sources? (2024)

Abstract: For decades, psychologists have been studying stereotypes using specially-designed rating scales to capture people’s beliefs and opinions about different social groups. Now, using NLP tools on extensive collections of text, we have the opportunity to study stereotypes “in the wild” and on a large scale. However, are we truly capturing the same information? In this paper we compare measurements along six psychologically-motivated, stereotype-relevant dimensions (Sociability, Morality, Ability, Assertiveness, Beliefs, and Status) for 10 groups, defined by occupation. We compute these measurements on stereotypical English sentences written by crowd-workers, stereotypical sentences generated by ChatGPT, and more general data collected from social media, and contrast the findings with traditional, survey-based results, as well as a spontaneous word-list generation task. We find that while the correlation with the traditional scales varies across dimensions, the free-text data can be used to specify the particular traits associated with each group, and provide context for numerical survey data.

## introduction there is growing interest in the possibility of using nlp and large corpora to augment, complement, or even replace traditional psychological surveys to collect social sciences data @xcite @xcite . one area where nlp research has started to contribute is in the study and analysis of stereotypes. stereotypes are "a set of cognitive generalizations (e.g., beliefs, expectations) about the qualities and characteristics of the members of a group or social category" @xcite . there are a number of properties of stereotypes that motivate the use of nlp tools to better study and understand them. first, stereotypes are often communicated and perpetuated through natural language @xcite . second, they are by definition widely-held and pervasive, and so should be detectable in large samples of data @xcite . third, they can lead to farreaching negative consequences, and so there is practical interest in understanding how stereotypes are expressed "in the wild" in order to develop effective counter-strategies @xcite . nlp researchers have begun to study methods of uncovering stereotype information in twitter data @xcite , news texts and books @xcite , spoken conversations @xcite , and large language models @xcite . however, the question remains whether the information we can extract from these natural language datasets can actually replicate the information obtained from more traditional methods in social psychology; namely, rating scales. a common paradigm in stereotype research involves choosing a set of attributes, or dimensions, of interest, and then asking human participants (often college undergraduates) to rate social groups along those dimensions. the dimensions of interest vary according to different theoretical models, but can include, for example, warmth and competence in the stereotype content model @xcite , or agency, beliefs, and communion in the abc model @xcite . the social groups may be categorized based on gender, race, age, or any other social variable relevant to the research. as a result, for each social group, the researchers obtain annotations along each dimension. in this work, we investigate the possibility of reproducing the results of such a scale-based study, using low-dimensional vector representations of natural language data to estimate the dimensions of interest. we consider six psychologicallymotivated dimensions -sociability, morality, ability, assertiveness, status, and beliefs 1 -and a set of ten groups defined by occupation. we conduct a detailed comparison of the kind of stereotype data we obtained through (1) direct stereotype elicitation from crowd-workers, (2) direct stereotype elicitation from a generative large language model, and (3) targeted data collection from twitter (now known as 'x'). we compare these sources of information to two paradigms in the psychology literature: the traditional method using rating scales, as mentioned above, and a newer method involving spontaneous word list elicitation. we consider three research questions in the current study: ## methods in the following section, we describe several different sources of survey and natural language data in english, namely: psychological rating scales (sec 3.1) as well as lists of spontaneously-produced adjectives, crowd-sourced stereotypes from the stereoset dataset @xcite , stereotypes prompted from chatgpt, and tweets from twitter (sec 3.2). these data sources differ in many relevant aspects, summarized in to make a direct comparison across all the data sources, we first identify the subset of social groups for which data is available in all the existing datasets. the majority of this subset consists of different occupations: politicians, teachers, ceos, scientists, bankers, accountants, engineers, farmers, lawyers, and nurses. thus we consider only these 10 target groups in the analysis. following our discussion of the datasets, in section 3.3 we present the dimensionality-reduction method we use to reduce the free-text sentences in the four natural language datasets down to six dimensions, so that they can be compared directly to the 6-dimensional gold standard rating scale data. ## conclusion we conducted a comparison of stereotypes of 10 occupational groups across traditional survey-based data and various text sources. the answers to our research questions are summarized as follows: deepening our understanding of stereotypes can help in the development of effective counterstrategies. the work presented in this paper can support these goals in a number of ways. for example, if we consider the ratings of scientists and engineers on the scale-based data, it is not entirely clear what an appropriate counter-example should be (a scientist with low morality and low ability?). however, the natural language data helps surface the more specifically harmful stereotype that scientists are all male and anti-social. challenging that aspect of the stereotype is more likely to be effective at increasing women's participation in science. at the same time, the scale-based data may provide information that is "hidden" in the social media data, such as the stereotypical idea that most farmers are religious and politically right-wing. this type of information, although essential in gaining a broader understanding of stereotypes, does not tend to be explicitly stated on social media. we also observed that the scale-based data, as well as the chatgpt data, do not clearly communicate extremely negative or offensive stereotypes -even though these should be the highest priority for mitigation. therefore, understanding the strengths and weaknesses of the information available in different datasets can have important real-world implications. furthermore, future work could examine how the data from unconventional sources, such as social media or chatgpt, may be used to augment more traditional sources, such as lexicons.