Call for Research on the Impact of Information Retrieval on Social Norms

The information retrieval (IR) systems of major media platforms have a significant impact on social norms. Social norms contribute to the cultural identity of a society, but can also lead to people being marginalized, suffering from social pressure, and feeling inferior. For this reason, we call on the IR community to (1) contribute to the social sciences with computational means to study the impact of IR systems on social norms, and (2) to incorporate respective social science research findings into IR system development. To support our call, this paper presents a dataset and classification technology for investigating the prevalence of normative beauty ideals in multimodal (image and text) search results and recommendations. On a dataset comprising 928 annotated social media posts, in addition to determining the best classification model for the task, we examine how state-of-the-art zero-shot classifiers perform compared to fine-tuned models, and how multimodal models perform compared to unimodal variants. With 92% classification accuracy, a late fusion model with individually fine-tuned image and text representations achieves peak effectiveness, which are promising first results for research in computational social science and on IR systems. To illustrate our work, we analyze the image search results pages of a major web search engine and report our findings. The code repository of our research is available at https://github.com/webis-de/ECIR-25.