A Literature Survey on Multimodal and Multilingual Sexism Detection

Xuan Luo, Bin Liang, Qianlong Wang, Jing Li, Erik Cambria, Xiaojun Zhang, Yulan He, Min Yang, Ruifeng Xu

Abstract: Sexism has become a pressing issue, driven by the rapid-spreading influence of societal norms, media portrayals, and online platforms that perpetuate and amplify gender biases. Curbing sexism has emerged as a critical challenge globally. Being capable of recognizing sexist statements and behaviors is of particular importance since it is the first step in mind change. This survey provides an extensive overview of recent advancements in sexism detection. We present details of the various resources used in this field and methodologies applied to the task, covering different languages, modalities, models, and approaches. Moreover, we examine the specific challenges these models encounter in accurately identifying and classifying sexism. Additionally, we highlight areas that require further research and propose potential new directions for future exploration in the domain of sexism detection. Through this comprehensive exploration, we strive to contribute to the advancement of interdisciplinary research, fostering a collective effort to combat sexism in its multifaceted manifestations.

External IDs:dblp:journals/tcss/LuoLWLCZHYX25