Detecting sexism in social media: an empirical analysis of linguistic patterns and strategies

With the rise of social networks, there has been a marked increase in offensive content targeting women, ranging from overt acts of hatred to subtler, often overlooked forms of sexism. The EXIST (sEXism Identification in Social neTworks) competition, initiated in 2021, aimed to advance research in automatically identifying these forms of online sexism. However, the results revealed the multifaceted nature of sexism and emphasized the need for robust systems to detect and classify such content. In this study, we provide an extensive analysis of sexism, highlighting the characteristics and diverse manifestations of sexism across multiple languages on social networks. To achieve this objective, we conducted a detailed analysis of the EXIST dataset to evaluate its capacity to represent various types of sexism. Moreover, we analyzed the systems submitted to the EXIST competition to identify the most effective methodologies and resources for the automated detection of sexism. We employed statistical methods to discern textual patterns related to different categories of sexism, such as stereotyping, misogyny, and sexual violence. Additionally, we investigated linguistic variations in categories of sexism across different languages and platforms. Our results suggest that the EXIST dataset covers a broad spectrum of sexist expressions, from the explicit to the subtle. We observe significant differences in the portrayal of sexism across languages; English texts predominantly feature sexual connotations, whereas Spanish texts tend to reflect neosexism. Across both languages, objectification and misogyny prove to be the most challenging to detect, which is attributable to the varied vocabulary associated with these forms of sexism. Additionally, we demonstrate that models trained on platforms like Twitter can effectively identify sexist content on less-regulated platforms such as Gab. Building on these insights, we introduce a transformer-based system with data augmentation techniques that outperforms competition benchmarks. Our work contributes to the field by enhancing the understanding of online sexism and advancing the technological capabilities for its detection.