Deep learning for author gender and sex identification in natural language text

With the development of digital interaction, there is a need to control the information distributed by users so that it complies with legal requirements. The study proposes a technique for classifying Russian-language texts by considering both the biological sex of the authors (male and female) and gender-related distinctions, including heterosexual and homosexual groups as well as specific LGBT categories such as gay men, lesbian women, bisexual, and transgender authors. The article proposes a technique for identifying the sex and gender of authors of Russian-language texts by utilizing an ensemble of methods, including Support Vector Machines (SVM), Convolutional Neural Networks (CNN), and the Russian Bidirectional Encoder Representations from Transformers (RuBERT). This research is introducing a technique for gender identification in Russian texts, as prior studies have only addressed the automatic determination of an author’s sex. To conduct the study, custom datasets of user comments from social networks were created and annotated with both sex and gender labels. In tasks related to identifying the author’s sex, the proposed technique achieved an accuracy exceeding 90 %. This includes the classification of heterosexual men and women as well as a mixed dataset comprising both heterosexual and homosexual individuals. When distinguishing individuals of the same sex based on sexual orientation, the results showed that homosexual and heterosexual women display uncommon writing styles in contrast to men of different orientations, with accuracy rates of 93 % for women and 85 % for men. Additionally, an experiment focused on identifying LGBT individuals and their gender identities based on writing style achieved an accuracy of 93 %. The key takeaway from this study is that combining CNN, RuBERT, and SVM leads to a more robust model for gender classification. The method has been tested on varying numbers of samples. In the basic experiments for determining sex, the method achieves an accuracy of 92 %. When addressing the task of detecting the author’s LGBT affiliation, the method achieves 93 % accuracy. The proposed technique can be applied for automated monitoring of social networks to detect and analyze the gender identity of text authors. It is also promising for use in content moderation systems as well as in sociological and linguistic research.