Jekyll institute or Mrs Hyde? gender identification with machine learning

Social media platforms offer an invaluable wealth of data to understand what is taking place in our society. However, social media data hides demographic biases related to characteristics such as gender or age. Therefore, considering social media data as representative of the population can lead to fallacious interpretations. For instance, in France in 2021, women represent 51.6% of the population 1 1 https://www.insee.fr/fr/statistiques/6024136 . , whereas on Twitter they represent only 33.5% of French users 2 2 https://datareportal.com/reports/digital-2021-france . . With such a significant difference between social network user demographics and the actual population, detecting the gender or age before delving into a deeper analysis of social phenomena becomes a priority. In this paper, we tackle the gender detection problem on Twitter. We introduce miniAM2, which is an assemblage model of an enriched distillation with weak-supervised learning. Our contributions are threefold: (i) a novel multilingual model that outperforms existing models in both accuracy and speed, allowing for real-time gender detection and organization status on Twitter based on their name , screen_name , and description , making it lighter and faster than state-of-the-art; (ii) an innovative assemblage multi-language strategy that enriches a distillation process with weak-supervised learning using minimal annotated data, and (iii) a unique method to adapt the model to similar languages without requiring annotated data in the target language, which provides significant advancements in handling resource-poor languages in gender detection tasks. We provide our model on demand so social scientists can use it for their analysis. • Over the SOTA multilingual model detects gender/org. from Twitter names and descriptions. • Multi-language approach enhances distillation, weak supervision, and low data use. • Adapts model to similar languages without annotated target language data..