Inferring Sociodemographic Attributes of Wikipedia Editors: State-of-the-art and Implications for Editor Privacy

Abstract

In this paper, we investigate the state-of-the-art of machine learning models to infer sociodemographic attributes of Wikipedia editors based on their public profile pages and corresponding implications for editor privacy. To build models for inferring sociodemographic attributes, ground truth labels are obtained via different strategies, using publicly disclosed information from editor profile pages. Different embedding techniques are used to derive features from editors’ profile texts. In comparative evaluations of different machine learning models, we show that the highest prediction accuracy can be obtained for the attribute gender, with precision values of 82% to 91% for women and men respectively, as well as an averaged F1-score of 0.78. For other attributes like age group, education, and religion, the utilized classifiers exhibit F1-scores in the range of 0.32 to 0.74, depending on the model class. By merely using publicly disclosed information of Wikipedia editors, we highlight issues surrounding editor privacy on Wikipedia and discuss ways to mitigate this problem. We believe our work can help start a conversation about carefully weighing the potential benefits and harms that come with the existence of information-rich, pre-labeled profile pages of Wikipedia editors.