Revealing the Demographic Attributes of the Authors from the Abstracts of Scientific Articles

This study presents multiple strategies to automatically reveal undisclosed demographic attributes of the authors in the double-blind submissions. From a limited amount of textual content of around 100-200 words excerpted from an abstract, this study aims to reveal the following pieces of information, i) the English language nativeness of the primary author, ii) the country of origin of the primary author, and iii) the gender of the primary author. We introduce an annotated dataset of over 5600 articles labeled with the native language, country of origin, and gender information of the primary authors. We employ classical machine learning (CML) algorithms with statistical n-gram features and transformer-based fine-tuned language models to determine various demographic attributes. We observe that transformer-based models yield slightly better performances for all three tasks. The transformer-based models achieve macro F1 scores close to 75% for identifying the English language nativeness of the primary authors. To determine the country of the non-native English authors, the fine-tuned transformer-based models obtain F1 scores of around 60% (10-class classification). For the gender prediction task, we attain F1 scores of 0.65 by the transformer-based models. The experimental results demonstrate that the fine-tuned language models and CML classifiers are capable of disclosing various author attributes with an acceptable level of accuracy that can undermine the blindness of the double-blind submission.