Gender equality is one of the 17 Sustainable Development Goals of the United Nations 2030 Agenda. It has become a topical issue for business entities and institutions. In parallel, gender governance and social sustainability reporting and disclosure has received increasing attention in specialized literature. This article contributes to this body of literature by proposing the results of the case study of public mega-universities in Italy. The performance plan documents of Italian mega-universities were analyzed using text mining techniques. These techniques can harness existing data to uncover latent information about gender equality, which may be present in the documents. The results show that the number of words related to gender equality in non-financial reporting and disclosure has consistently grown over time: words referring to gender equality in 2020-22 are almost double that of 2019-21, which, in turn, are much higher than in 2018-20. This study highlights that machine learning and Big Data research offer a great potential for universities to leverage the vast information generated from accountability mechanisms to gain new insights to improve decision-making on gender equality and social sustainability disclosure.
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