  Social media contains useful information about people and the society that
could help advance research in many different areas (e.g. by applying opinion
mining, emotion/sentiment analysis, and statistical analysis) such as business
and finance, health, socio-economic inequality and gender vulnerability. User
demographics provide rich information that could help study the subject
further. However, user demographics such as gender are considered private and
are not freely available. In this study, we propose a model based on
transformers to predict the user's gender from their images and tweets. We
fine-tune a model based on Vision Transformers (ViT) to stratify female and
male images. Next, we fine-tune another model based on Bidirectional Encoders
Representations from Transformers (BERT) to recognize the user's gender by
their tweets. This is highly beneficial, because not all users provide an image
that indicates their gender. The gender of such users could be detected form
their tweets. The combination model improves the accuracy of image and text
classification models by 6.98% and 4.43%, respectively. This shows that the
image and text classification models are capable of complementing each other by
providing additional information to one another. We apply our method to the
PAN-2018 dataset, and obtain an accuracy of 85.52%.
