Abstract—This paper presents an application of machine
learning to classify Facebook users’ gender based on their
username alone. User profile information on social networks is
important in many studies, but occasionally no information is
publicly available online, such as age or gender. Most studies
only use textual information from the web page. Instead,
we opted to study gender classification by username, in which
the gender is inferred from the users first name and alias name.
We focused only on Thai names which may have certain
patterns that reveal the owner’s gender. A combination
of different models is proposed to classify gender based on
Thai Facebook usernames. Each model was trained using
a supervised learning approach. Furthermore, all the
classification results were combined into a final model. Using
this method, the model achieved 91.75% level of accuracy.
Index Terms—Gender classification, Facebook username,
name analysis, social network, machine learning.
The authors are with the Department of Computer Engineering,
Chulalongkorn University, 254 Phayathai Rd., Phatumwan Bangkok, 10330
Thailand. (e-mail: 6170973921@student.chula.ac.th, sukree.s@chula.ac.th).
Cite: Supitcha Yuenyong and Sukree Sinthupinyo, "Gender Classification of Thai Facebook Usernames," International Journal of Machine Learning and Computing vol. 10, no. 5, pp. 618-623, 2020.
Copyright © 2020 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).