A robust gender inference model for online social networks and its application to LinkedIn and Twitter
DOI:
https://doi.org/10.5210/fm.v19i9.5216Keywords:
Text Mining in Online Social Networking Services, Psycho-linguistic analysis, Support Vector Machine, Digital Investigation, Attribute Disclosure, Gender Inference, LinkedIn, TwitterAbstract
Online social networking services have come to dominate the dot com world: Countless online communities coexist on the social Web. Some typically characteristic user attributes, such as gender, age group, sexual orientation, are not automatically part of the profile information. In some cases user attributes can even be deliberately and maliciously falsified. This paper examines automated inference of gender on online social networks by analyzing written text with a combination of natural language processing and classification techniques. Extensive experimentation on LinkedIn and Twitter has yielded accuracy of this gender identification technique of up to 98.4 percent.
Downloads
Additional Files
Published
How to Cite
Issue
Section
License
Authors retain copyright to their work published in First Monday. Please see the footer of each article for details.