A robust gender inference model for online social networks and its application to LinkedIn and Twitter

Authors

  • Athanasios Kokkos University of the Aegean
  • Theodoros Tzouramanis University of the Aegean

DOI:

https://doi.org/10.5210/fm.v19i9.5216

Keywords:

Text Mining in Online Social Networking Services, Psycho-linguistic analysis, Support Vector Machine, Digital Investigation, Attribute Disclosure, Gender Inference, LinkedIn, Twitter

Abstract

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.

Author Biographies

Athanasios Kokkos, University of the Aegean

Ph.D. candidate at the University of the Aegean

Theodoros Tzouramanis, University of the Aegean

Theodoros Tzouramanis, Assistant Professor,
Director of the Database Laboratory,
Department of Information and Communication Systems Engineering
University of the Aegean,
Karlovassi 83200, Samos, Greece.

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Published

2014-08-29

How to Cite

Kokkos, A., & Tzouramanis, T. (2014). A robust gender inference model for online social networks and its application to LinkedIn and Twitter. First Monday, 19(9). https://doi.org/10.5210/fm.v19i9.5216