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Journals


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A. Kokkos, Theodoros Tzouramanis, A robust gender inference model for online social networks and its application to LinkedIn and Twitter, First Monday, Vol. 19, No. 9, 2014, http://journals.uic.edu/ojs/index.php/fm...
 

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.

Conferences


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Books


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Conferences Proceedings Editor


Copyright Notice: This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted or mass reproduced without the explicit permission of the copyright holder.