Abstract
The present study compared three methods aimed at predicting the writer's gender based on writing features manifested in electronic discourse. The compared methods included qualitative content analysis, statistical analysis, and machine learning. These methods were further combined to create a mixed methods model. The findings showed that the machine learning model combined with qualitative content analysis produced the best prediction accuracy. Including qualitative content analysis was able to improve accuracy rates even when the training set for machine learning was relatively small. Thus, this study presented a concise model that can be fairly reliable in predicting gender based on electronic discourse with high accuracy rates and such accuracy was consistently found when the model was tested by two separate samples.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Cyber Research and Education
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
