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Analyzing the ever-increasing volume of posts on social media sites such asFacebook and Twitter requires improved information processing methods forprofiling authorship. Document classification is central to this task, but theperformance of traditional supervised classifiers has degraded as the volume ofsocial media has increased. This paper addresses this problem in the context ofgender detection through ensemble classification that employs multi-model deeplearning architectures to generate specialized understanding from differentfeature spaces.

Gender Detection on Social Networks using Ensemble Deep Learning