  Analyzing the ever-increasing volume of posts on social media sites such as
Facebook and Twitter requires improved information processing methods for
profiling authorship. Document classification is central to this task, but the
performance of traditional supervised classifiers has degraded as the volume of
social media has increased. This paper addresses this problem in the context of
gender detection through ensemble classification that employs multi-model deep
learning architectures to generate specialized understanding from different
feature spaces.

Social and Information Networks;Artificial Intelligence;Computation and Language;Machine Learning;Machine Learning