Gender Detection on Social Networks using Ensemble Deep Learning
This addresses the problem of profiling authorship on social networks like Facebook and Twitter for applications in targeted advertising or content moderation, but appears incremental.
The paper tackled gender detection from social media posts by using ensemble deep learning to improve classification performance over traditional methods, achieving unspecified gains in accuracy.
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.