# Gender Detection on Social Networks using Ensemble Deep Learning

arXiv: https://arxiv.org/abs/2004.06518 · HTML: https://mlsanity.com/paper/2004.06518/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2020.

## Built on by (most cited first, 2 of 2)

- [A Computational Approach to Understand Mental Health from Reddit: Knowledge-aware Multitask Learning Framework](https://mlsanity.com/paper/2203.11856/) (2022)
- [Multi-aspect Multilingual and Cross-lingual Parliamentary Speech Analysis](https://mlsanity.com/paper/2207.01054/) (2022)

## Abstract (arXiv)

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.