Gender Detection on Social Networks using Ensemble Deep Learning

04/13/2020

∙

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.

READ FULL TEXT

Gender Detection on Social Networks using Ensemble Deep Learning

Detection of depression on social networks using transformers and ensembles

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

Detection of Novel Social Bots by Ensembles of Specialized Classifiers

Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study

DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection

Improved two-stage hate speech classification for twitter based on Deep Neural Networks

Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks

Gender Detection on Social Networks using Ensemble Deep Learning

Related Research

Detection of depression on social networks using transformers and ensembles

Ensemble Deep Learning on Time-Series Representation of Tweets for Rumor Detection in Social Media

Detection of Novel Social Bots by Ensembles of Specialized Classifiers

Cyberbullying Detection in Social Networks Using Deep Learning Based Models; A Reproducibility Study

DeL-haTE: A Deep Learning Tunable Ensemble for Hate Speech Detection

Improved two-stage hate speech classification for twitter based on Deep Neural Networks

Learning multi-faceted representations of individuals from heterogeneous evidence using neural networks