In this study, we proposed a convolutional neural network model for gender prediction using English Twitter text as input. Ensemble of proposed model achieved an accuracy at 0.8237 on gender prediction and compared favorably with the state-of-the-art performance in a recent author profiling task. We further leveraged the trained models to predict the gender labels from an HPV vaccine related corpus and identified gender difference in public perceptions regarding HPV vaccine. The findings are largely consistent with previous survey-based studies.
Cite
@article{arxiv.1907.03167,
title = {Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning},
author = {Jingcheng Du and Chongliang Luo and Qiang Wei and Yong Chen and Cui Tao},
journal= {arXiv preprint arXiv:1907.03167},
year = {2019}
}
Comments
This manuscript has been accepted by 2019 KDD Workshop on Applied Data Science for Healthcare