Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning

TL;DR

This paper develops a CNN-based model to predict gender from Twitter data and uses it to analyze gender differences in public perceptions of HPV vaccine, aligning with prior survey results.

Contribution

It introduces a CNN ensemble for gender prediction on Twitter data and applies it to study gender-based perception differences on HPV vaccine.

Findings

Gender differences in HPV vaccine perceptions identified

Model achieved 82.37% accuracy in gender prediction

Results align with previous survey studies

Abstract

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.