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In this study, we proposed a convolutional neural network model for genderprediction using English Twitter text as input. Ensemble of proposed modelachieved an accuracy at 0.8237 on gender prediction and compared favorably withthe state-of-the-art performance in a recent author profiling task. We furtherleveraged the trained models to predict the gender labels from an HPV vaccinerelated corpus and identified gender difference in public perceptions regardingHPV vaccine. The findings are largely consistent with previous survey-basedstudies.

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