Exploring difference in public perceptions on HPV vaccine between gender groups from Twitter using deep learning
This work addresses public health monitoring by identifying gender-based perceptions of the HPV vaccine on social media, but it is incremental as it applies an existing method to new data with results aligning with known findings.
The study tackled gender prediction from English Twitter text using a convolutional neural network, achieving an accuracy of 0.8237, and applied it to analyze gender differences in public perceptions of the HPV vaccine, finding results consistent with prior survey-based studies.
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.