Health issue identification in social media based on multi-task hierarchical neural networks with topic attention

Health issue identification in social media is to predict whether the writers have a disease based on their posts. Numerous posts and comments are shared on social media by users. Certain posts may reflect writers' health condition, which can be employed for health issue identification. Usually, the health issue identification problem is formulated as a classification task.

In this paper, we propose novel multi-task hierarchical neural networks with topic attention for identifying health issue based on posts collected from the social media platforms. Specifically, the model incorporates the hierarchical relationship among the document, sentences, and words via bidirectional gated recurrent units (BiGRUs). The global topic information shared across posts is incorporated with the hidden states of BiGRUs to obtain the topic-enhanced attention weights for words. In addition, tasks of predicting whether the writers suffer from a disease (health issue identification) and predicting the specific domain of the posts (domain category classification) are learned jointly in multi-task mechanism.

The proposed method is evaluated on two datasets: dementia issue dataset and depression issue dataset. The proposed approach achieves 98.03% and 88.28% F-1 score on two datasets, outperforming the state-of-the-art approach by 0.73% and 0.4% respectively. Further experimental analysis shows the effectiveness of incorporating both the multi-task learning framework and topic attention mechanism.