The Detection of Mental Health Conditions by Incorporating External Knowledge

Mental health conditions have become a growing problem; it increases the likelihood of premature death for patients, and imposes a high economic burden on the world. However, some studies have shown that if patients are detected and treated early, the social impact and economic costs of mental illness can be reduced. With the popularity of social media, people are sharing their feelings on it, which allows data from social media to be used to study mental health conditions. However, past research had been limited to the optimization of the model or using different types of data available on social media, resulting in models that only rely on data to make decisions. Moreover, people judge things not only by the data collected, but also by background knowledge. Therefore, we considered the diagnostic process of doctors and combined the knowledge of psychological screening tools and diagnostic criteria into the model. In addition, we also tested the effect of combining general knowledge. We retrieve the top m most relevant knowledge segments for each user's post, and then put both into the prediction model. Experimental results show that our method outperforms previous studies, and the F1-score is increased more than 10% in some situations. Moreover, because the knowledge segments are automatically retrieved, our method does not require additional manual labeling, and the knowledge set can be freely adjusted. These show that our method can help detect mental health conditions and can be continuously optimized in practice.