Automatic detection of depression symptoms in twitter using multimodal analysis

Depression is the most prevalent mental disorder that can lead to suicide. Due to the tendency of people to share their thoughts on social platforms, social data contain valuable information that can be used to identify user's psychological states. In this paper, we provide an automated approach to collect and evaluate tweets based on self-reported statements and present a novel multimodal framework to predict depression symptoms from user profiles. We used n-gram language models, LIWC dictionaries, automatic image tagging, and bag-of-visual-words. We consider the correlation-based feature selection and nine different classifiers with standard evaluation metrics to assess the effectiveness of the method. Based on the analysis, the tweets and bio-text alone showed 91% and 83% accuracy in predicting depressive symptoms, respectively, which seems to be an acceptable result. We also believe performance improvements can be achieved by limiting the user domain or presence of clinical information.