Screening for Depressed Individuals by Using Multimodal Social Media Data
Keywords:Depression, Explainability, Natural Language Processing, Reinforcement Learning, Students
AbstractDepression has increased at alarming rates in the worldwide population. One alternative to finding depressed individuals is using social media data to train machine learning (ML) models to identify depressed cases automatically. Previous works have already relied on ML to solve this task with reasonably good F-measure scores. Still, several limitations prevent the full potential of these models. In this work, we show that the depression identification task through social media is better modeled as a Multiple Instance Learning (MIL) problem that can exploit the temporal dependencies between posts.
How to Cite
Mann, P., Paes, A., & Matsushima, E. H. (2021). Screening for Depressed Individuals by Using Multimodal Social Media Data. Proceedings of the AAAI Conference on Artificial Intelligence, 35(18), 15722-15723. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/17858
The Twenty-Sixth AAAI/SIGAI Doctoral Consortium