Screening for Depressed Individuals by Using Multimodal Social Media Data

Authors

  • Paulo Mann Universidade Federal Fluminense
  • Aline Paes Universidade Federal Fluminense
  • Elton H. Matsushima Universidade Federal Fluminense

Keywords:

Depression, Explainability, Natural Language Processing, Reinforcement Learning, Students

Abstract

Depression 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.

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Published

2021-05-18

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

Issue

Section

The Twenty-Sixth AAAI/SIGAI Doctoral Consortium