ML Anthology

Screening for Depressed Individuals by Using Multimodal Social Media Data

Paulo Mann, Aline Paes, Elton H. Matsushima
AAAI 2021 pp. 15722-15723
doi:10.1609/AAAI.V35I18.17858 /aaai/2021/mann2021aaai-screening/

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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Cite

Text

Mann et al. "Screening for Depressed Individuals by Using Multimodal Social Media Data." AAAI Conference on Artificial Intelligence, 2021. doi:10.1609/AAAI.V35I18.17858

Markdown

[Mann et al. "Screening for Depressed Individuals by Using Multimodal Social Media Data." AAAI Conference on Artificial Intelligence, 2021.](https://mlanthology.org/aaai/2021/mann2021aaai-screening/) doi:10.1609/AAAI.V35I18.17858

BibTeX

@inproceedings{mann2021aaai-screening,
  title     = {{Screening for Depressed Individuals by Using Multimodal Social Media Data}},
  author    = {Mann, Paulo and Paes, Aline and Matsushima, Elton H.},
  booktitle = {AAAI Conference on Artificial Intelligence},
  year      = {2021},
  pages     = {15722-15723},
  doi       = {10.1609/AAAI.V35I18.17858},
  url       = {https://mlanthology.org/aaai/2021/mann2021aaai-screening/}
}