Summary of Narrationdep: Narratives on Social Media For Automatic Depression Detection, by Hamad Zogan et al.
NarrationDep: Narratives on Social Media For Automatic Depression Detection
by Hamad Zogan, Imran Razzak, Shoaib Jameel, Guandong Xu
First submitted to arxiv on: 24 Jul 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes a novel deep learning framework called NarrationDep for detecting narratives associated with depression in social media posts. By analyzing tweets, NarrationDep accurately identifies crucial narratives by jointly modeling individual user tweet representations and clusters of users’ tweets. The framework consists of two layers: the first layer models text posts, while the second layer learns semantic representations of tweets using a novel hierarchical learning component. The results show that NarrationDep outperforms comparative models on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NarrationDep is a new way to understand what people are saying on social media and if they might be depressed. It looks at lots of tweets and finds patterns that help it figure out if someone’s talking about depression or not. This can help with things like detecting mental health issues, understanding how people think and feel, and even helping people get the support they need. |
Keywords
* Artificial intelligence * Deep learning
