# NarrationDep: Narratives on Social Media For Automatic Depression Detection

arXiv: https://arxiv.org/abs/2407.17174 · HTML: https://mlsanity.com/paper/2407.17174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 83 references that are in the map.
- First posted on arXiv 24 Jul 2024.

## Builds on (most cited first, 13 of 13)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://mlsanity.com/paper/1906.08237/) (2019)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [PaLM-E: An Embodied Multimodal Language Model](https://mlsanity.com/paper/2303.03378/) (2023)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Inferring Interpersonal Relations in Narrative Summaries](https://mlsanity.com/paper/1512.00112/) (2015)
- [Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression](https://mlsanity.com/paper/2205.09497/) (2022)
- [Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia](https://mlsanity.com/paper/2007.02325/) (2020)
- [THEaiTRE 1.0: Interactive generation of theatre play scripts](https://mlsanity.com/paper/2102.08892/) (2021)
- [Predicting the Quality of Short Narratives from Social Media](https://mlsanity.com/paper/1707.02499/) (2017)
- [Anxious Depression Prediction in Real-time Social Data](https://mlsanity.com/paper/1903.10222/) (2019)

## Abstract (arXiv)

Social media posts provide valuable insight into the narrative of users and their intentions, including providing an opportunity to automatically model whether a social media user is depressed or not. The challenge lies in faithfully modelling user narratives from their online social media posts, which could potentially be useful in several different applications. We have developed a novel and effective model called textttNarrationDep, which focuses on detecting narratives associated with depression. By analyzing a user's tweets, textttNarrationDep accurately identifies crucial narratives. textttNarrationDep is a deep learning framework that jointly models individual user tweet representations and clusters of users' tweets. As a result, textttNarrationDep is characterized by a novel two-layer deep learning model: the first layer models using social media text posts, and the second layer learns semantic representations of tweets associated with a cluster. To faithfully model these cluster representations, the second layer incorporates a novel component that hierarchically learns from users' posts. The results demonstrate that our framework outperforms other comparative models including recently developed models on a variety of datasets.