# Depression detection in social media posts using affective and social norm features

arXiv: https://arxiv.org/abs/2303.14279 · HTML: https://mlsanity.com/paper/2303.14279/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 38 references that are in the map.
- First posted on arXiv 24 Mar 2023.

## Builds on (most cited first, 11 of 11)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [EmoBank: Studying the Impact of Annotation Perspective and Representation Format on Dimensional Emotion Analysis](https://mlsanity.com/paper/2205.01996/) (2022)
- [MoralStrength: Exploiting a Moral Lexicon and Embedding Similarity for Moral Foundations Prediction](https://mlsanity.com/paper/1904.08314/) (2019)
- [Identifying Depressive Symptoms from Tweets: Figurative Language Enabled Multitask Learning Framework](https://mlsanity.com/paper/2011.06149/) (2020)
- [An Exploratory Analysis of the Relation Between Offensive Language and Mental Health](https://mlsanity.com/paper/2105.14888/) (2021)
- [Attention-based Conditioning Methods for External Knowledge Integration](https://mlsanity.com/paper/1906.03674/) (2019)
- [Affective Conditioning on Hierarchical Networks applied to Depression Detection from Transcribed Clinical Interviews](https://mlsanity.com/paper/2006.08336/) (2020)
- [Assessing the Severity of Health States based on Social Media Posts](https://mlsanity.com/paper/2009.09600/) (2020)

## Built on by (most cited first, 1 of 1)

- [Harnessing the Power of Hugging Face Transformers for Predicting Mental Health Disorders in Social Networks](https://mlsanity.com/paper/2306.16891/) (2023)

## Abstract (arXiv)

We propose a deep architecture for depression detection from social media posts. The proposed architecture builds upon BERT to extract language representations from social media posts and combines these representations using an attentive bidirectional GRU network. We incorporate affective information, by augmenting the text representations with features extracted from a pretrained emotion classifier. Motivated by psychological literature we propose to incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme. Our analysis indicates that morality and profanity can be important features for depression detection. We apply our model for depression detection on Reddit posts on the Pirina dataset, and further consider the setting of detecting depressed users, given multiple posts per user, proposed in the Reddit RSDD dataset. The inclusion of the proposed features yields state-of-the-art results in both settings, namely 2.65% and 6.73% absolute improvement in F1 score respectively. Index Terms: Depression detection, BERT, Feature fusion, Emotion recognition, profanity, morality