# Data Augmentation for Mental Health Classification on Social Media

arXiv: https://arxiv.org/abs/2112.10064 · HTML: https://mlsanity.com/paper/2112.10064/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 40 references that are in the map.
- First posted on arXiv 19 Dec 2021.

## Builds on (most cited first, 24 of 24)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks](https://mlsanity.com/paper/1901.11196/) (2019)
- [Improving Neural Machine Translation Models with Monolingual Data](https://mlsanity.com/paper/1511.06709/) (2015)
- [A Survey of Data Augmentation Approaches for NLP](https://mlsanity.com/paper/2105.03075/) (2021)
- [MASS: Masked Sequence to Sequence Pre-training for Language Generation](https://mlsanity.com/paper/1905.02450/) (2019)
- [Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations](https://mlsanity.com/paper/1805.06201/) (2018)
- [Facebook FAIR's WMT19 News Translation Task Submission](https://mlsanity.com/paper/1907.06616/) (2019)
- [Data Augmentation using Pre-trained Transformer Models](https://mlsanity.com/paper/2003.02245/) (2020)
- [word2vec Explained: deriving Mikolov et al.'s negative-sampling word-embedding method](https://mlsanity.com/paper/1402.3722/) (2014)
- [Conditional BERT Contextual Augmentation](https://mlsanity.com/paper/1812.06705/) (2018)
- [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://mlsanity.com/paper/2106.07499/) (2021)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [Sequence-Level Mixed Sample Data Augmentation](https://mlsanity.com/paper/2011.09039/) (2020)
- [An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation](https://mlsanity.com/paper/1607.05368/) (2016)
- [Learning to Paraphrase for Question Answering](https://mlsanity.com/paper/1708.06022/) (2017)
- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (2021)
- [Conditional Augmentation for Aspect Term Extraction via Masked Sequence-to-Sequence Generation](https://mlsanity.com/paper/2004.14769/) (2020)
- [Conditional Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.10176/) (2021)
- [BET: A Backtranslation Approach for Easy Data Augmentation in Transformer-based Paraphrase Identification Context](https://mlsanity.com/paper/2009.12452/) (2020)
- [Finding Friends and Flipping Frenemies: Automatic Paraphrase Dataset Augmentation Using Graph Theory](https://mlsanity.com/paper/2011.01856/) (2020)
- [TensiStrength: Stress and relaxation magnitude detection for social media texts](https://mlsanity.com/paper/1607.00139/) (2016)
- [Quantifying the Suicidal Tendency on Social Media: A Survey](https://mlsanity.com/paper/2110.03663/) (2021)

## Built on by (most cited first, 7 of 7)

- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [Simple Data Augmentation Techniques for Chinese Disease Normalization](https://mlsanity.com/paper/2306.01931/) (2023)
- [Not Enough Labeled Data? Just Add Semantics: A Data-Efficient Method for Inferring Online Health Texts](https://mlsanity.com/paper/2309.09877/) (2023)
- [Data Augmentation for Emotion Detection in Small Imbalanced Text Data](https://mlsanity.com/paper/2310.17015/) (2023)
- [Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings](https://mlsanity.com/paper/2406.05190/) (2024)
- [3M-Health: Multimodal Multi-Teacher Knowledge Distillation for Mental Health Detection](https://mlsanity.com/paper/2407.09020/) (2024)
- [Monte Carlo Tree Search for Interpreting Stress in Natural Language](https://mlsanity.com/paper/2204.08105/) (2022)

## Abstract (arXiv)

The mental disorder of online users is determined using social media posts. The major challenge in this domain is to avail the ethical clearance for using the user generated text on social media platforms. Academic re searchers identified the problem of insufficient and unlabeled data for mental health classification. To handle this issue, we have studied the effect of data augmentation techniques on domain specific user generated text for mental health classification. Among the existing well established data augmentation techniques, we have identified Easy Data Augmentation (EDA), conditional BERT, and Back Translation (BT) as the potential techniques for generating additional text to improve the performance of classifiers. Further, three different classifiers Random Forest (RF), Support Vector Machine (SVM) and Logistic Regression (LR) are employed for analyzing the impact of data augmentation on two publicly available social media datasets. The experiments mental results show significant improvements in classifiers performance when trained on the augmented data.