Data Augmentation for Mental Health Classification on Social Media

Ansari, Gunjan; Garg, Muskan; Saxena, Chandni

doi:10.48550/arxiv.2112.10064

Cited by 2 publications

(1 citation statement)

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“…[42] suggests the problem of noisy labels and address it by introducing a new dataset on depression versus suicide. In extension to this, a data augmentation approach resolve the problem of limited data availability for mental health analysis [138].…”

Section: The Historical Evolution Of Classification Modelsmentioning

confidence: 99%

Mental Health Analysis in Social Media Posts: A Survey

Garg

2023

Arch Computat Methods Eng

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The surge in internet use to express personal thoughts and beliefs makes it increasingly feasible for the social NLP research community to find and validate associations between social media posts and mental health status . Cross-sectional and longitudinal studies of social media data bring to fore the importance of real-time responsible AI models for mental health analysis. Aiming to classify the research directions for social computing and tracking advances in the development of machine learning (ML) and deep learning (DL) based models, we propose a comprehensive survey on quantifying mental health on social media . We compose a taxonomy for mental healthcare and highlight recent attempts in examining social well-being with personal writings on social media. We define all the possible research directions for mental healthcare and investigate a thread of handling online social media data for stress, depression and suicide detection for this work. The key features of this manuscript are (i) feature extraction and classification, (ii) recent advancements in AI models, (iii) publicly available dataset, (iv) new frontiers and future research directions. We compile this information to introduce young research and academic practitioners with the field of computational intelligence for mental health analysis on social media. In this manuscript, we carry out a quantitative synthesis and a qualitative review with the corpus of over 92 potential research articles. In this context, we release the collection of existing work on suicide detection in an easily accessible and updatable repository: https://github.com/drmuskangarg/mentalhealthcare .

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Section: The Historical Evolution Of Classification Modelsmentioning

confidence: 99%