mlsanityData to 7 Oct 2026

Social Behavior and Mental Health: A Snapshot Survey under COVID-19 Pandemic

arXiv 2105.08165 ↗First posted 17 May 2021cs.SI cs.CL cs.CYcited by 0

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Abstract (arXiv)

Online social media provides a channel for monitoring people's social behaviors and their mental distress. Due to the restrictions imposed by COVID-19 people are increasingly using online social networks to express their feelings. Consequently, there is a significant amount of diverse user-generated social media content. However, COVID-19 pandemic has changed the way we live, study, socialize and recreate and this has affected our well-being and mental health problems. There are growing researches that leverage online social media analysis to detect and assess user's mental status. In this paper, we survey the literature of social media analysis for mental disorders detection, with a special focus on the studies conducted in the context of COVID-19 during 2020-2021. Firstly, we classify the surveyed studies in terms of feature extraction types, varying from language usage patterns to aesthetic preferences and online behaviors. Secondly, we explore detection methods used for mental disorders detection including machine learning and deep learning detection methods. Finally, we discuss the challenges of mental disorder detection using social media data, including the privacy and ethical concerns, as well as the technical challenges of scaling and deploying such systems at large scales, and discuss the learnt lessons over the last few years.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 166 references that are in the map. First posted on arXiv 17 May 2021.

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References · 16

  1. 2021Deep Learning for Suicide and Depression Identification with Unsupervised Label Correctioncited by 45
  2. 2018Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequencescited by 36
  3. 2019Suicidal Ideation Detection: A Review of Machine Learning Methods and Applicationscited by 31
  4. 2016Instagram photos reveal predictive markers of depressioncited by 25
  5. 2016Forecasting the onset and course of mental illness with Twitter datacited by 25
  6. 2017Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Mediacited by 24
  7. 2020Deep Sentiment Classification and Topic Discovery on Novel Coronavirus or COVID-19 Online Discussions: NLP Using LSTM Recurrent Neural Network Approachcited by 20
  8. 2019A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streamscited by 18
  9. 2019What Twitter Profile and Posted Images Reveal About Depression and Anxietycited by 17
  10. 2020Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Mediacited by 12
  11. 2020Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Mediacited by 12
  12. 2020Detection and Classification of mental illnesses on social media using RoBERTacited by 8
4 more
  1. 2020Mining social media data for biomedical signals and health-related behaviorcited by 7
  2. 2019See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Datacited by 4
  3. 2020Monitoring Depression Trend on Twitter during the COVID-19 Pandemiccited by 2
  4. 2021LonelyText: A Short Messaging Based Classification of Lonelinesscited by 1

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