ArticleIEEE transactions on computational social systems2021
Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia.
Article in IEEE transactions on computational social systems, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Enhancing explainability and performance of the depression detection model on social media utilizing feature engineering and LLMs.Health information science and systems · 2026Article
- Depression Detection on Social Media: A Classification Framework and Research Challenges and Opportunities.Journal of healthcare informatics research · 2024Article
- Large-Scale Estimation and Analysis of Web Users' Mood from Web Search Query and Mobile Sensor Data.Big data · 2024Article
- Leveraging twitter data to understand nurses' emotion dynamics during the COVID-19 pandemic.Health information science and systems · 2023Article
- Impact of Early COVID-19 Waves on Cardiac Rehabilitation Delivery in Australia: A National Survey.Heart, lung & circulation · 2023Article
- A global portrait of expressed mental health signals towards COVID-19 in social media space.International journal of applied earth observation and geoinformation : ITC journal · 2023Article
- Reddit language indicates changes associated with diet, physical activity, substance use, and smoking during COVID-19.PloS one · 2023Article
- Analyzing the public sentiment on COVID-19 vaccination in social media: Bangladesh context.Array (New York, N.Y.) · 2022Article
- Risk Perception Influence on Vaccination Program on COVID-19 in Chile: A Mathematical Model.International journal of environmental research and public health · 2022Article
- Social Support and Depressive Symptoms in the Context of COVID-19 Lockdown: The Moderating Role of Attachment Styles.International journal of public health · 2022Article
- Automatic detection of depression symptoms in twitter using multimodal analysis.The Journal of supercomputing · 2022Article
- The times, they are a-changin': tracking shifts in mental health signals from early phase to later phase of the COVID-19 pandemic in Australia.BMJ global health · 2022Article
- Sentiment analysis and topic modeling for COVID-19 vaccine discussions.World wide web · 2022Article
- Behavioral Correlates of COVID-19 Worry: Stigma, Knowledge, and News Source.International journal of environmental research and public health · 2021Article
- The impact of COVID-19 pandemic on the level of depression among health care workers: cross-sectional study.PeerJ · 2021Article
- One in Five Street Traditional Coffee Vendors Suffered from Depression During the COVID-19 Pandemic in Harar Town, Ethiopia.Neuropsychiatric disease and treatment · 2021Article
- COVID-19: Detecting depression signals during stay-at-home period.Health informatics journalArticle
- Generalizing factors of COVID-19 vaccine attitudes in different regions: A summary generation and topic modeling approach.Digital healthArticle
- Understanding COVID-19 vaccine hesitancy of different regions in the post-epidemic era: A causality deep learning approach.Digital healthArticle
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The recent Coronavirus Infectious Disease 2019 (COVID-19) pandemic has caused an unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. Depression can cause serious emotional, behavioral, and physical health problems with significant consequences, both personal and social costs included. This article studies community depression dynamics due to the COVID-19 pandemic through user-generated content on Twitter. A new approach based on multimodal features from tweets and term frequency-inverse document frequency (TF-IDF) is proposed to build depression classification models. Multimodal features capture depression cues from emotion, topic, and domain-specific perspectives. We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia. Our novel classification model is capable of extracting depression polarities that may be affected by COVID-19 and related events during the COVID-19 period. The results found that people became more depressed after the outbreak of COVID-19. The measures implemented by the government, such as the state lockdown, also increased depression levels.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.