A Multimodal Framework for Depression Detection During Covid-19 via Harvesting Social Media
Bibliographic Data
| ID | 22108802 |
|---|---|
| Authors | Ashutosh Anshul (0000-0001-8883-7494, Indian Institute of Technology Indore), Gumpili Sai Pranav (Indian Institute of Technology Indore), G Pranav (Indian Institute of Technology Indore), Mohammad Zia Ur Rehman (0000-0001-6374-8102, Indian Institute of Technology Indore), Nagendra Kumar (0000-0002-8292-7947, Indian Institute of Technology Indore) |
| Year | 2024 |
| Volume | 11 |
| Issue | 2 |
| Pages | 2872-2888 |
| Publication date | 2024-04-01 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | IEEE Transactions on Computational Social Systems (JOURNAL) |
| Journal identifiers | ISSN: 2329-924X • E-ISSN: 2373-7476 |
| Publisher | Institute of Electrical and Electronics Engineers (IEEE) (PUBLISHER) |
| DOI | 10.1109/tcss.2023.3309229 |
| OpenAlex | W4386453710 |
| Language | EN |
| Citations received | 2 |
| References cited | 56 |
The recent coronavirus disease (COVID-19) has become a pandemic and has affected the entire globe. During the pandemic, we have observed a spike in cases related to mental health, such as anxiety, stress, and depression. Depression significantly influences most diseases worldwide, making it difficult to detect mental health conditions in people due to unawareness and unwillingness to consult a doctor. However, nowadays, people extensively use online social media platforms to express their emotions and thoughts. Hence, social media platforms are now becoming a large data source that can be utilized for detecting depression and mental illness. However, the existing approaches often overlook data sparsity in tweets and the multimodal aspects of social media. In this article, we propose a novel multimodal framework that combines textual, user-specific, and image analysis to detect depression among social media users. To provide enough context about the user’s emotional state, we propose the following: 1) an extrinsic feature by harnessing the URLs present in tweets and 2) extracting textual content present in images posted in tweets. We also extract five sets of features belonging to different modalities to describe a user. In addition, we introduce a deep learning model, the visual neural network (VNN), to generate embeddings of user-posted images, which are used to create the visual feature vector for prediction. We contribute a curated COVID-19 dataset of depressed and nondepressed users for research purposes and demonstrate the effectiveness of our model in detecting depression during the COVID-19 outbreak. Our model outperforms the existing state-of-the-art methods over a benchmark dataset by 2%–8% and produces promising results on the COVID-19 dataset. Our analysis highlights the impact of each modality and provides valuable insights into users’ mental and emotional states
Anxiety · Data science · Disease · Internet privacy · Machine learning · Mental health · Modalities · Pandemic · Psychiatry · Sentiment analysis · Social media · Sociology · Topic model · Visualization · World Wide Web · Computer Science · Digital Mental Health Interventions · Medicine · Mental Health via Writing · Psychology · Sentiment Analysis and Opinion Mining · Artificial Intelligence
Content Analysis
Mental Health, Risk Factors, and Social Media Use During the Covid-19 Epidemic and Cordon Sanitaire Among the Community and Health Professionals in Wuhan, China
Crowdsourcing a Word–emotion Association Lexicon
Sentiment of Emojis
Generalized anxiety disorder, depressive symptoms and sleep quality during Covid-19 outbreak in China
Early depression detection in social media based on deep learning and underlying emotions
A Hybrid Deep Neural Network for Multimodal Personalized Hashtag Recommendation
Detecting Community Depression Dynamics Due to Covid-19 Pandemic in Australia
Hashtag recommendation for enhancing the popularity of social media posts
| Citations per year | 1 |
|---|---|
| Citation span | 2024 - 2024 (1) |
| Highly cited | No |