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Depression Detection Method Using Multimodal Social Media Data: An Integrative Review
- 1 School of Information Science and Engineering, Yanshan University
* Author to whom correspondence should be addressed.
Abstract
An increasing number of people are suffering from depression due to rising chronic stress levels. With the advent of Web 2.0, individuals are more inclined to express their emotions on social media, offering new opportunities for depression prediction. Researchers have developed various single-modal methods for early-stage depression prediction. Recently, multimodal social media data has been utilized to enhance the accuracy of depression detection methods. These methods primarily extract multidimensional information such as text, language, and images from social media users, integrating these diverse modes to assess the risk or severity of depression. This approach significantly improves the precision of depression prediction. However, the research is still in its early stages, with challenges such as limited datasets and many areas requiring further improvement. To aid researchers in better understanding and refining multimodal approaches, we conducted a review that summarizes emerging research directions in using multimodal techniques for depression prediction on social media. Additionally, this review compares different depression detection methods, datasets, and the various modalities used in multimodal approaches, analyzing their strengths and limitations. Finally, it offers suggestions for future research.
Keywords
depression detection, multimodal social media data, deep learning, feature fusion.
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Cite this article
Ma,J. (2024). Depression Detection Method Using Multimodal Social Media Data: An Integrative Review. Applied and Computational Engineering,106,44-51.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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