Continuous Stress Detection Based on Social Media
IEEE Journal of Biomedical and Health Informatics, 2023•ieeexplore.ieee.org
Leveraging social media for stress detection has been growing attention in recent years.
Most relevant studies so far concentrated on training a stress detection model on the entire
data in a closed environment, and did not continuously incorporate new information into the
already established models but instead regularly reconstruct a new model from scratch. In
this study, we formulate a social media based continuous stress detection task with two
particular questions to be addressed:(1) when to adapt a learned stress detection model …
Most relevant studies so far concentrated on training a stress detection model on the entire
data in a closed environment, and did not continuously incorporate new information into the
already established models but instead regularly reconstruct a new model from scratch. In
this study, we formulate a social media based continuous stress detection task with two
particular questions to be addressed:(1) when to adapt a learned stress detection model …
Leveraging social media for stress detection has been growing attention in recent years. Most relevant studies so far concentrated on training a stress detection model on the entire data in a closed environment, and did not continuously incorporate new information into the already established models but instead regularly reconstruct a new model from scratch. In this study, we formulate a social media based continuous stress detection task with two particular questions to be addressed: (1) when to adapt a learned stress detection model? and (2) how to adapt a learned stress detection model? We design a protocol to quantify the conditions that trigger model's adaptation, and develop a layer-inheritance based knowledge distillation method to continually adapt the learned stress detection model to incoming data, while retaining the knowledge gained previously. The experimental results on a constructed dataset containing 69 users on Tencent Weibo validate the effectiveness of the proposed adaptive layer-inheritance based knowledge distillation method, achieving 86.32% and 91.56% of accuracy in 3-label and 2-label continuous stress detection. Implications and further possible improvements are also discussed at the end of the article.
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