Abstract
With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. These types of systems, which are mainly dependent on machine learning techniques, must be able to deal with obtaining the semantic and syntactic meaning of texts posted by users on social media. The data generated by users on social media contains unstructured and unpredictable content. Several systems based on machine learning and social media platforms have recently been introduced to identify health-related problems. However, the text representation and deep learning techniques employed provide only limited information and knowledge about the different texts posted by users. This is owing to a lack of long-term dependencies between each word in the entire text and a lack of proper exploitation of recent deep learning schemes. In this paper, we propose a novel framework to efficiently and effectively identify depression and anxiety-related posts while maintaining the contextual and semantic meaning of the words used in the whole corpus when applying bidirectional encoder representations from transformers (BERT). In addition, we propose a knowledge distillation technique, which is a recent technique for transferring knowledge from a large pretrained model (BERT) to a smaller model to boost performance and accuracy. We also devised our own data collection framework from Reddit and Twitter, which are the most common social media sites. Finally, we employed word2vec and BERT with Bi-LSTM to effectively analyze and detect depression and anxiety signs from social media posts. Our system surpasses other state-of-the-art methods and achieves an accuracy of 98% using the knowledge distillation technique.
Highlights
With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety
bidirectional long short-term memory (Bi-long short-term memory (LSTM)) and distilled bidirectional encoder representations from transformers (BERT) achieved higher classification accuracies of 96% and 98%, respectively. e main reason is that the student model mimics the teacher model that initially was trained on general text corpus such as Wikipedia and BookCorpus. erefore, distilled BERT obtains a competitive or even a superior performance when fine-tuned to our depression- and anxiety-related data domain. e learning of this small model from the bigger pretrained model in our proposed framework is termed knowledge distillation
We developed a strongly constructed framework for the detection of mental health problems using deep learning techniques such as BERT, Bi-LSTM, and a knowledge distillation based on social media content created by users. e proposed framework enhances the accuracy of smart healthcare systems to detect mental-health-related problems mainly depression and anxiety. is research work can be utilized to build a real-time system for early mentalhealth-related problem detection mainly based on user posts on Reddit and Twitter
Summary
With the current advancement in the Internet, there has been a growing demand for building intelligent and smart systems that can efficiently address the detection of health-related problems on social media, such as the detection of depression and anxiety. Several systems based on machine learning and social media platforms have recently been introduced to identify health-related problems. Many systems in the medical sector were designed to supervise the mental health of users using smartphones and wearable sensors as a data source [4, 7]. Such systems are not positioned to gather valuable information on demand keeping the freshness of data. Obtaining meaningful information from these data and efficiently examining them has become extremely challenging for traditional mental health problem detection systems
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