Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

Zogan, Hamad; Razzak, Muhammad Imran; Wang, Xianzhi; Jameel, Shoaib; Xu, Guandong

doi:10.1007/s11280-021-00992-2

Cited by 86 publications

(41 citation statements)

References 59 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Hatoon et al applied linguistic inquiry and word count (LIWC) with a linear SVM to predict depression from Twitter data and obtained an accuracy of 82.50% [ 43 ]. Zogan et al employed multimodalities + word embedding (word2vec) with a bidirectional gated recurrent unit-convolutional neural network (BiGRU-CNN) for depression detection from Twitter data and achieved an accuracy of 85% [ 26 ]. In 2019, Kumar et al utilized a feature matrix with ensemble vote classification using RF, NB, and gradient boosting and obtained an accuracy of 85.09% [ 27 ].…”

Section: Resultsmentioning

confidence: 99%

“…Nevertheless, the previously proposed research works were not very successful in many different scenarios. Furthermore, mental health diagnosis is difficult to realize on a large scale because of the old data collection techniques such as interviews and questionnaires [ 26 ]. These approaches are unscalable to reach larger societal groups within a certain community.…”

Section: Related Studiesmentioning

confidence: 99%

See 1 more Smart Citation

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

Zeberga

Attique

Shah

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Related Studiesmentioning

confidence: 99%

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

Zeberga

Attique

Shah

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…MDHAN: Zogan et al [ 14 ] proposed MDHAN. They extracted semantic information using a hierarchical attention network and user behavior by a multimodal encoder.…”

Section: Resultsmentioning

confidence: 99%

“…Orabi et al [ 13 ] investigated the performance differences between recurrent neural network (RNN) models and CNN models in depression detection. Zogan et al [ 14 ] fused semantic and user behavior information for detecting depression, and proposed the multimodal depression detection with hierarchical attention network (MDHAN).…”

Section: Introductionmentioning

confidence: 99%

Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation

Cui¹,

Wang²,

Lin³

et al. 2022

JMIR Med Inform

View full text Add to dashboard Cite

Background Depression detection has recently received attention in the field of natural language processing. The task aims to detect users with depression based on their historical posts on social media. However, existing studies in this area use the entire historical posts of the users and select depression indicator posts. Moreover, these methods fail to effectively extract deep emotional semantic features or simply concatenate emotional representation. To solve this problem, we propose a model to extract deep emotional semantic features and select depression indicator posts based on the emotional states. Objective This study aims to develop an emotion-based reinforcement attention network for depression detection of users on social media. Methods The proposed model is composed of 2 components: the emotion extraction network, which is used to capture deep emotional semantic information, and the reinforcement learning (RL) attention network, which is used to select depression indicator posts based on the emotional states. Finally, we concatenated the output of these 2 parts and send them to the classification layer for depression detection. Results Experimental results of our model on the multimodal depression data set outperform the state-of-the-art baselines. Specifically, the proposed model achieved accuracy, precision, recall, and F1-score of 90.6%, 91.2%, 89.7%, and 90.4%, respectively. Conclusions The proposed model utilizes historical posts of users to effectively identify users’ depression tendencies. The experimental results show that the emotion extraction network and the RL selection layer based on emotional states can effectively improve the accuracy of detection. In addition, sentence-level attention layer can capture core posts.

show abstract

“…They achieved accuracies of 72%, 76%, and 90% for RNN, LSTM, and BiLSTM networks, respectively. Zogan et al (2022) developed explainable Multi-aspect depression detection utilizing social media analysis to detect depressed people. It employs a hierarchical attention network to extract users’ online activity and behavior characteristics.…”

Section: Related Workmentioning

confidence: 99%

An optimized deep learning approach for suicide detection through Arabic tweets

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

View full text Add to dashboard Cite

Many people worldwide suffer from mental illnesses such as major depressive disorder (MDD), which affect their thoughts, behavior, and quality of life. Suicide is regarded as the second leading cause of death among teenagers when treatment is not received. Twitter is a platform for expressing their emotions and thoughts about many subjects. Many studies, including this one, suggest using social media data to track depression and other mental illnesses. Even though Arabic is widely spoken and has a complex syntax, depressive detection methods have not been applied to the language. The Arabic tweets dataset should be scraped and annotated first. Then, a complete framework for categorizing tweet inputs into two classes (such as Normal or Suicide) is suggested in this study. The article also proposes an Arabic tweet preprocessing algorithm that contrasts lemmatization, stemming, and various lexical analysis methods. Experiments are conducted using Twitter data scraped from the Internet. Five different annotators have annotated the data. Performance metrics are reported on the suggested dataset using the latest Bidirectional Encoder Representations from Transformers (BERT) and Universal Sentence Encoder (USE) models. The measured performance metrics are balanced accuracy, specificity, F1-score, IoU, ROC, Youden Index, NPV, and weighted sum metric (WSM). Regarding USE models, the best-weighted sum metric (WSM) is 80.2%, and with regards to Arabic BERT models, the best WSM is 95.26%.

show abstract

Explainable depression detection with multi-aspect features using a hybrid deep learning model on social media

Cited by 86 publications

References 59 publications

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

A Novel Text Mining Approach for Mental Health Prediction Using Bi-LSTM and BERT Model

Emotion-Based Reinforcement Attention Network for Depression Detection on Social Media: Algorithm Development and Validation

An optimized deep learning approach for suicide detection through Arabic tweets

Contact Info

Product

Resources

About