Survey of Depression Detection using Social Networking Sites via Data Mining

Zafar, Aqsa; Chitnis, Sanjay

doi:10.1109/confluence47617.2020.9058189

Cited by 25 publications

(11 citation statements)

References 10 publications

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“…Patient Repository for EEG data and computational tools (PREDICT) 7 is a high volume publicly available dataset that contains EEG data. There exist several data repositories which contain imaging and patient-specific data.…”

Section: Ref Eeg Selection Criteria Subject Inclusion Criteriamentioning

confidence: 99%

“…Recently, automatic recognition of mental states and mental disorders has attracted considerable attention from computer vision and artificial intelligence community. While, earlier works mostly adopted Functional Magnetic Resonance Imaging (fMRI) [4], visual cues [5], self-rating scale [6] and social network analysis [7]. In recent years non-invasive sensors based devices, such as Electroencephalogram (EEG), have been widely employed in the literature.…”

Section: Introductionmentioning

confidence: 99%

“…Depresjon Data set link https://datasets.simula.no/depresjon/ #data-collection7 Patient Repository for EEG data and computational tools (PREDICT) Data set Link PatientRepositoryforEEGdataandcomputationaltools(PREDICT…”

mentioning

confidence: 99%

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EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Yasin¹,

Hussain²,

Aslan³

et al. 2020

Preprint

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A summary of clinical background of MDD and BD, Brain structure and EEG based brain activity,• EEG experimental protocols study for mental disorders diagnosis,• An extensive study of state-of-the-art shallow and deep neural networks models for EEG based clinical depression detection,• An extensive study of neural networks based approaches for Biploar Disorder diagnosis using EEG signals,• A discussion of state-of-the-art methods limitations and giving valuable recommendations for future research

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Section: Ref Eeg Selection Criteria Subject Inclusion Criteriamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

Yasin¹,

Hussain²,

Aslan³

et al. 2020

Preprint

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“…Sometimes mental illness has been attributed to the mass shooting in the US [30], which has taken numerous innocent lives. One of the common mental health problems is depression that is more dominant than other mental illness conditions worldwide [65]. Diagnosis of depression is usually a difficult task because depression detection needs a thorough and detailed psychological testing by experienced psychiatrists at an early stage [42] and it requires interviews, questionnaires, self-reports or testimony from friends and relatives.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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The ability to explain why the model produced results in such a way is an important problem, especially in the medical domain. Model explainability is important for building trust by providing insight into the model prediction. However, most existing machine learning methods provide no explainability, which is worrying. For instance, in the task of automatic depression prediction, most machine learning models lead to predictions that are obscure to humans. In this work, we propose explainable Multi-Aspect Depression Detection with Hierarchical Attention Network MDHAN, for automatic detection of depressed users on social media and explain the model prediction. We have considered user posts augmented with additional features from Twitter. Specifically, we encode user posts using two levels of attention mechanisms applied at the tweet-level and word-level, calculate each tweet and words’ importance, and capture semantic sequence features from the user timelines (posts). Our hierarchical attention model is developed in such a way that it can capture patterns that leads to explainable results. Our experiments show that MDHAN outperforms several popular and robust baseline methods, demonstrating the effectiveness of combining deep learning with multi-aspect features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media. MDHAN achieves excellent performance and ensures adequate evidence to explain the prediction.

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“…Sometimes mental illness has been attributed to the mass shooting in the US [26], which has taken numerous innocent lives. One of the common mental health problems is depression that is more dominant than other mental illness conditions worldwide [60]. The fatality risk of suicides in depressed people is 20 times higher than the general population [54].…”

Section: Introductionmentioning

confidence: 99%

Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media

Zogan,

Razzak,

Wang

et al. 2020

Preprint

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Social networks enable people to interact with one another by sharing information, sending messages, making friends, and having discussions, which generates massive amounts of data every day, popularly called as the user-generated content. This data is present in various forms such as images, text, videos, links, and others and reflects user behaviours including their mental states. It is challenging yet promising to automatically detect mental health problems from such data which is short, sparse and sometimes poorly phrased. However, there are efforts to automatically learn patterns using computational models on such user-generated content. While many previous works have largely studied the problem on a small-scale by assuming uni-modality of data which may not give us faithful results, we propose a novel scalable hybrid model that combines Bidirectional Gated Recurrent Units (BiGRUs) and Convolutional Neural Networks to detect depressed users on social media such as Twitter-based on multi-modal features. Specifically, we encode words in user posts using pre-trained word embeddings and BiGRUs to capture latent behavioural patterns, long-term dependencies, and correlation across the modalities, including semantic sequence features from the user timelines (posts). The CNN model then helps learn useful features. Our experiments show that our model outperforms several popular and strong baseline methods, demonstrating the effectiveness of combining deep learning with multi-modal features. We also show that our model helps improve predictive performance when detecting depression in users who are posting messages publicly on social media.

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Survey of Depression Detection using Social Networking Sites via Data Mining

Cited by 25 publications

References 10 publications

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

EEG based Major Depressive disorder and Bipolar disorder detection using Neural Networks: A review

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

Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media

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