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• W4306173662 endingPage "109713" @default.
• W4306173662 startingPage "109713" @default.
• W4306173662 abstract "Psychological health, i.e., citizens’ emotional and mental well-being, is one of the most neglected public health issues. Depression is the most common mental health issue and the leading cause of suicide and self-injurious behavior. Clinical diagnosis of these mental health issues is expensive and also ignored due to social stigma and lack of awareness. Nowadays, online social media has become the preferred mode of communication, which people use to express their thoughts, feelings, and emotions. Hence, user-generated content from online social media can be leveraged for non-clinical mental health assessment and surveillance. Conventional machine learning and NLP techniques have been used for the automated detection of mental health issues using social media data for a very long time now. However, the objective of our research is to study the applications of deep learning techniques for early detection and non-clinical, predictive diagnosis of depression, self-harm, and suicide ideation from online social network content only. To the best of our knowledge, we did not find any systematic literature review that studies the applications of deep learning techniques in this domain. In order to address this research gap, we conducted a systematic literature review (SLR) of 96 relevant research studies published until date that have applied deep learning techniques for detecting depression and suicide or self-harm behavior from social media content. Our work comprehensively covers state-of-the-art w.r.t. techniques, features, datasets, and performance metrics, which can be of great value to researchers. We enumerate all the available datasets in this domain and discuss their characteristics. We also discuss the research gaps, challenges, and future research directions for advancing & catalyzing research in this domain. To the best of our knowledge, our study is the only and the most recent survey for this domain covering the latest research published until date. Based on our learnings from this review, we have also proposed a framework for mental health surveillance. We believe the findings of our work will be beneficial for researchers working in this domain." @default.
• W4306173662 created "2022-10-14" @default.
• W4306173662 creator A5039870532 @default.
• W4306173662 creator A5053492956 @default.
• W4306173662 date "2022-11-01" @default.
• W4306173662 modified "2023-09-30" @default.
• W4306173662 title "Deep learning techniques for suicide and depression detection from online social media: A scoping review" @default.
• W4306173662 cites W1537829113 @default.
• W4306173662 cites W1689711448 @default.
• W4306173662 cites W1901616594 @default.
• W4306173662 cites W2002016471 @default.
• W4306173662 cites W2009718376 @default.
• W4306173662 cites W2025768430 @default.
• W4306173662 cites W2079591709 @default.
• W4306173662 cites W2079735306 @default.
• W4306173662 cites W2080216641 @default.
• W4306173662 cites W2081249542 @default.
• W4306173662 cites W2106686523 @default.
• W4306173662 cites W2136922672 @default.
• W4306173662 cites W2162051395 @default.
• W4306173662 cites W2163459014 @default.
• W4306173662 cites W2250553926 @default.
• W4306173662 cites W2341172199 @default.
• W4306173662 cites W2405042511 @default.
• W4306173662 cites W2508051475 @default.
• W4306173662 cites W2512306956 @default.
• W4306173662 cites W2530421149 @default.
• W4306173662 cites W2592472466 @default.
• W4306173662 cites W2613843855 @default.
• W4306173662 cites W2619542576 @default.
• W4306173662 cites W2729451077 @default.
• W4306173662 cites W2741216199 @default.
• W4306173662 cites W2741937156 @default.
• W4306173662 cites W2744519557 @default.
• W4306173662 cites W2756736520 @default.
• W4306173662 cites W2765540322 @default.
• W4306173662 cites W2783557991 @default.
• W4306173662 cites W2800428573 @default.
• W4306173662 cites W2805240699 @default.
• W4306173662 cites W2805409402 @default.
• W4306173662 cites W2807710762 @default.
• W4306173662 cites W2865118579 @default.
• W4306173662 cites W2883860074 @default.
• W4306173662 cites W2889391310 @default.
• W4306173662 cites W2890929258 @default.
• W4306173662 cites W2898967081 @default.
• W4306173662 cites W2900152803 @default.
• W4306173662 cites W2905587047 @default.
• W4306173662 cites W2911378332 @default.
• W4306173662 cites W2912720349 @default.
• W4306173662 cites W2919115771 @default.
• W4306173662 cites W2927148761 @default.
• W4306173662 cites W2937603980 @default.
• W4306173662 cites W2947343305 @default.
• W4306173662 cites W2953413710 @default.
• W4306173662 cites W2963026768 @default.
• W4306173662 cites W2963261455 @default.
• W4306173662 cites W2970205254 @default.
• W4306173662 cites W2981984641 @default.
• W4306173662 cites W2985355520 @default.
• W4306173662 cites W2987607344 @default.
• W4306173662 cites W2994062161 @default.
• W4306173662 cites W2998535576 @default.
• W4306173662 cites W3005911073 @default.
• W4306173662 cites W3022185682 @default.
• W4306173662 cites W3023846141 @default.
• W4306173662 cites W3033857372 @default.
• W4306173662 cites W3033913896 @default.
• W4306173662 cites W3088352144 @default.
• W4306173662 cites W3092071453 @default.
• W4306173662 cites W3100359458 @default.
• W4306173662 cites W3101200480 @default.
• W4306173662 cites W3101267588 @default.
• W4306173662 cites W3102530385 @default.
• W4306173662 cites W3103163889 @default.
• W4306173662 cites W3105623222 @default.
• W4306173662 cites W3110065222 @default.
• W4306173662 cites W3111058005 @default.
• W4306173662 cites W3112184078 @default.
• W4306173662 cites W3115771810 @default.
• W4306173662 cites W3116085234 @default.
• W4306173662 cites W3138276986 @default.
• W4306173662 cites W3154426640 @default.
• W4306173662 cites W3163825730 @default.
• W4306173662 cites W3165299977 @default.
• W4306173662 cites W3166185110 @default.
• W4306173662 cites W3167447905 @default.
• W4306173662 cites W3169261221 @default.
• W4306173662 cites W3179564441 @default.
• W4306173662 cites W3197497981 @default.
• W4306173662 cites W4200103512 @default.
• W4306173662 cites W4205555191 @default.
• W4306173662 cites W4214489301 @default.
• W4306173662 cites W4221055733 @default.
• W4306173662 cites W4224326626 @default.
• W4306173662 doi "https://doi.org/10.1016/j.asoc.2022.109713" @default.
• W4306173662 hasPublicationYear "2022" @default.
• W4306173662 type Work @default.

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