Early Depression Detection from Social Network Using Deep Learning Techniques

Shah, Faisal Muhammad; Ahmed, Farzad; Joy, Sajib Kumar Saha; Ahmed, Sifat; Sadek, Samir H.; Shil, Rimon; Kabir, Md. Hasanul

doi:10.1109/tensymp50017.2020.9231008

Cited by 61 publications

(14 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Of the 36 mental health classification studies, 14 (39%) studies incorporated external mental health data sets into data labeling procedures to support the ground truth of classification. External data set sources ranged from Wikipedia [ 36 ], Twitter [ 37 ], and AskAPatient [ 65 ] to formalized medical sources, including the Unified Medical Language System [ 31 ], the International Classification of Diseases, 10th Revision [ 48 ], and the Fifth Edition of the Diagnostic and Statistical Manual of Mental Disorders [ 48 , 69 ].…”

Section: Resultsmentioning

confidence: 99%

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Boettcher¹

2021

JMIR Ment Health

View full text Add to dashboard Cite

Background The study of depression and anxiety using publicly available social media data is a research activity that has grown considerably over the past decade. The discussion platform Reddit has become a popular social media data source in this nascent area of study, in part because of the unique ways in which the platform is facilitative of research. To date, no work has been done to synthesize existing studies on depression and anxiety using Reddit. Objective The objective of this review is to understand the scope and nature of research using Reddit as a primary data source for studying depression and anxiety. Methods A scoping review was conducted using the Arksey and O’Malley framework. MEDLINE, Embase, CINAHL, PsycINFO, PsycARTICLES, Scopus, ScienceDirect, IEEE Xplore, and ACM academic databases were searched. Inclusion criteria were developed using the participants, concept, and context framework outlined by the Joanna Briggs Institute Scoping Review Methodology Group. Eligible studies featured an analytic focus on depression or anxiety and used naturalistic written expressions from Reddit users as a primary data source. Results A total of 54 studies were included in the review. Tables and corresponding analyses delineate the key methodological features, including a comparatively larger focus on depression versus anxiety, an even split of original and premade data sets, a widespread analytic focus on classifying the mental health states of Reddit users, and practical implications that often recommend new methods of professionally delivered monitoring and outreach for Reddit users. Conclusions Studies of depression and anxiety using Reddit data are currently driven by a prevailing methodology that favors a technical, solution-based orientation. Researchers interested in advancing this research area will benefit from further consideration of conceptual issues surrounding the interpretation of Reddit data with the medical model of mental health. Further efforts are also needed to locate accountability and autonomy within practice implications, suggesting new forms of engagement with Reddit users.

show abstract

Section: Resultsmentioning

confidence: 99%

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Boettcher¹

2021

JMIR Ment Health

View full text Add to dashboard Cite

show abstract

“…We train two CNN models for baselines: CNNWithMax and MultiChannelCNN. • Bi-LSTM model that was used in a recent study for depression classification of text [23,24]. Fig.…”

Section: Resultsmentioning

confidence: 99%

“…More recently, a hybrid model has been introduced that analyzes user's textual posts [23]. In a hybrid model, Bidirectional Long Short-Term Memory (Bi-LSTM) with various word representation methods and were employed to detect depression, which gave good results.…”

Section: Depression Detectionmentioning

confidence: 99%

Attention-Based Bi-LSTM Model for Arabic Depression Classification

Almars¹

2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

Depression is a common mental health issue that affects a large percentage of people all around the world. Usually, people who suffer from this mood disorder have issues such as low concentration, dementia, mood swings, and even suicide. A social media platform like Twitter allows people to communicate as well as share photos and videos that reflect their moods. Therefore, the analysis of social media content provides insight into individual moods, including depression. Several studies have been conducted on depression detection in English and less in Arabic. The detection of depression from Arabic social media lags behind due the complexity of Arabic language and the lack of resources and techniques available. In this study, we performed a depression analysis on Arabic social media content to understand the feelings of the users. A bidirectional long short-term memory (Bi-LSTM) with an attention mechanism is presented to learn important hidden features for depression detection successfully. The proposed deep learning model combines an attention mechanism with a Bi-LSTM to simultaneously focus on discriminative features and learn significant word weights that contribute highly to depression detection. In order to evaluate our model, we collected a Twitter dataset of approximately 6000 tweets. The data labelling was done by manually classifying tweets as depressed or not depressed. Experimental results showed that the proposed model outperformed state-of-the-art machine learning models in detecting depression. The attention-based Bi-LSTM model achieved 0.83% accuracy on the depression detection task.

show abstract

“…Then, we setup Bi-LSTM model for the LIWC features after applying PCA for dimensionality reduction. We utilise BiLSTM similar to [65], as it's often applied to text features as at any time step, it handles information about the past and future which helps improving the module accurcy. We feed the time domain features extracted from the audio signal into a Dense Deep Network to preserve discriminative information.…”

Section: Modelmentioning

confidence: 99%

DeepSafety:Multi-level Audio-Text Feature Extraction and Fusion Approach for Violence Detection in Conversations

Anwar¹,

Kanjo²,

Anderez³

2022

Preprint

View full text Add to dashboard Cite

Natural Language Processing has recently made understanding human interaction easier, leading to improved sentimental analysis and behaviour prediction. However, the choice of words and vocal cues in conversations presents an underexplored rich source of natural language data for personal safety and crime prevention. When accompanied by audio analysis, it makes it possible to understand the context of a conversation, including the level of tension or rift between people. Building on existing work, we introduce a new information fusion approach that extracts and fuses multi-level features including verbal, vocal, and text as heterogeneous sources of information to detect the extent of violent behaviours in conversations. Our multilevel multimodel fusion framework integrates four types of information from raw audio signals including embeddings generated from both BERT and Bi-long short-term memory (LSTM) models along with the output of 2D CNN applied to Melfrequency Cepstrum (MFCC) as well as the output of audio Time-Domain dense layer. The embeddings are then passed to three-layer FC networks, which serve as a concatenated step. Our experimental setup revealed that the combination of the multi-level features from different modalities achieves better performance than using a single one with F1 Score=0.85. We expect that the findings derived from our method provides new approaches for violence detection in conversations.

show abstract

Early Depression Detection from Social Network Using Deep Learning Techniques

Cited by 61 publications

References 6 publications

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Studies of Depression and Anxiety Using Reddit as a Data Source: Scoping Review

Attention-Based Bi-LSTM Model for Arabic Depression Classification

DeepSafety:Multi-level Audio-Text Feature Extraction and Fusion Approach for Violence Detection in Conversations

Contact Info

Product

Resources

About