Predicting Social Network Users with Depression from Simulated Temporal Data

Wongkoblap, Akkapon; Vadillo, Miguel A.; Ćurčin, Vasa

doi:10.1109/eurocon.2019.8861514

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…The approaches proposed in this work and the state-of-the-art approach used the same REDDIT to train and evaluate the suggested models. Kumar et al [8] 2019 100 users 1 (depression) 3 ML models Acc= 85% Tarik et al [9] 2019 Not mentioned 1 (depression) 2 DL models Acc= 74% Hussain et al [10] 2019 Not mentioned 2 (depression & anxiety) 3 ML models F1-score= 0.84 Wong et al [11] 2019 Not mentioned 1 (depression) 2 DL models -Rezaii et al [12] 2019 40 users 1 (depression) 2 NLP techniques Acc= 90% Inkpen et al [13] 2019 Not mentioned 2 (depression and PTSD) 1 DL model Acc= 88% Thorstad et al [6] 2019 All REDDIT 4 (all mental disorders) 1 ML model F1-score= 0.77 Trifan et al [14] 2020 Not mentioned 1 (depression) 3 ML models F1-score= 0.72 Jiang et al [15] 2020 Not mentioned 4 (all mental disorders) 1 DL model F1-score= 0.64 Alghamdi et al [16] 2020 Not mentioned 1 (depression) 6 ML models Acc= 80% Birnbaum et al [17] 2020 223 users 1 (depression) 2 ML models Acc= 77% Chatterjee et al [18] 2021 Not mentioned 1 (depression) 1 ML models Acc= 76% Ren et al [19] 2021 Not mentioned 1 (depression) 1 DL models Acc= 91% Shaoxiong et al [20] 2022 All REDDIT 1 (depression) 2 EL models Acc= 75% Nalini. L [21] 2022 Not mentioned Not mentioned 3 ML models Not mentioned Tufail [22] 2023 Not mentioned 1 (depression) 1 DL model Acc= 64% Koushik et al [23] 2023 Not mentioned 1 (depression) 1 ML & 2 DL Acc= 60% Yicheng et al [25] 2023 Not mentioned 1 (depression) 1 Time series approach -Helmy et al [27] 2024 70,000 tweets 1 (depression) 5 ML models Acc=92% Dhariwal [28] 2024 Small healthcare dataset The proposed work aims at the early detection and even the prediction of potential future mental disorder from social media data.…”

Section: ) Language Models Resultsmentioning

confidence: 99%

“…In 2019 also, Tarik et al [9] went to deep learning to automatically detect depression from social media. The authors used Low-Short Term Memory (LSTM) combined with Gated Recurrent Units (GRUs) to perform the task.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Wong et al [11] used the posts on social media also to detect mental health issues. Their study focused on comparing training a predictive model with multiple instance learning (MIL) trained via Long-Short Term Memory (LSTM), with the MIL trained via Convolutional Neural Networks (CNNs).…”

Section: Literature Reviewmentioning

confidence: 99%

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Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Abdullah,

Negied

2024

IEEE Access

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Social media platforms are used widely by all people to express their feelings, opinions, and emotional states. Billions of people worldwide use them daily to share what they think and feel in their posts. Amongst all social media available platforms, Facebook only contains around three billion personal accounts. In this work Reddit dataset is used to automatically detect mental illness from social media posts. This study is not only limited to early detection of already existing mental illness or disorder like depression and anxiety from social posts, but also and most importantly the study is extended to predict successfully potential mental illness that would happen in future. This study deploys Nineteen different models to study the capability of them in detecting and predicting mental disorders from social media posts. Some of the deployed models are classical machine learning classifiers, some are ensemble learning models, and the rest are large language models (LLMs). Six machine learning classifiers were used in this work for the automatic detection and prediction of mental illness and logistic regression proved to be the best amongst other classifiers in this task. Nine Ensemble methods were also used and examined. Amongst the Nine ensemble learning models VC2, Light GBM, Bagging estimator, and XGBoost proved to be superior in this task. Four large language models were also used and examined for the same task. RoBERTa and OpenAI GPT proved to outperform the rest of models in this task. All those models were built, trained, tested, and compared with previous work in literature to get the best possible results. The study covers the main four mental disorders which are ADHD, Anxiety, Bipolar, and Depression. The work proposed in this paper succeeded in outperforming the results in literature in terms of number of addressed mental disorders, number of models used and tested, and dataset size used to validate results. The proposed work also outperformed the only attempt in literature that addressed all mental disorders in results of detection and prediction noticeably. This work achieved the detection of already existing mental disorders F1-score of 0.80 from clinical data and of 0.52 from non-clinical data, and it achieved a prediction of future mental disorder F1-score of 0.43 from non-clinical data.

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Section: ) Language Models Resultsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Detection and prediction of Future Mental disorder from Social Media Data using Machine Learning, Ensemble Learning, and Large Language Models.

Abdullah,

Negied

2024

IEEE Access

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“…In the work of Ricard et al [ 62 ], the mean and SD of the text-based scores for the most recent k posts were utilized as features in their model training, with k as a hyperparameter tuned through cross-validation. Wongkoblap et al [ 57 ] created a predictive model and used n-fold cross-validation to report the performance of the model. The results of the evaluation are presented with accuracy, precision, recall, and the f1-score achieved by the model after training and testing with five-fold cross-validation.…”

Section: Resultsmentioning

confidence: 99%

Detecting Depression Signs on Social Media: A Systematic Literature Review

et al. 2022

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Among mental health diseases, depression is one of the most severe, as it often leads to suicide; due to this, it is important to identify and summarize existing evidence concerning depression sign detection research on social media using the data provided by users. This review examines aspects of primary studies exploring depression detection from social media submissions (from 2016 to mid-2021). The search for primary studies was conducted in five digital libraries: ACM Digital Library, IEEE Xplore Digital Library, SpringerLink, Science Direct, and PubMed, as well as on the search engine Google Scholar to broaden the results. Extracting and synthesizing the data from each paper was the main activity of this work. Thirty-four primary studies were analyzed and evaluated. Twitter was the most studied social media for depression sign detection. Word embedding was the most prominent linguistic feature extraction method. Support vector machine (SVM) was the most used machine-learning algorithm. Similarly, the most popular computing tool was from Python libraries. Finally, cross-validation (CV) was the most common statistical analysis method used to evaluate the results obtained. Using social media along with computing tools and classification methods contributes to current efforts in public healthcare to detect signs of depression from sources close to patients.

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“…There are dataset available for detecting depression task from social media platform such as Twitter (Leis et al, 2019;Arora and Arora, 2019;Yazdavar et al, 2020;de Jesús Titla-Tlatelpa et al, 2021;Chiong et al, 2021;Safa et al, 2021), Reddit (de Jesús Titla-Tlatelpa et al, 2021Ríssola et al, 2019;Tadesse et al, 2019;Burdisso et al, 2019;Martínez-Castaño et al, 2020), Facebook (Chiong et al, 2021;Wongkoblap et al, 2019;Wu et al, 2020;Yang et al, 2020), Instagram (Mann et al, 2020;Ricard et al, 2018), Weibo (Li et al, 2018;Yu et al, 2021) and NHANES, K-NHANES (Oh et al, 2019). The linguistic feature extraction methods used for detecting depression signs on social media such as Word embedding (Mandelbaum and Shalev, 2016), N-grams (Cavnar et al, 1994), Tokenization (Webster andKit, 1992), Bag of words (Zhang et al, 2010;Aho and Ullman, 1972), Stemming (Jivani et al, 2011), Emotion analysis (Leis et al, 2019;Shen et al, 2017;Chen et al, 2018), Part-of-Speech (POS) tagging (Chiong et al, 2021;Wu et al, 2020), Behavior features (Wu et al, 2020) and Sentiment polarity (Leis et al, 2019;Ríssola et al, 2019).…”

Section: Introductionmentioning

confidence: 99%