Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

Li, Zepeng; Zhou, Jiawei; An, Zhengyi; Cheng, Wenchuan; Hu, Bin

doi:10.3390/e24040442

Cited by 13 publications

(7 citation statements)

References 36 publications

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“…Out of the nine studies, six studies [ 58 , 59 , 60 , 61 , 62 , 63 ] worked on sentiment analysis, while two studies [ 64 , 65 ] carried out work on depression detection. Study [ 66 ] approached suicide detection as their objective. LSTM and BiLSTM, as SDL or as HDL, were the main approaches used in studies [ 58 , 60 , 62 , 63 ], while studies [ 59 , 64 ] used statistical ML algorithms, and the authors of [ 65 ] implemented decision trees with pruning for classification.…”

Section: Discussionmentioning

confidence: 99%

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Singh

Fouda

et al. 2023

Diagnostics

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Depression is increasingly prevalent, leading to higher suicide risk. Depression detection and sentimental analysis of text inputs in cross-domain frameworks are challenging. Solo deep learning (SDL) and ensemble deep learning (EDL) models are not robust enough. Recently, attention mechanisms have been introduced in SDL. We hypothesize that attention-enabled EDL (aeEDL) architectures are superior compared to attention-not-enabled SDL (aneSDL) or aeSDL models. We designed EDL-based architectures with attention blocks to build eleven kinds of SDL model and five kinds of EDL model on four domain-specific datasets. We scientifically validated our models by comparing “seen” and “unseen” paradigms (SUP). We benchmarked our results against the SemEval (2016) sentimental dataset and established reliability tests. The mean increase in accuracy for EDL over their corresponding SDL components was 4.49%. Regarding the effect of attention block, the increase in the mean accuracy (AUC) of aeSDL over aneSDL was 2.58% (1.73%), and the increase in the mean accuracy (AUC) of aeEDL over aneEDL was 2.76% (2.80%). When comparing EDL vs. SDL for non-attention and attention, the mean aneEDL was greater than aneSDL by 4.82% (3.71%), and the mean aeEDL was greater than aeSDL by 5.06% (4.81%). For the benchmarking dataset (SemEval), the best-performing aeEDL model (ALBERT+BERT-BiLSTM) was superior to the best aeSDL (BERT-BiLSTM) model by 3.86%. Our scientific validation and robust design showed a difference of only 2.7% in SUP, thereby meeting the regulatory constraints. We validated all our hypotheses and further demonstrated that aeEDL is a very effective and generalized method for detecting symptoms of depression in cross-domain settings.

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Section: Discussionmentioning

confidence: 99%

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Singh

Fouda

et al. 2023

Diagnostics

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“…The language model utilized by ChatGPT is gpt-3.5-turbo 5 , which is one of the most advanced language models developed by OpenAI. ChatGPT accept a sequence of messages as an input and produce a message generated by the model as an output.…”

Section: ) Chatgptmentioning

confidence: 99%

“…In recent years, the proliferation of digital platforms and social media has provided an unprecedented opportunity to capture and analyze large-scale data related to mental health [2] [3]. Machine learning and Natural Language Processing (NLP) techniques have shown promise in detecting linguistic patterns and indicators of suicidal ideation in diverse textbased data sources, such as social media posts, online forums, and electronic health records [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

Ghanadian,

Nejadgholi,

Osman

2024

IEEE Access

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Suicidal ideation detection is a vital research area that holds great potential for improving mental health support systems. However, the sensitivity surrounding suicide-related data poses challenges in accessing large-scale, annotated datasets necessary for training effective machine learning models. To address this limitation, we introduce an innovative strategy that leverages the capabilities of generative AI models, such as ChatGPT, Flan-T5, and Llama, to create synthetic data for suicidal ideation detection. Our data generation approach is grounded in social factors extracted from psychology literature and aims to ensure coverage of essential information related to suicidal ideation. In our study, we benchmarked against state-of-the-art NLP classification models, specifically, those centered around the BERT family structures. When trained on the real-world dataset, UMD, these conventional models tend to yield F1-scores ranging from 0.75 to 0.87. Our synthetic data-driven method, informed by social factors, offers consistent F1scores of 0.82 for both models, suggesting that the richness of topics in synthetic data can bridge the performance gap across different model complexities. Most impressively, when we combined a mere 30% of the UMD dataset with our synthetic data, we witnessed a substantial increase in performance, achieving an F1-score of 0.88 on the UMD test set. Such results underscore the cost-effectiveness and potential of our approach in confronting major challenges in the field, such as data scarcity and the quest for diversity in data representation.

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“…The platform allows users to share and spread information instantly and interactively in the form of text, pictures, videos, and other multimedia. The number of monthly active users of Weibo increased to more than 500 million by 2022 [32], with nearly 80% of the platform being young users. Users have the option to hide personal information and share their thoughts openly on the platform, which has attracted many depressed people to share their suicide plans on the platform.…”

Section: Data Setmentioning

confidence: 99%

Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion

Liu

Shi

Jiang

2022

IJERPH

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Suicide has become a serious problem, and how to prevent suicide has become a very important research topic. Social media provides an ideal platform for monitoring suicidal ideation. This paper presents an integrated model for multidimensional information fusion. By integrating the best classification models determined by single and multiple features, different feature information is combined to better identify suicidal posts in online social media. This approach was assessed with a dataset formed from 40,222 posts annotated by Weibo. By integrating the best classification model of single features and multidimensional features, the proposed model ((BSC + RFS)-fs, WEC-fs) achieved 80.61% accuracy and a 79.20% F1-score. Other representative text information representation methods and demographic factors related to suicide may also be important predictors of suicide, which were not considered in this study. To the best of our knowledge, this is the good try that feature combination and ensemble algorithms have been fused to detect user-generated content with suicidal ideation. The findings suggest that feature combinations do not always work well, and that an appropriate combination strategy can make classification models work better. There are differences in the information contained in different functional carriers, and a targeted choice classification model may improve the detection rate of suicidal ideation.

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Deep Hierarchical Ensemble Model for Suicide Detection on Imbalanced Social Media Data

Cited by 13 publications

References 36 publications

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Attention-Enabled Ensemble Deep Learning Models and Their Validation for Depression Detection: A Domain Adoption Paradigm

Socially Aware Synthetic Data Generation for Suicidal Ideation Detection Using Large Language Models

Detecting Suicidal Ideation in Social Media: An Ensemble Method Based on Feature Fusion

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