An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms

• URL: http://arxiv.org/abs/2112.10609v1
• Date: Fri, 17 Dec 2021 15:34:03 GMT
• Title: An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms
• Authors: Shini Renjith, Annie Abraham, Surya B.Jyothi, Lekshmi Chandran, Jincy Thomson
• Abstract summary: This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect suicidal intentions. The proposed model demonstrated an accuracy of 90.3 percent and an F1-score of 92.6 percent.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Suicidal ideation detection from social media is an evolving research with great challenges. Many of the people who have the tendency to suicide share their thoughts and opinions through social media platforms. As part of many researches it is observed that the publicly available posts from social media contain valuable criteria to effectively detect individuals with suicidal thoughts. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide. This can be achieved by identifying the sudden changes in a user behavior automatically. Natural language processing techniques can be used to collect behavioral and textual features from social media interactions and these features can be passed to a specially designed framework to detect anomalies in human interactions that are indicators of suicidal intentions. We can achieve fast detection of suicidal ideation using deep learning and/or machine learning based classification approaches. For such a purpose, we can employ the combination of LSTM and CNN models to detect such emotions from posts of the users. In order to improve the accuracy, some approaches like using more data for training, using attention model to improve the efficiency of existing models etc. could be done. This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect any underlying suicidal intentions. During evaluations, the proposed model demonstrated an accuracy of 90.3 percent and an F1-score of 92.6 percent, which is greater than the baseline models.

Related papers

• Leveraging Large Language Models for Suicide Detection on Social Media with Limited Labels [3.1399304968349186]
  This paper explores the use of Large Language Models (LLMs) to automatically detect suicidal content in text-based social media posts. We develop an ensemble approach involving prompting with Qwen2-72B-Instruct, and using fine-tuned models such as Llama3-8B, Llama3.1-8B, and Gemma2-9B. Experimental results show that the ensemble model significantly improves the detection accuracy, by 5% points compared with the individual models.
  arXiv  Detail & Related papers  (2024-10-06T14:45:01Z)
• Modeling User Preferences via Brain-Computer Interfacing [54.3727087164445]
  We use Brain-Computer Interfacing technology to infer users' preferences, their attentional correlates towards visual content, and their associations with affective experience. We link these to relevant applications, such as information retrieval, personalized steering of generative models, and crowdsourcing population estimates of affective experiences.
  arXiv  Detail & Related papers  (2024-05-15T20:41:46Z)
• SOS-1K: A Fine-grained Suicide Risk Classification Dataset for Chinese Social Media Analysis [22.709733830774788]
  This study presents a Chinese social media dataset designed for fine-grained suicide risk classification. Seven pre-trained models were evaluated in two tasks: high and low suicide risk, and fine-grained suicide risk classification on a level of 0 to 10. Deep learning models show good performance in distinguishing between high and low suicide risk, with the best model achieving an F1 score of 88.39%.
  arXiv  Detail & Related papers  (2024-04-19T06:58:51Z)
• Non-Invasive Suicide Risk Prediction Through Speech Analysis [74.8396086718266]
  We present a non-invasive, speech-based approach for automatic suicide risk assessment. We extract three sets of features, including wav2vec, interpretable speech and acoustic features, and deep learning-based spectral representations. Our most effective speech model achieves a balanced accuracy of $66.2,%$.
  arXiv  Detail & Related papers  (2024-04-18T12:33:57Z)
• Conceptualizing Suicidal Behavior: Utilizing Explanations of Predicted Outcomes to Analyze Longitudinal Social Media Data [2.76101452577748]
  The COVID-19 pandemic has escalated mental health crises worldwide. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression. As these conditions develop, signs of suicidal ideation may manifest in social media interactions.
  arXiv  Detail & Related papers  (2023-12-13T17:15:12Z)
• Depression detection in social media posts using affective and social norm features [84.12658971655253]
  We propose a deep architecture for depression detection from social media posts. We incorporate profanity and morality features of posts and words in our architecture using a late fusion scheme. The inclusion of the proposed features yields state-of-the-art results in both settings.
  arXiv  Detail & Related papers  (2023-03-24T21:26:27Z)
• Order-Disorder: Imitation Adversarial Attacks for Black-box Neural Ranking Models [48.93128542994217]
  We propose an imitation adversarial attack on black-box neural passage ranking models. We show that the target passage ranking model can be transparentized and imitated by enumerating critical queries/candidates. We also propose an innovative gradient-based attack method, empowered by the pairwise objective function, to generate adversarial triggers.
  arXiv  Detail & Related papers  (2022-09-14T09:10:07Z)
• A Quantitative and Qualitative Analysis of Suicide Ideation Detection using Deep Learning [5.192118773220605]
  This paper replicated competitive social media-based suicidality detection/prediction models. We evaluated the feasibility of detecting suicidal ideation using multiple datasets and different state-of-the-art deep learning models.
  arXiv  Detail & Related papers  (2022-06-17T10:23:37Z)
• Neurosymbolic hybrid approach to driver collision warning [64.02492460600905]
  There are two main algorithmic approaches to autonomous driving systems. Deep learning alone has achieved state-of-the-art results in many areas. But sometimes it can be very difficult to debug if the deep learning model doesn't work.
  arXiv  Detail & Related papers  (2022-03-28T20:29:50Z)
• Detecting Potentially Harmful and Protective Suicide-related Content on Twitter: A Machine Learning Approach [0.1582078748632554]
  We apply machine learning methods to automatically label large quantities of Twitter data. Two deep learning models achieved the best performance in two classification tasks. This work enables future large-scale investigations on harmful and protective effects of various kinds of social media content on suicide rates and on help-seeking behavior.
  arXiv  Detail & Related papers  (2021-12-09T09:35:48Z)
• Learning Language and Multimodal Privacy-Preserving Markers of Mood from Mobile Data [74.60507696087966]
  Mental health conditions remain underdiagnosed even in countries with common access to advanced medical care. One promising data source to help monitor human behavior is daily smartphone usage. We study behavioral markers of daily mood using a recent dataset of mobile behaviors from adolescent populations at high risk of suicidal behaviors.
  arXiv  Detail & Related papers  (2021-06-24T17:46:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.