mlsanityData to 7 Oct 2026

Understanding Mental Health Content on Social Media and Its Effect Towards Suicidal Ideation

arXiv 2501.09309 ↗First posted 16 Jan 2025cs.CY cs.AI cs.CLcited by 1

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Abstract (arXiv)

This review underscores the critical need for effective strategies to identify and support individuals with suicidal ideation, exploiting technological innovations in ML and DL to further suicide prevention efforts. The study details the application of these technologies in analyzing vast amounts of unstructured social media data to detect linguistic patterns, keywords, phrases, tones, and contextual cues associated with suicidal thoughts. It explores various ML and DL models like SVMs, CNNs, LSTM, neural networks, and their effectiveness in interpreting complex data patterns and emotional nuances within text data. The review discusses the potential of these technologies to serve as a life-saving tool by identifying at-risk individuals through their digital traces. Furthermore, it evaluates the real-world effectiveness, limitations, and ethical considerations of employing these technologies for suicide prevention, stressing the importance of responsible development and usage. The study aims to fill critical knowledge gaps by analyzing recent studies, methodologies, tools, and techniques in this field. It highlights the importance of synthesizing current literature to inform practical tools and suicide prevention efforts, guiding innovation in reliable, ethical systems for early intervention. This research synthesis evaluates the intersection of technology and mental health, advocating for the ethical and responsible application of ML, DL, and NLP to offer life-saving potential worldwide while addressing challenges like generalizability, biases, privacy, and the need for further research to ensure these technologies do not exacerbate existing inequities and harms.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 14 of its 87 references that are in the map. First posted on arXiv 16 Jan 2025.

Builds on

14 of 87 references.

References · 14

  1. 2015A Critical Review of Recurrent Neural Networks for Sequence Learningcited by 223
  2. 2014Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognitioncited by 111
  3. 2017Do Convolutional Neural Networks Learn Class Hierarchy?cited by 61
  4. 2017Word Affect Intensitiescited by 51
  5. 2021Deep Learning for Suicide and Depression Identification with Unsupervised Label Correctioncited by 45
  6. 2017Understanding Hidden Memories of Recurrent Neural Networkscited by 43
  7. 2019Exploring Interpretable LSTM Neural Networks over Multi-Variable Datacited by 42
  8. 2014Effective Use of Word Order for Text Categorization with Convolutional Neural Networkscited by 39
  9. 2017Emotion Intensities in Tweetscited by 32
  10. 2020Simplified Self-Attention for Transformer-based End-to-End Speech Recognitioncited by 20
  11. 2020Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Mediacited by 12
  12. 2021Learning Models for Suicide Prediction from Social Media Postscited by 10
2 more
  1. 2021An ensemble deep learning technique for detecting suicidal ideation from posts in social media platformscited by 4
  2. 2019Sequential Learning of Convolutional Features for Effective Text Classificationcited by 3

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Cited by year

Citing papers per year: 2025: 1.

1 paper.

  1. 2025Detecting Suicidal Ideation in Text with Interpretable Deep Learning: A CNN-BiGRU with Attention Mechanismcited by 0