# Quantifying the Suicidal Tendency on Social Media: A Survey

arXiv: https://arxiv.org/abs/2110.03663 · HTML: https://mlsanity.com/paper/2110.03663/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 136 references that are in the map.
- First posted on arXiv 4 Oct 2021.

## Builds on (most cited first, 29 of 29)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Universal Sentence Encoder](https://mlsanity.com/paper/1803.11175/) (2018)
- [GoEmotions: A Dataset of Fine-Grained Emotions](https://mlsanity.com/paper/2005.00547/) (2020)
- [Hierarchical Question-Image Co-Attention for Visual Question Answering](https://mlsanity.com/paper/1606.00061/) (2016)
- [Deep Learning Based Text Classification: A Comprehensive Review](https://mlsanity.com/paper/2004.03705/) (2020)
- [Dual Attention Networks for Multimodal Reasoning and Matching](https://mlsanity.com/paper/1611.00471/) (2016)
- [Depression and Self-Harm Risk Assessment in Online Forums](https://mlsanity.com/paper/1709.01848/) (2017)
- [Dreaddit: A Reddit Dataset for Stress Analysis in Social Media](https://mlsanity.com/paper/1911.00133/) (2019)
- [SMHD: A Large-Scale Resource for Exploring Online Language Usage for Multiple Mental Health Conditions](https://mlsanity.com/paper/1806.05258/) (2018)
- [Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction](https://mlsanity.com/paper/2102.09427/) (2021)
- [On the State of Social Media Data for Mental Health Research](https://mlsanity.com/paper/2011.05233/) (2020)
- [Latent Suicide Risk Detection on Microblog via Suicide-Oriented Word Embeddings and Layered Attention](https://mlsanity.com/paper/1910.12038/) (2019)
- [Instagram photos reveal predictive markers of depression](https://mlsanity.com/paper/1608.03282/) (2016)
- [Multilingual Offensive Language Identification for Low-resource Languages](https://mlsanity.com/paper/2105.05996/) (2021)
- [A Text Classification Framework for Simple and Effective Early Depression Detection Over Social Media Streams](https://mlsanity.com/paper/1905.08772/) (2019)
- [Affective Computing for Large-Scale Heterogeneous Multimedia Data: A Survey](https://mlsanity.com/paper/1911.05609/) (2019)
- [Understanding and Measuring Psychological Stress using Social Media](https://mlsanity.com/paper/1811.07430/) (2018)
- [Building and Using Personal Knowledge Graph to Improve Suicidal Ideation Detection on Social Media](https://mlsanity.com/paper/2012.09123/) (2020)
- [Learning Models for Suicide Prediction from Social Media Posts](https://mlsanity.com/paper/2105.03315/) (2021)
- [DepressionNet: A Novel Summarization Boosted Deep Framework for Depression Detection on Social Media](https://mlsanity.com/paper/2105.10878/) (2021)
- [PyPlutchik: visualising and comparing emotion-annotated corpora](https://mlsanity.com/paper/2105.04295/) (2021)
- [How Constraints Affect Content: The Case of Twitter's Switch from 140 to 280 Characters](https://mlsanity.com/paper/1804.02318/) (2018)
- [Explainable Depression Detection with Multi-Modalities Using a Hybrid Deep Learning Model on Social Media](https://mlsanity.com/paper/2007.02847/) (2020)

## Built on by (most cited first, 2 of 2)

- [CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts](https://mlsanity.com/paper/2207.04674/) (2022)
- [Data Augmentation for Mental Health Classification on Social Media](https://mlsanity.com/paper/2112.10064/) (2021)

## Abstract (arXiv)

Amid lockdown period more people express their feelings over social media platforms due to closed third-place and academic researchers have witnessed strong associations between the mental healthcare and social media posts. The stress for a brief period may lead to clinical depressions and the long-lasting traits of prevailing depressions can be life threatening with suicidal ideation as the possible outcome. The increasing concern towards the rise in number of suicide cases is because it is one of the leading cause of premature but preventable death. Recent studies have shown that mining social media data has helped in quantifying the suicidal tendency of users at risk. This potential manuscript elucidates the taxonomy of mental healthcare and highlights some recent attempts in examining the potential of quantifying suicidal tendency on social media data. This manuscript presents the classification of heterogeneous features from social media data and handling feature vector representation. Aiming to identify the new research directions and advances in the development of Machine Learning (ML) and Deep Learning (DL) based models, a quantitative synthesis and a qualitative review was carried out with corpus of over 77 potential research articles related to stress, depression and suicide risk from 2013 to 2021.