# Social Support Detection from Social Media Texts

arXiv: https://arxiv.org/abs/2411.02580 · HTML: https://mlsanity.com/paper/2411.02580/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 4 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Crowdsourcing a Word-Emotion Association Lexicon](https://mlsanity.com/paper/1308.6297/) (2013)
- [Thou shalt not hate: Countering Online Hate Speech](https://mlsanity.com/paper/1808.04409/) (2018)
- [Hate Lingo: A Target-based Linguistic Analysis of Hate Speech in Social Media](https://mlsanity.com/paper/1804.04257/) (2018)
- [Detection of Hate Speech using BERT and Hate Speech Word Embedding with Deep Model](https://mlsanity.com/paper/2111.01515/) (2021)
- [Hope Speech Detection: A Computational Analysis of the Voice of Peace](https://mlsanity.com/paper/1909.12940/) (2019)
- [PolyHope: Two-Level Hope Speech Detection from Tweets](https://mlsanity.com/paper/2210.14136/) (2022)
- [Analyzing Emotional Trends from X platform using SenticNet: A Comparative Analysis with Cryptocurrency Price](https://mlsanity.com/paper/2405.03084/) (2024)
- [Utilizing deep learning models for the identification of enhancers and super-enhancers based on genomic and epigenomic features](https://mlsanity.com/paper/2401.07470/) (2024)
- [Stress Detection on Code-Mixed Texts in Dravidian Languages using Machine Learning](https://mlsanity.com/paper/2410.06428/) (2024)

## Built on by (most cited first, 3 of 3)

- [Improving statistical learning methods via features selection without replacement sampling and random projection](https://mlsanity.com/paper/2506.00053/) (2025)
- [Decoding Market Emotions in Cryptocurrency Tweets via Predictive Statement Classification with Machine Learning and Transformers](https://mlsanity.com/paper/2603.24933/) (2026)
- [Stigma and Support in Online Sexual Violence Narratives on Reddit](https://mlsanity.com/paper/2608.11433/) (2026)

## Abstract (arXiv)

Social support, conveyed through a multitude of interactions and platforms such as social media, plays a pivotal role in fostering a sense of belonging, aiding resilience in the face of challenges, and enhancing overall well-being. This paper introduces Social Support Detection (SSD) as a Natural language processing (NLP) task aimed at identifying supportive interactions within online communities. The study presents the task of Social Support Detection (SSD) in three subtasks: two binary classification tasks and one multiclass task, with labels detailed in the dataset section. We conducted experiments on a dataset comprising 10,000 YouTube comments. Traditional machine learning models were employed, utilizing various feature combinations that encompass linguistic, psycholinguistic, emotional, and sentiment information. Additionally, we experimented with neural network-based models using various word embeddings to enhance the performance of our models across these subtasks.The results reveal a prevalence of group-oriented support in online dialogues, reflecting broader societal patterns. The findings demonstrate the effectiveness of integrating psycholinguistic, emotional, and sentiment features with n-grams in detecting social support and distinguishing whether it is directed toward an individual or a group. The best results for different subtasks across all experiments range from 0.72 to 0.82.