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arXiv · 2411.02580

Social Support Detection from Social Media Texts

Zahra Ahani·Moein Shahiki Tash·Fazlourrahman Balouchzahi·Luis Ramos·Grigori Sidorov·Alexander Gelbukh
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Abstract

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.

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BibTeXRIS

Zahra Ahani, Moein Shahiki Tash, Fazlourrahman Balouchzahi, Luis Ramos, Grigori Sidorov, Alexander Gelbukh. 2024-11-04. Social Support Detection from Social Media Texts. https://arxiv.org/abs/2411.02580

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