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.

翻译：社会支持通过社交媒体等多种互动和平台传递，在培养归属感、帮助应对挑战时的韧性以及提升整体幸福感方面发挥着关键作用。本文提出将社会支持检测（SSD）作为一项自然语言处理（NLP）任务，旨在识别在线社区中的支持性互动。本研究将社会支持检测（SSD）任务分为三个子任务：两个二分类任务和一个多分类任务，具体标签详见数据集部分。我们在一个包含10,000条YouTube评论的数据集上进行了实验。研究采用了传统的机器学习模型，并利用了包含语言、心理语言学、情感和情绪信息的多种特征组合。此外，我们还尝试了基于神经网络的模型，使用多种词嵌入技术来提升模型在这些子任务上的性能。结果显示，在线对话中普遍存在群体导向的支持，这反映了更广泛的社会模式。研究结果表明，将心理语言学、情感和情绪特征与n-gram相结合，能有效检测社会支持并区分其指向个体还是群体。所有实验中，不同子任务的最佳结果范围在0.72到0.82之间。