社交媒体文本中的社会支持检测

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。