融合符号知识与神经网络，动态捕捉疫情期社交媒体心理舆情。
A Domain-Agnostic Neurosymbolic Approach for Big Social Data Analysis: Evaluating Mental Health Sentiment on Social Media during COVID-19
- 结合神经网络与符号知识库，动态适应语言演化。
- 在120亿推文数据上F1超92%，优于纯数据驱动模型。
- 响应快、算力低，适合实时健康监测场景。
通过社交媒体监测公众情绪在新冠疫情等健康危机中具有潜在价值。但传统基于频率和数据驱动的神经网络方法，因语言持续演变而可能遗漏新出现的相关内容。人工构建的符号知识源（如标准语和俚语词典）可提升对动态语言中社交信号的识别能力。本文提出一种神经符号方法，将神经网络与符号知识源融合，增强对与新冠肺炎相关心理健康推文的检测与解读。该方法在约120亿条推文、250万条子版块数据及70万篇新闻文章组成的大型语料库上，结合多个知识图谱进行评估。结果表明，该方法能动态适应语言变化，在多项指标上超越纯数据驱动模型，F1得分超过92%。同时，其对新数据的适应速度更快，计算开销远低于微调大型语言模型（LLMs）。研究证明神经符号方法在动态环境中解释文本（如公共卫生监测）中的有效性。
原文摘要 · Abstract (English)
Monitoring public sentiment via social media is potentially helpful during health crises such as the COVID-19 pandemic. However, traditional frequency-based, data-driven neural network-based approaches can miss newly relevant content due to the evolving nature of language in a dynamically evolving environment. Human-curated symbolic knowledge sources, such as lexicons for standard language and slang terms, can potentially elevate social media signals in evolving language. We introduce a neurosymbolic method that integrates neural networks with symbolic knowledge sources, enhancing the detection and interpretation of mental health-related tweets relevant to COVID-19. Our method was evaluated using a corpus of large datasets (approximately 12 billion tweets, 2.5 million subreddit data, and 700k news articles) and multiple knowledge graphs. This method dynamically adapts to evolving language, outperforming purely data-driven models with an F1 score exceeding 92\%. This approach also showed faster adaptation to new data and lower computational demands than fine-tuning pre-trained large language models (LLMs). This study demonstrates the benefit of neurosymbolic methods in interpreting text in a dynamic environment for tasks such as health surveillance.
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