SACRED：一个忠实标注的多媒体多模态多语言数据集，用于在线灵性中连接类型的分类
SACRED: A Faithful Annotated Multimedia Multimodal Multilingual Dataset for Classifying Connectedness Types in Online Spirituality
AI总结：
本文提出SACRED数据集，用于分类在线灵性中的连接类型，评估13种LLM及传统方法，发现DeepSeek-V3在抽象概念分类中表现优异，GPT-4o-mini在视觉任务中表现最佳，同时发现新的连接类型。
摘要：
在宗教和神学研究中，灵性因其超越文化并提供独特体验而受到广泛关注。然而，社会科学家往往依赖有限的数据集，这些数据集基本上无法在线获取。在本研究中，我们与社会科学家合作，开发了一个高质量的多媒体多模态数据集SACRED，其中分类的忠实性得到了保证。使用SACRED，我们评估了13种流行的LLM以及传统规则基础和微调方法的性能。结果表明，DeepSeek-V3模型在分类抽象概念（如Quora测试集中的79.19%准确率）方面表现良好，而GPT-4o-mini模型在视觉任务中超越了其他模型（63.99% F1分数）。据推测，这将是首个从在线灵性交流中获得的注释多模态数据集。我们的研究还发现了一种有价值的新连接类型，这对沟通科学研究具有重要意义。
英文摘要：
In religion and theology studies, spirituality has garnered significant research attention for the reason that it not only transcends culture but offers unique experience to each individual. However, social scientists often rely on limited datasets, which are basically unavailable online. In this study, we collaborated with social scientists to develop a high-quality multimedia multi-modal datasets, \textbf{SACRED}, in which the faithfulness of classification is guaranteed. Using \textbf{SACRED}, we evaluated the performance of 13 popular LLMs as well as traditional rule-based and fine-tuned approaches. The result suggests DeepSeek-V3 model performs well in classifying such abstract concepts (i.e., 79.19\% accuracy in the Quora test set), and the GPT-4o-mini model surpassed the other models in the vision tasks (63.99\% F1 score). Purportedly, this is the first annotated multi-modal dataset from online spirituality communication. Our study also found a new type of connectedness which is valuable for communication science studies.
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