Inspiration is linked to various positive outcomes, such as increased creativity, productivity, and happiness. Although inspiration has great potential, there has been limited effort toward identifying content that is inspiring, as opposed to just engaging or positive. Additionally, most research has concentrated on Western data, with little attention paid to other cultures. This work is the first to study cross-cultural inspiration through machine learning methods. We aim to identify and analyze real and AI-generated cross-cultural inspiring posts. To this end, we compile and make publicly available the InspAIred dataset, which consists of 2,000 real inspiring posts, 2,000 real non-inspiring posts, and 2,000 generated inspiring posts evenly distributed across India and the UK. The real posts are sourced from Reddit, while the generated posts are created using the GPT-4 model. Using this dataset, we conduct extensive computational linguistic analyses to (1) compare inspiring content across cultures, (2) compare AI-generated inspiring posts to real inspiring posts, and (3) determine if detection models can accurately distinguish between inspiring content across cultures and data sources.

本研究是首次利用机器学习方法研究跨文化灵感，通过建立包括印度和英国的2,000个真实灵感帖子、2,000个真实非灵感帖子和2,000个生成灵感帖子的数据集，我们进行广泛的计算语言学分析，比较了不同文化中的灵感内容，并比较了AI生成的灵感帖子与真实灵感帖子的差异，同时确定了检测模型在跨文化和数据来源的灵感内容区分上的准确性。

跨文化社交媒体数据中的灵感检测与分析