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.

Our work aims to facilitate by Thrash and Elliot as possessing three core such encounters by providing tools for automatic identification characteristics: evocation (i.e., it is triggered rather than of text content likely to be judged inspiring. We focus on willed), transcendence (i.e., it orients towards things outside inspiration in everyday content as judged by lay people, similar of and greater than the self), and approach motivation (i.e., it in spirit to early work by Hart who attempted to capture the energizes approach rather than avoidance [1]-[3]). Inspiration experience of inspiration in ordinary life [5], rather than "as if has two distinct stages: one an activation state that is more akin it were reserved for the gifted artist, the breakthrough scientist, to feeling and emotion, the second an urge to act.