  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.
