Cross-cultural Inspiration Detection and Analysis in Real and LLM-generated Social Media Data
It addresses the lack of cross-cultural studies in inspiration detection, which is incremental as it applies existing methods to new cultural data.
This work tackled the problem of identifying and analyzing cross-cultural inspiring content in social media by compiling the InspAIred dataset with 6,000 posts from India and the UK, including real and AI-generated ones, and conducted analyses to compare inspiring content across cultures and data sources.
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.