Detecting Inspiring Content on Social Media
It addresses the need for automated inspiration detection in social media for users and researchers, but is incremental as it applies existing methods to a new domain.
This paper tackles the problem of automatically detecting inspiring content on social media, a topic not previously researched in NLP, by analyzing posts to identify inspiring characteristics and topics, and releases a dataset of 11,600 Reddit posts with linguistic heuristics for detection.
Inspiration moves a person to see new possibilities and transforms the way they perceive their own potential. Inspiration has received little attention in psychology, and has not been researched before in the NLP community. To the best of our knowledge, this work is the first to study inspiration through machine learning methods. We aim to automatically detect inspiring content from social media data. To this end, we analyze social media posts to tease out what makes a post inspiring and what topics are inspiring. We release a dataset of 5,800 inspiring and 5,800 non-inspiring English-language public post unique ids collected from a dump of Reddit public posts made available by a third party and use linguistic heuristics to automatically detect which social media English-language posts are inspiring.