Proceedings of the Annual Meeting of the Cognitive Science Society

The rapid adoption of social media by billions of people from all over the world has unleashed unprecedentedopportunities for marketers and cognitive scientists to better understand why some message become popular while other diequickly. We designed a novel technique for automatically learning to differentiate popular tweets from unpopular ones and topredict how popular a given tweet will become in a given target audience. To demonstrate the effectiveness of our approach,we applied it to real world data collected from six social media messaging campaigns run by a variety of marketing as wellas non-profit organizations including Proctor and Gamble’s Always Campaign. The studies showed that our approach can behighly effective (achieving accuracy scores from 92% to 99%) for automatically learning what makes a message popular in anygiven group as well as for automatically predicting how popular a message will be in a given target audience.