Every day, people take action, trying to achieve their personal, high-order goals. People decide what actions to take based on their personal experience, knowledge and gut instinct. While this leads to positive outcomes for some people, many others do not have the necessary experience, knowledge and instinct to make good decisions. What if, rather than making decisions based solely on their own personal experience, people could take advantage of the reported experiences of hundreds of millions of other people?

In this paper, we investigate the feasibility of mining the relationship between actions and their outcomes from the aggregated timelines of individuals posting experiential microblog reports. our contributions include an architecture for extracting action-outcome relationships from social media data, techniques for identifying experiential social media messages and converting them to event timelines, and an analysis and evaluation of action-outcome extraction in case studies.