The task of mining pros and cons from actions has applications in decision support. Given an action query and social media data, we mine related pros and cons of the action via extracting significant events as potential outcomes of the action. We propose using actions and characteristics to select relevant messages, and adjective vectors to establish similarity among adjectives. We introduce SS to select event headlines, and to rank them in the final pros-and-cons table. Our results on two data sets indicate our algorithm can generate more meaningful pros and cons than an existing algorithm.