Social media channels enjoy many advantages over traditional media channels, such as ubiquity, mobility, immediacy, and seamless communication in reporting, covering and sharing real-world events, e.g., the Boston bombings, the NBA finals, and the U.S Presidential elections. Given these advantages, social media posts such as tweets can typically reflect events as they happen, in real-time. Despite these benefits, social media channels also tend to be noisy, chaotic, and overwhelming. As a result, the vast amount of noisy social media data poses tremendous challenges for conducting in-depth analysis, which is critical to applications for event playback, journalistic investigation, storytelling, etc. The purpose of this workshop is to bring together researchers that are working in a variety of areas that are all related to the larger problem of analyzing and understanding events using social media responses, to discuss: 1) what are the recently developed machine learning and data mining techniques that can be leveraged to address challenges in analyzing events using social media data, and 2) from challenges in analyzing events, what are the practical research directions in the machine learning and data mining community.