Crises and situations of mass emergency such as earthquakes and hurricanes cause massive damage to lives and property. Crisis response is an essential task to mitigate the impact of a crisis. An effective response to a crisis necessitates information gathering and analysis. Traditionally this process has been restricted to the information collected by first responders on the ground in the affected region or official agencies such as local governments involved in the response. However, the ubiquity of mobile devices has empowered people to publish information during crisis, such as the damage caused by a hurricane through social media. Social media has thus emerged as an important channel of information which can be leveraged to improve crisis response. Twitter is an example of a popular medium which has been used during recent crises. But, the data is noisy and uncurated, and it has high volume and high velocity. In this work, we study four key problems in the use of social media for crisis response: effective monitoring and analysis of high volume crisis tweets, detecting crisis events automatically in streaming data, identifying users who can be followed to effectively monitor crisis, and finally understanding user behavior during crisis to detect tweets inside the crisis region. To address these problems we propose two systems which assist a disaster responder or an analyst to collaboratively collect tweets related to crisis and analyze it using visual analytics to identify interesting regions, topics, and users involved in the discussion. We present a novel approach to detecting events automatically in a noisy, high volume Twitter stream. We introduce a method to tackle information overload by identifying information leaders during a crisis, who can be followed for efficient crisis monitoring.