Title from publisher's bibliographic system (viewed on 04 Jul 2016).

Machine generated contents note: 1. Introduction; 2. Volume: data acquisition, storage, and retrieval; 3. Vagueness: natural language and semantics; 4. Variety: classification and clustering; 5. Virality: networks and information propagation; 6. Velocity: online methods and data streams; 7. Volunteers: humanitarian crowdsourcing; 8. Veracity: misinformation and credibility; 9. Validity: biases and pitfalls of social media data; 10. Visualization: crisis maps and beyond; 11. Values: privacy and ethics; 12. Conclusions and outlook.

Social media is an invaluable source of time-critical information during a crisis. However, emergency response and humanitarian relief organizations that would like to use this information struggle with an avalanche of social media messages that exceeds human capacity to process. Emergency managers, decision makers, and affected communities can make sense of social media through a combination of machine computation and human compassion - expressed by thousands of digital volunteers who publish, process, and summarize potentially life-saving information. This book brings together computational methods from many disciplines: natural language processing, semantic technologies, data mining, machine learning, network analysis, human-computer interaction, and information visualization, focusing on methods that are commonly used for processing social media messages under time-critical constraints, and offering more than 500 references to in-depth information.