  Social media are more than just a one-way communication channel. Data can be
collected, analyzed and contextualized to support disaster risk management.
However, disaster management agencies typically use such added-value
information to support only their own decisions. A feedback loop between
contextualized information and data suppliers would result in various
advantages. First, it could facilitate the near real-time communication of
early warnings derived from social media, linked to other sources of
information. Second, it could support the staff of aid organizations during
response operations. Based on the example of Hurricanes Harvey and Irma we show
how filtered, geolocated Tweets can be used for rapid damage assessment. We
claim that the next generation of big data analyses will have to generate
actionable information resulting from the application of advanced analytical
techniques. These applications could include the provision of social
media-based training data for algorithms designed to forecast actual cyclone
impacts or new socio-economic validation metrics for seasonal climate
forecasts.
