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Social sensing can provide useful information to help detect, manage and solve problems related to people’s lifes and physical surroundings. Because of the huge amount of content generated on social media, the problem of social sensing is the varying quality of data, so it is necessary to filter out the irrelevant content returned by search requests. The goal of our research is to develop a knowledge-based system that is able to analyse tweets in Spanish to select the most salient posts with respect to a given problem (e.g. flood events). The main contribution of this article is to describe a measure that computes the salience of tweets by integrating the text-oriented perception of the problem with the network-oriented impact of the message. The system was tested with the natural disaster of a DANA that struck Spain in September 2019.
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