Exploiting Twitter to fulfill information needs during incidents
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Abstract
How can Twitter be exploited to fulfill information needs of users on Twitter during incidents? In this Thesis this question wil be investigated and a strategy to locate tweets which fulfill information needs will be introduced. First, techniques are proposed to automatically detect incidents given an unstructured incident data source and subsequently tweets which report on these incidents are tracked. An interpretative analysis is carried out to derive and classify typical information needs during incidents. Furthermore, a metric is proposed to compute a tweet's relevance by measuring its informativeness and the user's trustworthiness. Finally, tweets are ranked according to this metric such that for each information need at a particular time the most relevant tweets can be found.