Semantics + filtering + search = twitcident. exploring information in social web streams

Abstract

Automatically filtering relevant information about a real-world incident from Social Web streams and making the information accessible and findable in the given context of the incident are non-trivial scientific challenges. In this paper, we engineer and evaluate solutions that analyze the semantics of Social Web data streams to solve these challenges. We introduce Twitcident, a framework and Web-based system for filtering, searching and analyzing information about real-world incidents or crises. Given an incident, our framework automatically starts tracking and filtering information that is relevant for the incident from Social Web streams and Twitter particularly. It enriches the semantics of streamed messages to profile incidents and to continuously improve and adapt the information filtering to the current temporal context. Faceted search and analytical tools allow people and emergency services to retrieve particular information fragments and overview and analyze the current situation as reported on the Social Web.