OPTICS: ordering points to identify the clustering structure
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Earthquake shakes Twitter users: real-time event detection by social sensors
Proceedings of the 19th international conference on World wide web
Spatial Generalization and Aggregation of Massive Movement Data
IEEE Transactions on Visualization and Computer Graphics
Comparing twitter and traditional media using topic models
ECIR'11 Proceedings of the 33rd European conference on Advances in information retrieval
Recognizing named entities in tweets
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Simple supervised document geolocation with geodesic grids
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Part-of-speech tagging for Twitter: annotation, features, and experiments
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies: short papers - Volume 2
Spatiotemporal anomaly detection through visual analysis of geolocated Twitter messages
PACIFICVIS '12 Proceedings of the 2012 IEEE Pacific Visualization Symposium
Supervised text-based geolocation using language models on an adaptive grid
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
A visual analytics framework for spatio-temporal analysis and modelling
Data Mining and Knowledge Discovery
VAST '12 Proceedings of the 2012 IEEE Conference on Visual Analytics Science and Technology (VAST)
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Social microblogging services such as Twitter result in massive streams of georeferenced messages and geolocated status updates. This real-time source of information is invaluable for many application areas, in particular for disaster detection and response scenarios. Consequently, a considerable number of works has dealt with issues of their acquisition, analysis and visualization. Most of these works not only assume an appropriate percentage of georeferenced messages that allows for detecting relevant events for a specific region and time frame, but also that these geolocations are reasonably correct in representing places and times of the underlying spatio-temporal situation. In this paper, we review these two key assumption based on the results of applying a visual analytics approach to a dataset of georeferenced Tweets from Germany over eight months witnessing several large-scale flooding situations throughout the country. Our results confirm the potential of Twitter as a distributed 'social sensor' but at the same time highlight some caveats in interpreting immediate results. To overcome these limits we explore incorporating evidence from other data sources including further social media and mobile phone network metrics to detect, confirm and refine events with respect to location and time. We summarize the lessons learned from our initial analysis by proposing recommendations and outline possible future work directions.