DMCA
R.: Semantics + Filtering + Search = Twitcident Exploring Information (2012)
| Venue: | in Social Web Streams Categories and Subject Descriptors. In: International Conference on Hypertext and Social |
| Citations: | 16 - 1 self |
BibTeX
@INPROCEEDINGS{Abel12r.:semantics,
author = {Fabian Abel and Claudia Hauff and Houben Ke Tao},
title = {R.: Semantics + Filtering + Search = Twitcident Exploring Information},
booktitle = {in Social Web Streams Categories and Subject Descriptors. In: International Conference on Hypertext and Social},
year = {2012}
}
Share
 |
 |
 |
 |
||
OpenURL
Abstract
Automatically filtering relevant information about a realworld 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. We put our Twitcident system into practice by connecting it to emergency broadcasting services in the Netherlands to allow for the retrieval of relevant information from Twitter streams for any incident that is reported by those services. We conduct large-scale experiments in which we evaluate (i) strategies for filtering relevant information for a given incident and (ii) search strategies for finding particular information pieces. Our results prove that the semantic enrichment offered by our framework leads to major and significant improvements of both the filtering and the search performance. A demonstration is available via:
Keyphrases
relevant information search twitcident exploring information social web stream search performance search strategy real-world incident particular information piece emergency broadcasting service analytical tool significant improvement semantic enrichment information filtering particular information fragment streamed message non-trivial scientific challenge current temporal context web-based system twitcident system realworld incident social web twitter stream large-scale experiment social web data stream current situation emergency service
