Combating spam in tagging systems
AIRWeb '07 Proceedings of the 3rd international workshop on Adversarial information retrieval on the web
Towards automatic extraction of event and place semantics from flickr tags
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
The influence limiter: provably manipulation-resistant recommender systems
Proceedings of the 2007 ACM conference on Recommender systems
Proceedings of the 2009 International Workshop on Location Based Social Networks
17th SIGSPATIAL International Conference on Advances in Geographic Information Systems
Conceptualization of place via spatial clustering and co-occurrence analysis
Proceedings of the 2009 International Workshop on Location Based Social Networks
Proceedings of the 2009 International Workshop on Location Based Social Networks
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Detection, classification and visualization of place-triggered geotagged tweets
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
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Social reporting is based on the idea that the members of a location-based social network observe real-world events and publish reports about their observations. Application scenarios include crisis management, bird watching or even some sorts of mobile games. A major issue in social reporting is the quality of the reports. We propose an approach to the quality problem that is based on the reciprocal confirmation of reports by other reports. This contrasts with approaches that require users to verify reports, that is, to explicitly evaluate their veridicality. We propose to use spatio-termporal proximity as a first criterion for confirmation and social distance as a second one. By combining these two measures we construct a graph containing the reports as nodes connected by confirmation edges that can adopt positive as well as negative values. This graph builds the basis for the computation of confirmation values for individual reports by different aggregation measures. By applying our approach to two use cases, we show the importance of a weighted combination, since the meaningfulness of the constituent measures varies between different contexts.