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Towards Vandalism Detection in OpenStreetMap Through a Data Driven Approach (Short Paper)

Authors Quy Thy Truong , Guillaume Touya , Cyril de Runz

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ACM Subject Classification
  • Human-centered computing → Collaborative content creation
  • Computing methodologies → Anomaly detection
Keywords
  • Vandalism
  • Volunteered Geographic Information
  • Outlier detection

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Abstract

Vandalism is a phenomenon that has affected by now the digital domain, in particular in the context of Volunteered Geographic Information projects. This paper aims at proposing a methodology to detect vandalism in the OpenStreetMap project. First, an analysis of related works sheds light on the lack of consensus when it comes to defining vandalism in VGI from both conceptual and practical points of view. Second, we present experiments on the use of clustering-based outlier detection methods to identify vandalism in OSM. The outcome of this study focuses on choosing the right variables when it comes to detecting vandalism in OSM.

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Quy Thy Truong, Guillaume Touya, and Cyril de Runz. Towards Vandalism Detection in OpenStreetMap Through a Data Driven Approach (Short Paper). In 10th International Conference on Geographic Information Science (GIScience 2018). Leibniz International Proceedings in Informatics (LIPIcs), Volume 114, pp. 61:1-61:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2018) https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.61

Author Details

Quy Thy Truong
  • Univ. Paris-Est, LASTIG COGIT, IGN, ENSG, F-94160 Saint-Mande, France
Guillaume Touya
  • Univ. Paris-Est, LASTIG COGIT, IGN, ENSG, F-94160 Saint-Mande, France
Cyril de Runz
  • Modeco, CReSTIC, University of Reims Champagne-Ardenne, CS 30012, Reims cedex 2, France

References

  1. Adler, Luca Alfaro, Santiago M. Mola-Velasco, Paolo Rosso, and Andrew G. West. Wikipedia vandalism detection: Combining natural language, metadata, and reputation features. In Alexander Gelbukh, editor, Computational Linguistics and Intelligent Text Processing, volume 6609 of Lecture Notes in Computer Science, chapter 23, pages 277-288. Springer Berlin Heidelberg, Berlin, Heidelberg, 2011. URL: http://dx.doi.org/10.1007/978-3-642-19437-5_23.
  2. Andrea Ballatore. Defacing the map: Cartographic vandalism in the digital commons. The Cartographic Journal, 51(3):214-224, 2014. URL: http://dx.doi.org/10.1179/1743277414y.0000000085.
  3. Jean-François Girres and Guillaume Touya. Quality assessment of the french OpenStreetMap dataset. Transactions in GIS, 14(4):435-459, aug 2010. URL: http://dx.doi.org/10.1111/j.1467-9671.2010.01203.x.
  4. Stefan Heindorf, Martin Potthast, Benno Stein, and Gregor Engels. Vandalism detection in wikidata. In Proceedings of the 25th ACM International on Conference on Information and Knowledge Management - CIKM '16, pages 327-336. ACM Press, 2016. URL: http://dx.doi.org/10.1145/2983323.2983740.
  5. Alexander Hinneburg and Hans H. Gabriel. DENCLUE 2.0: Fast clustering based on kernel density estimation. In Michael R. Berthold, John S. Taylor, Nada Lavrac, Michael R. Berthold, John S. Taylor, and Nada Lavrac, editors, IDA, volume 4723 of Lecture Notes in Computer Science, pages 70-80. Springer, 2007. URL: http://dx.doi.org/10.1007/978-3-540-74825-0_7.
  6. Pascal Neis, Marcus Goetz, and Alexander Zipf. Towards automatic vandalism detection in OpenStreetMap. ISPRS International Journal of Geo-Information, 1(3):315-332, nov 2012. URL: http://dx.doi.org/10.3390/ijgi1030315.
  7. Philip G. Zimbardo. A Social-Psychological analysis of vandalism: Making sense of senseless violence. Technical report, Stanford University, Department of Psychology, 1971. Google Scholar
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