Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | WWW '22 |
| Untertitel | Proceedings of the ACM Web Conference 2022 |
| Seiten | 643-651 |
| Seitenumfang | 9 |
| ISBN (elektronisch) | 9781450390965 |
| Publikationsstatus | Veröffentlicht - 25 Apr. 2022 |
| Veranstaltung | 31st ACM World Wide Web Conference, WWW 2022 - Virtual, Online, Frankreich Dauer: 25 Apr. 2022 → 29 Apr. 2022 |
Abstract
OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid.
ASJC Scopus Sachgebiete
- Informatik (insg.)
- Computernetzwerke und -kommunikation
- Informatik (insg.)
- Software
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WWW '22: Proceedings of the ACM Web Conference 2022. 2022. S. 643-651.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Attention-Based Vandalism Detection in OpenStreetMap
AU - Tempelmeier, Nicolas
AU - Demidova, Elena
N1 - Funding Information: This work is supported by The National Key Research and Development Program of China No. 2021YFB3101400 and the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDC02040400. We are grateful to anonymous reviewers for their fruitful comments, corrections and inspiration to improve this paper. We also sincerely appreciate the shepherding from Magnus Almgren and writing help from Zhong Guan and Lulin Wang.
PY - 2022/4/25
Y1 - 2022/4/25
N2 - OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid.
AB - OpenStreetMap (OSM), a collaborative, crowdsourced Web map, is a unique source of openly available worldwide map data, increasingly adopted in Web applications. Vandalism detection is a critical task to support trust and maintain OSM transparency. This task is remarkably challenging due to the large scale of the dataset, the sheer number of contributors, various vandalism forms, and the lack of annotated data. This paper presents Ovid - a novel attention-based method for vandalism detection in OSM. Ovid relies on a novel neural architecture that adopts a multi-head attention mechanism to summarize information indicating vandalism from OSM changesets effectively. To facilitate automated vandalism detection, we introduce a set of original features that capture changeset, user, and edit information. Furthermore, we extract a dataset of real-world vandalism incidents from the OSM edit history for the first time and provide this dataset as open data. Our evaluation conducted on real-world vandalism data demonstrates the effectiveness of Ovid.
KW - OpenStreetMap
KW - Trustworthiness on the Web
KW - Vandalism Detection
UR - http://www.scopus.com/inward/record.url?scp=85129890399&partnerID=8YFLogxK
U2 - 10.48550/arXiv.2201.10406
DO - 10.48550/arXiv.2201.10406
M3 - Conference contribution
AN - SCOPUS:85129890399
SP - 643
EP - 651
BT - WWW '22
T2 - 31st ACM World Wide Web Conference, WWW 2022
Y2 - 25 April 2022 through 29 April 2022
ER -