There is much literature on Wikipedia vandalism detection. However, this writing addresses two facets given little treatment to date. First, prior efforts emphasize zero-delay detection, classifying edits the moment they are made. If classification can be delayed (e.g., compiling offline distributions), it is possible to leverage ex post facto evidence. This work describes/evaluates several fea...

This paper overviews 9 vandalism detectors that have been developed and evaluated within PAN’10. We start with a survey of 55 different kinds of features employed in the detectors. Then, the detectors’ performances are evaluated in detail based on precision, recall, and the receiver operating characteristic. Finally, we set up a meta detector that combines all detectors into one, which turns ou...

Landscape Architecture Agenda 2050 classified vandalism as a serious environmental threat. Vandalism incidence has major impact on urban tree performances, such health declines, poor appearance, and performance-related effects. Hence, there is need for assessment that can present the statuses to guide decision-makers managers in improvement rectification of decisions. This study assessed level ...

Wikipedia despite having a very small budget has been among the top ten most visited websites for over half a decade. Being this visible also generated the problem of ill intended people modifying Wikipedia in a destructive manner. VandalSense is an experimental tool programmed by F. Gediz Aksit to automatically identify vandalism on Wikipedia through the use of machine learning and text mining...

Wikipedia vandalism identification is a very complex issue, which is now mostly solved manually by volunteers. This paper presents the main components of a system built by our group in order to automatically identify vandalized Wikipedia articles. The main component of our system is a machine learning component that uses three types of features grouped in 3 classes: Metadata, Text and Language....

One of the valuable features of any collaboratively constructed semantic resource (CSR) is its ability to – as a system – continuously correct itself. Wikipedia is an excellent example of such a process, with vandalism and misinformation being removed or reverted in astonishing time by a coalition of human editors and machine bots. However, some errors are harder to spot than others, a problem ...