홈 › Papers

Wikidata Vandalism Detection - The Loganberry Vandalism Detector at WSDM Cup 2017

2017-12-19 · Zhu Qi University of Illinois Urbana-Champaign, Ng Hongwei University of Illinois Urbana-Champaign, Liu Liyuan University of Illinois Urbana-Champaign, Ji Ziwei University of Illinois Urbana-Champaign, Jiang Bingjie University of Illinois Urbana-Champaign, Shen Jiaming University of Illinois Urbana-Champaign, Gui Huan University of Illinois Urbana-Champaign

Wikidata is the new, large-scale knowledge base of the Wikimedia Foundation. As it can be edited by anyone, entries frequently get vandalized, leading to the possibility that it might spread of falsified information if such posts are not detected. The WSDM 2017 Wiki Vandalism Detection Challenge requires us to solve this problem by computing a vandalism score denoting the likelihood that a revision corresponds to an act of vandalism and performance is measured using the ROC-AUC obtained on a held-out test set. This paper provides the details of our submission that obtained an ROC-AUC score of 0.91976 in the final evaluation.

📄 PDF Abstract BibTeX arXiv:1712.06922

Code (0)

등록된 구현이 없습니다.

Ensemble Models for Detecting Wikidata Vandalism with Stacking - Team Honeyberry Vandalism Detector at WSDM Cup 2017

2017-12-19 · Yamazaki Tomoya Yahoo Japan Corporation, Sasaki Mei Yahoo Japan Corporation, Murakami Naoya Yahoo Japan Corporation, Makabe Takuya Yahoo Japan Corporation 외

The WSDM Cup 2017 is a binary classification task for classifying Wikidata revisions into vandalism and non-vandalism. This paper describes our method using some machine learning techniques such as under-sampling, featur…

BIG-bench Machine Learning Binary Classification feature selection

Building automated vandalism detection tools for Wikidata

2017-03-10 · Sarabadani Amir, Halfaker Aaron, Taraborelli Dario

Wikidata, like Wikipedia, is a knowledge base that anyone can edit. This open collaboration model is powerful in that it reduces barriers to participation and allows a large number of people to contribute. However, it ex…

Feature Engineering

Overview of the Wikidata Vandalism Detection Task at WSDM Cup 2017

2017-12-16 · Heindorf Stefan, Potthast Martin, Engels Gregor, Stein Benno

We report on the Wikidata vandalism detection task at the WSDM Cup 2017. The task received five submissions for which this paper describes their evaluation and a comparison to state of the art baselines. Unlike previous …

A Production Oriented Approach for Vandalism Detection in Wikidata - The Buffaloberry Vandalism Detector at WSDM Cup 2017

2017-12-19 · Crescenzi Rafael Austral University, Fernandez Marcelo Austral University, Calabria Federico A. Garcia Austral University, Albani Pablo Austral University 외

Wikidata is a free and open knowledge base from the Wikimedia Foundation, that not only acts as a central storage of structured data for other projects of the organization, but also for a growing array of information sys…

Misinformation

Proceedings of the WSDM Cup 2017: Vandalism Detection and Triple Scoring

2017-12-27 · Potthast Martin, Heindorf Stefan, Bast Hannah

The WSDM Cup 2017 was a data mining challenge held in conjunction with the 10th International Conference on Web Search and Data Mining (WSDM). It addressed key challenges of knowledge bases today: quality assurance and e…