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Concentration Free Outlier Detection Read chapter Read first chapter

2017 | OriginalPaper | Chapter

Wikipedia Vandal Early Detection: From User Behavior to User Embedding

Authors : Shuhan Yuan, Panpan Zheng, Xintao Wu, Yang Xiang

Published in: Machine Learning and Knowledge Discovery in Databases

Publisher: Springer International Publishing

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Abstract

Wikipedia is the largest online encyclopedia that allows anyone to edit articles. In this paper, we propose the use of deep learning to detect vandals based on their edit history. In particular, we develop a multi-source long-short term memory network (M-LSTM) to model user behaviors by using a variety of user edit aspects as inputs, including the history of edit reversion information, edit page titles and categories. With M-LSTM, we can encode each user into a low dimensional real vector, called user embedding. Meanwhile, as a sequential model, M-LSTM updates the user embedding each time after the user commits a new edit. Thus, we can predict whether a user is benign or vandal dynamically based on the up-to-date user embedding. Furthermore, those user embeddings are crucial to discover collaborative vandals. Code and data related to this chapter are available at: https://​bitbucket.​org/​bookcold/​vandal_​detection.

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previous chapter Sequence Generation with Target Attention
Footnotes
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Metadata
Title
Wikipedia Vandal Early Detection: From User Behavior to User Embedding
Authors
Shuhan Yuan
Panpan Zheng
Xintao Wu
Yang Xiang
Copyright Year
2017
Book
Machine Learning and Knowledge Discovery in Databases

Print ISBN: 978-3-319-71248-2

Electronic ISBN: 978-3-319-71249-9

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-71249-9_50

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