Wikipedia Vandal Early Detection: From User Behavior to User Embedding

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.

[1]  Jimeng Sun,et al.  Neighborhood formation and anomaly detection in bipartite graphs , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[2]  V. S. Subrahmanian,et al.  VEWS: A Wikipedia Vandal Early Warning System , 2015, KDD.

[3]  Yang Xiang,et al.  SNE: Signed Network Embedding , 2017, PAKDD.

[4]  Luca de Alfaro,et al.  A content-driven reputation system for the wikipedia , 2007, WWW '07.

[5]  Philip S. Yu,et al.  Review spam detection via temporal pattern discovery , 2012, KDD.

[6]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[7]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[8]  Arjun Mukherjee,et al.  What Yelp Fake Review Filter Might Be Doing? , 2013, ICWSM.

[9]  Jeffrey Pennington,et al.  GloVe: Global Vectors for Word Representation , 2014, EMNLP.

[10]  Cristina V. Lopes,et al.  Vandalism detection in Wikipedia: a high-performing, feature-rich model and its reduction through Lasso , 2011, Int. Sym. Wikis.

[11]  Benno Stein,et al.  Vandalism Detection in Wikidata , 2016, CIKM.

[12]  Santiago Moisés Mola-Velasco,et al.  Wikipedia Vandalism Detection Through Machine Learning: Feature Review and New Proposals - Lab Report for PAN at CLEF 2010 , 2012, CLEF.

[13]  Bo Zhang,et al.  Discriminative Deep Random Walk for Network Classification , 2016, ACL.

[14]  Mingzhe Wang,et al.  LINE: Large-scale Information Network Embedding , 2015, WWW.

[15]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[16]  Christos Faloutsos,et al.  oddball: Spotting Anomalies in Weighted Graphs , 2010, PAKDD.

[17]  Santiago Moisés Mola-Velasco,et al.  Wikipedia vandalism detection , 2011, WWW.

[18]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[19]  Steven Skiena,et al.  DeepWalk: online learning of social representations , 2014, KDD.

[20]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[21]  Matthew D. Zeiler.  ADADELTA: An Adaptive Learning Rate Method , 2012, ArXiv.

[22]  Diyi Yang,et al.  Hierarchical Attention Networks for Document Classification , 2016, NAACL.

[23]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[24]  Charu C. Aggarwal,et al.  Heterogeneous Network Embedding via Deep Architectures , 2015, KDD.

[25]  Arjun Mukherjee,et al.  Exploiting Burstiness in Reviews for Review Spammer Detection , 2021, ICWSM.

[26]  Insup Lee,et al.  Detecting Wikipedia vandalism via spatio-temporal analysis of revision metadata? , 2010, EUROSEC '10.

[27]  Jeffrey Dean,et al.  Efficient Estimation of Word Representations in Vector Space , 2013, ICLR.

[28]  Jiawei Han,et al.  Survey on web spam detection: principles and algorithms , 2012, SKDD.

[29]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[30]  Qiaozhu Mei,et al.  PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks , 2015, KDD.

[31]  Marc Najork,et al.  Detecting spam web pages through content analysis , 2006, WWW '06.

[32]  Ee-Peng Lim,et al.  Detecting product review spammers using rating behaviors , 2010, CIKM.

[33]  Pascal Vincent,et al.  Representation Learning: A Review and New Perspectives , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Hector Garcia-Molina,et al.  Web graph similarity for anomaly detection , 2010, Journal of Internet Services and Applications.

[35]  Jure Leskovec,et al.  node2vec: Scalable Feature Learning for Networks , 2016, KDD.

[36]  Jun Li,et al.  Spectrum-based Deep Neural Networks for Fraud Detection , 2017, CIKM.

[37]  Xiaowei Ying,et al.  Spectrum based fraud detection in social networks , 2011, ICDE.

[38]  William Yang Wang,et al.  “Got You!”: Automatic Vandalism Detection in Wikipedia with Web-based Shallow Syntactic-Semantic Modeling , 2010, COLING.

[39]  Paolo Rosso,et al.  Wikipedia Vandalism Detection: Combining Natural Language, Metadata, and Reputation Features , 2011, CICLing.