REV2: Fraudulent User Prediction in Rating Platforms
暂无分享,去创建一个
Christos Faloutsos | V. S. Subrahmanian | Mohit Kumar | Bryan Hooi | Srijan Kumar | Disha Makhija | Srijan Kumar | C. Faloutsos | Bryan Hooi | Mohit Kumar | Disha Makhija
[1] V. S. Subrahmanian,et al. An Army of Me: Sockpuppets in Online Discussion Communities , 2017, WWW.
[2] Jing Wang,et al. Bonus, Disclosure, and Choice: What Motivates the Creation of High-Quality Paid Reviews? , 2012, ICIS.
[3] Charu C. Aggarwal,et al. The Troll-Trust Model for Ranking in Signed Networks , 2016, WSDM.
[4] Christos Faloutsos,et al. Suspicious Behavior Detection: Current Trends and Future Directions , 2016, IEEE Intelligent Systems.
[5] Christos Faloutsos,et al. BIRDNEST: Bayesian Inference for Ratings-Fraud Detection , 2015, SDM.
[6] Xifeng Yan,et al. Synthetic review spamming and defense , 2013, WWW.
[7] Mohammad Ali Abbasi,et al. Trust-Aware Recommender Systems , 2014 .
[8] Christos Faloutsos,et al. Edge Weight Prediction in Weighted Signed Networks , 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).
[9] Weixiang Shao,et al. Bimodal Distribution and Co-Bursting in Review Spam Detection , 2017, WWW.
[10] Philip S. Yu,et al. Review Graph Based Online Store Review Spammer Detection , 2011, 2011 IEEE 11th International Conference on Data Mining.
[11] Загоровская Ольга Владимировна,et al. Исследование влияния пола и психологических характеристик автора на количественные параметры его текста с использованием программы Linguistic Inquiry and Word Count , 2015 .
[12] V. S. Subrahmanian,et al. Predicting human behavior: The next frontiers , 2017, Science.
[13] Arjun Mukherjee,et al. What Yelp Fake Review Filter Might Be Doing? , 2013, ICWSM.
[14] J C Carrington. Predicting Human Behavior , 2008, Science.
[15] Arnab Bhattacharya,et al. Finding the bias and prestige of nodes in networks based on trust scores , 2011, WWW.
[16] George Valkanas,et al. The Impact of Fake Reviews on Online Visibility: A Vulnerability Assessment of the Hotel Industry , 2016, Inf. Syst. Res..
[17] Anna Cinzia Squicciarini,et al. Uncovering Crowdsourced Manipulation of Online Reviews , 2015, SIGIR.
[18] Krishna P. Gummadi,et al. Towards Detecting Anomalous User Behavior in Online Social Networks , 2014, USENIX Security Symposium.
[19] Krishna P. Gummadi,et al. Understanding and combating link farming in the twitter social network , 2012, WWW.
[20] Abhinav Kumar,et al. Spotting opinion spammers using behavioral footprints , 2013, KDD.
[21] Philip S. Yu,et al. Review spam detection via temporal pattern discovery , 2012, KDD.
[22] Michalis Faloutsos,et al. TrueView: Harnessing the Power of Multiple Review Sites , 2015, WWW.
[23] Martin Ester,et al. Detecting Singleton Review Spammers Using Semantic Similarity , 2015, WWW.
[24] Srinivasan Venkatesh,et al. Battling the Internet water army: Detection of hidden paid posters , 2011, 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013).
[25] Philip S. Yu,et al. Identify Online Store Review Spammers via Social Review Graph , 2012, TIST.
[26] Krishna P. Gummadi,et al. Strength in Numbers: Robust Tamper Detection in Crowd Computations , 2015, COSN.
[27] James W. Pennebaker,et al. Linguistic Inquiry and Word Count (LIWC2007) , 2007 .
[28] Ee-Peng Lim,et al. Detecting product review spammers using rating behaviors , 2010, CIKM.
[29] Leman Akoglu,et al. Collective Opinion Spam Detection: Bridging Review Networks and Metadata , 2015, KDD.
[30] Hong Cheng,et al. Robust Reputation-Based Ranking on Bipartite Rating Networks , 2012, SDM.
[31] Danai Koutra,et al. Graph based anomaly detection and description: a survey , 2014, Data Mining and Knowledge Discovery.
[32] Christos Faloutsos,et al. Opinion Fraud Detection in Online Reviews by Network Effects , 2013, ICWSM.
[33] Hyun Ah Song,et al. FRAUDAR: Bounding Graph Fraud in the Face of Camouflage , 2016, KDD.
[34] Derek Greene,et al. Merging multiple criteria to identify suspicious reviews , 2010, RecSys '10.
[35] Jure Leskovec,et al. From amateurs to connoisseurs: modeling the evolution of user expertise through online reviews , 2013, WWW.
[36] Eric Gilbert,et al. VADER: A Parsimonious Rule-Based Model for Sentiment Analysis of Social Media Text , 2014, ICWSM.
[37] Neil Shah,et al. False Information on Web and Social Media: A Survey , 2018, ArXiv.
[38] Christos Faloutsos,et al. CatchSync: catching synchronized behavior in large directed graphs , 2014, KDD.