A Semi-Supervised Learning Approach for Tackling Twitter Spam Drift

Twitter has changed the way people get information by allowing them to express their opinion and comments on the daily tweets. Unfortunately, due to the high popularity of Twitter, it has become ve...

[1]  Jun Zhang,et al.  Asymmetric self-learning for tackling Twitter Spam Drift , 2015, 2015 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[2]  Taghi M. Khoshgoftaar,et al.  Survey of review spam detection using machine learning techniques , 2015, Journal of Big Data.

[3]  Kurt Driessens,et al.  Using Weighted Nearest Neighbor to Benefit from Unlabeled Data , 2006, PAKDD.

[4]  Jun Zhang,et al.  A Performance Evaluation of Machine Learning-Based Streaming Spam Tweets Detection , 2015, IEEE Transactions on Computational Social Systems.

[5]  Virgílio A. F. Almeida,et al.  Detecting Spammers on Twitter , 2010 .

[6]  Rishabh Kaushal,et al.  Ecosystem of spamming on Twitter: Analysis of spam reporters and spam reportees , 2016, 2016 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[7]  R. K. Chauhan,et al.  Spam detection using KNN and decision tree mechanism in social network , 2016, 2016 Fourth International Conference on Parallel, Distributed and Grid Computing (PDGC).

[8]  Ala' M. Al-Zoubi,et al.  Spam profile detection in social networks based on public features , 2017, 2017 8th International Conference on Information and Communication Systems (ICICS).

[9]  Muhammad Arshad Islam,et al.  A hybrid approach for spam detection for Twitter , 2017, 2017 14th International Bhurban Conference on Applied Sciences and Technology (IBCAST).

[10]  Sri Suning Kusumawardani,et al.  Development of semi-supervised named entity recognition to discover new tourism places , 2016, 2016 2nd International Conference on Science and Technology-Computer (ICST).

[11]  Igor Santos,et al.  Semi-supervised Learning for Unknown Malware Detection , 2011, DCAI.

[12]  Jun Zhang,et al.  Statistical Twitter Spam Detection Demystified: Performance, Stability and Scalability , 2017, IEEE Access.

[13]  Wei Hu,et al.  Twitter spammer detection using data stream clustering , 2014, Inf. Sci..

[14]  Rodolfo Zunino,et al.  Spam detection of Twitter traffic: A framework based on random forests and non-uniform feature sampling , 2016, 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM).

[15]  Saad Alaboodi,et al.  Detecting Arabic spammers and content polluters on Twitter , 2016, 2016 Sixth International Conference on Digital Information Processing and Communications (ICDIPC).

[16]  Marc L. Pusey,et al.  Evaluation of semi-supervised learning for classification of protein crystallization imagery , 2014, IEEE SOUTHEASTCON 2014.

[17]  Adelaide V. Finch,et al.  September , 1867, The Hospital.

[18]  Bernhard Pfahringer,et al.  A semi-supervised Spam mail detector , 2006 .

[19]  Xiao Chen,et al.  6 million spam tweets: A large ground truth for timely Twitter spam detection , 2015, 2015 IEEE International Conference on Communications (ICC).

[20]  Vern Paxson,et al.  @spam: the underground on 140 characters or less , 2010, CCS '10.

[21]  Peerapon Vateekul,et al.  Improve Accuracy of Defect Severity Categorization Using Semi-Supervised Approach on Imbalanced Data Sets , .

[22]  Igor Santos,et al.  Collective classification for unknown malware detection , 2011, Proceedings of the International Conference on Security and Cryptography.

[23]  Mehrdad Jalali,et al.  Detecting spam tweets in Twitter using a data stream clustering algorithm , 2015, 2015 International Congress on Technology, Communication and Knowledge (ICTCK).

[24]  Yu Wang,et al.  Statistical Features-Based Real-Time Detection of Drifted Twitter Spam , 2017, IEEE Transactions on Information Forensics and Security.

[25]  Ian H. Witten,et al.  The WEKA data mining software: an update , 2009, SKDD.