Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89418
Title: Semi-supervised spam detection in Twitter stream
Authors: Sedhai, Surendra
Sun, Aixin
Keywords: Semi-supervised Learning
Twitter
Issue Date: 2017
Source: Sedhai, S., & Sun, A. (2018). Semi-supervised spam detection in Twitter stream. IEEE Transactions on Computational Social Systems, 5(1), 169-175.
Series/Report no.: IEEE Transactions on Computational Social Systems
Abstract: Most existing techniques for spam detection on Twitter aim to identify and block users who post spam tweets. In this paper, we propose a semi-supervised spam detection (S3D) framework for spam detection at tweet-level. The proposed framework consists of two main modules: spam detection module operating in real-time mode and model update module operating in batch mode. The spam detection module consists of four lightweight detectors: 1) blacklisted domain detector to label tweets containing blacklisted URLs; 2) near-duplicate detector to label tweets that are near-duplicates of confidently prelabeled tweets; 3) reliable ham detector to label tweets that are posted by trusted users and that do not contain spammy words; and 4) multi classifier-based detector labels the remaining tweets. The information required by the detection module is updated in batch mode based on the tweets that are labeled in the previous time window. Experiments on a large-scale data set show that the framework adaptively learns patterns of new spam activities and maintain good accuracy for spam detection in a tweet stream.
URI: https://hdl.handle.net/10356/89418
http://hdl.handle.net/10220/44906
DOI: 10.1109/TCSS.2017.2773581
Rights: © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: [http://dx.doi.org/10.1109/TCSS.2017.2773581].
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

Files in This Item:
File Description SizeFormat 
Semi-Supervised Spam Detection in Twitter Stream.pdf623.9 kBAdobe PDFThumbnail
View/Open

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.