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) multiclassifier-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.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System : Pentium Dual Core.
·
Hard Disk : 120 GB.
·
Monitor : 15’’ LED
·
Input Devices : Keyboard, Mouse
·
Ram : 1 GB
SOFTWARE REQUIREMENTS:
·
Operating system : Windows 7.
·
Coding Language : JAVA/J2EE
·
Tool : Netbeans 7.2.1
·
Database : MYSQL
REFERENCE:
Surendra Sedhai and Aixin
Sun, “Semi-Supervised Spam Detection in Twitter Stream”, IEEE TRANSACTIONS ON
COMPUTATIONAL SOCIAL SYSTEMS, 2018.