ABSTRACT:
Online Social Networks (OSNs) have
not only significantly reformed the social interaction pattern but have also
emerged as an effective platform for recommendation of services and products.
The upswing in use of the OSNs has also witnessed growth in unwanted activities
on social media. On the one hand, the spammers on social media can be a high
risk towards the security of legitimate users and on the other hand some of the
legitimate users, such as bloggers can pollute the results of recommendation
systems that work alongside the OSNs. The polluted results of recommendation
systems can be precarious to the masses that track recommendations. Therefore,
it is necessary to segregate such type of users from the genuine experts. We
propose a framework that separates the spammers and unsolicited bloggers from
the genuine experts of a specific domain. The proposed approach employs
modified Hyperlink Induced Topic Search (HITS) to separate the unsolicited
bloggers from the experts on Twitter on the basis of tweets. The approach
considers domain specific keywords in the tweets and several tweet
characteristics to identify the unsolicited bloggers. Experimental results
demonstrate the effectiveness of the proposed methodology as compared to
several state-of-the-art approaches and classifiers.
SYSTEM REQUIREMENTS:
HARDWARE REQUIREMENTS:
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System : Pentium Dual Core.
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Hard Disk : 120 GB.
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Monitor : 15’’ LED
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Input Devices : Keyboard, Mouse
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Ram : 1 GB
SOFTWARE REQUIREMENTS:
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Operating system : Windows 7.
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Coding Language : JAVA/J2EE
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Tool : Netbeans 7.2.1
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Database : MYSQL
REFERENCE:
Muhammad U. S.
Khan, Member, IEEE, Mazhar Ali, Member, IEEE, Assad Abbas, Student Member,
IEEE, Samee U. Khan, Senior Member, IEEE, and Albert Y. Zomaya, Fellow, IEEE, “Segregating Spammers and Unsolicited Bloggers from Genuine Experts on
Twitter”, IEEE Transactions on Dependable and Secure Computing, 2018.