ABSTRACT:
Twitter is one of the most popular microblogging services, which is generally used to share
news and updates through short messages restricted to 280 characters. However,
its open nature and large user base are frequently exploited by automated
spammers, content polluters, and other ill-intended users to commit various
cyber crimes, such as cyberbullying, trolling, rumor dissemination, and stalking. Accordingly, a number of
approaches have been proposed by researchers to address these problems.
However, most of these approaches are based on user characterization and
completely disregarding mutual interactions. In this study, we present a hybrid
approach for detecting automated spammers by amalgamating community based
features with other feature categories, namely metadata- , content-, and
interaction-based features. The novelty of the proposed approach lies in the
characterization of users based on their interactions with their followers
given that a user can evade features that are related to his/her own
activities, but evading those based on the followers is difficult. Nineteen
different features, including six newly defined features and two redefined
features, are identified for learning three classifiers, namely, random forest,
decision tree, and Bayesian network, on a real dataset that comprises benign
users and spammers. The discrimination power of different feature categories is
also analyzed, and interaction- and community-based features are determined to
be the most effective for spam detection, whereas metadata-based features are
proven to be the least effective.
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:
Mohd Fazil and
Muhammad Abulaish, Senior Member, IEEE, “A Hybrid
Approach for Detecting Automated Spammers in Twitter”, IEEE Transactions on
Information Forensics and Security, 2018.