ABSTRACT:
Information credibility on Twitter has been a topic of interest among
researchers in the fields of both computer and social sciences, primarily
because of the recent growth of this platform as a tool for information
dissemination. Twitter has made it increasingly possible to offer
near-real-time transfer of information in a very cost-effective manner. It is
now being used as a source of news among a wide array of users around the
globe. The beauty of this platform is that it delivers timely content in a
tailored manner that makes it possible for users to obtain news regarding their
topics of interest. Consequently, the development of techniques that can verify
information obtained from Twitter has become a challenging and necessary task.
In this paper, we propose a new credibility analysis system for assessing
information credibility on Twitter to prevent the proliferation of fake or
malicious information. The proposed system consists of four integrated
components: a reputation-based component, a credibility classifier engine, a
user experience component, and a feature-ranking algorithm. The components
operate together in an algorithmic form to analyze and assess the credibility of
Twitter tweets and users. We tested the performance of our system on two
different datasets from 489,330 unique Twitter accounts. We applied 10-fold
cross-validation over four machine learning algorithms. The results reveal that
a significant balance between recall and precision was achieved for the tested
dataset.
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:
Majed Alrubaian, Student
Member, IEEE,
Muhammad Al-Qurishi, Student
Member, IEEE Mohammad Mehedi Hassan, Member, IEEE and Atif
Alamri, Member, IEEE, “A Credibility Analysis System
for Assessing Information on Twitter”, IEEE Transactions on Dependable and
Secure Computing, 2018.