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107506

User-Generated Content (UGC) Credibility on Social Media Using Sentiment Classification

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Last updated: 26 Dec 2024

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Abstract

Web 2.0 technologies have seen a big evolution recently leading to the existence of a huge amount of unreliable and misleading content due to the openness and low publishing barrier nature of the content generated through social media platforms. As a fact, the User-Generated Content (UGC) on social media platforms suffers from a lack of professional gatekeepers to monitor this content. Consequently, most online users fall into the trap of being misled through fake information that spreads rapidly. They usually rely on this information without any verification and this prevents them from making accurate decisions concerning their social lives, politics, or business events. Because online users face difficulty in finding which piece of information is credible or not, the researchers found that assessing User-Generated Content (UGC) of social media is very important in resolving the issue of credibility. This paper adapted some of the existing literature and concluded that many previous approaches have investigated information credibility on Twitter and a limited number of Facebook for proposing a new approach for measuring posts credibility. The proposed model used to measure the credibility of Facebook posts through a formula combined from the page profile rank and the post-analysis score. The model was tested and achieved 87.45 % accuracy.

DOI

10.21608/fcihib.2019.107506

Keywords

social media, Credibility on Social Media, Sentiment Classification, Machine Learning Techniques, performance evaluation

Authors

First Name

Esraa

Last Name

Afify

MiddleName

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Affiliation

Helwan University

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Orcid

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First Name

Ahmed

Last Name

Sharaf Eldin

MiddleName

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Affiliation

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Email

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Orcid

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First Name

Ayman

Last Name

E. Khedr

MiddleName

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Affiliation

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Email

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Orcid

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First Name

Fahad

Last Name

Kamal Alsheref

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Affiliation

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Email

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Volume

1

Article Issue

1

Related Issue

16264

Issue Date

2019-01-01

Receive Date

2019-01-19

Publish Date

2019-01-19

Page Start

1

Page End

19

Print ISSN

2537-0901

Online ISSN

2535-1397

Article File

FCIHIB_Volume 1_Issue 1_Pages 1-19.pdf

PDF . 933.7kB

Link

https://fcihib.journals.ekb.eg/article_107506.html

Detail API

https://fcihib.journals.ekb.eg/service?article_code=107506

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Type

المقالة الأصلية

Type Code

1,411

Publication Type

Journal

Publication Title

النشرة المعلوماتية في الحاسبات والمعلومات

Publication Link

https://fcihib.journals.ekb.eg/

MainTitle

User-Generated Content (UGC) Credibility on Social Media Using Sentiment Classification

Details

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Article

Created At

23 Jan 2023