A Multi-dimensional Credibility Assessment for Arabic News Sources

Due to the advances in social media, it has become the most popular means of the propagation of news. Many news items are published on social media like Facebook, Twitter, Instagram, etc. Facebook is a huge source for spreading and consuming daily news, but it is an unstructured way of producing new...

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Bibliographic Details
Published in:International journal of advanced computer science & applications 2021, Vol.12 (9)
Main Authors: Gaber, Amira M., El-din, Mohamed Nour, Moussa, Hanan
Format: Article
Language:English
Subjects:
Algorithms
Credibility
Data analysis
Digital media
Domains
Machine learning
News
Social networks
Online Access:Get full text
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Summary:Due to the advances in social media, it has become the most popular means of the propagation of news. Many news items are published on social media like Facebook, Twitter, Instagram, etc. Facebook is a huge source for spreading and consuming daily news, but it is an unstructured way of producing news about domains (Art, Health, Education, Sport, Politics, etc.). Thus, this paper will present a model to assess the credibility of news sources over the social context in a particular domain through a particular period of time from a multidimensional perspective. Based on these dimensions of credibility, this model will be designed, evaluated, and implemented by using machine learning algorithms and Arabic NLP approaches to assess the credibility score for Arabic news sources on Facebook. In addition, the study will visualize their scores at different data analysis levels to make the assessment more precise and trustworthy. The proposed model has been implemented and tested over some real Arabic news sources for specific domains and over a period of time to produce a credibility score for each one, whereas the user can display these scores and choose the most credible news sources. The credibility assessment model will be more specific and accurate for a specific domain and time with an accuracy of 98%.
ISSN:2158-107X
2156-5570
DOI:10.14569/IJACSA.2021.0120935