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DOI: 10.14569/IJACSA.2019.0100743
PDF

A Proposed Model for Detecting Facebook News’ Credibility

Author 1: Amira M Idrees
Author 2: Fahad Kamal Alsheref
Author 3: Ahmed I. ElSeddawy

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 10 Issue 7, 2019.

  • Abstract and Keywords
  • How to Cite this Article
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Abstract: Social networks are currently one of the main News’ sources for most of their users. Moreover, News channels also consider social networks as main channels not only for spreading the news but also for measuring the feedback from their followers. Facebook Followers can comment or react to the news, which represents the follower’s feedback about this topic. Therefore, it is a fact that measuring the News’ credibility is one of the important tasks that could control the propagation of the fake news as well as the number of News’ followers. The proposed model in this research highlights the impact of the News’ followers on detecting the News’ polarity either it is fake or not. The proposed model focuses on applying an intelligent sentiment analysis using Vector Space Model (VSM) which is one of the most successful techniques on the users’ comments and reactions through the emoji. Then the degree of credibility is determined according to the correlation coefficient. An experimental study was applied using Facebook News dataset, which included the News and the followers’ feedbacks.

Keywords: Social network; vector space model; correlation coefficient; sentiment analysis

Amira M Idrees, Fahad Kamal Alsheref and Ahmed I. ElSeddawy, “A Proposed Model for Detecting Facebook News’ Credibility” International Journal of Advanced Computer Science and Applications(IJACSA), 10(7), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100743

@article{Idrees2019,
title = {A Proposed Model for Detecting Facebook News’ Credibility},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100743},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100743},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {7},
author = {Amira M Idrees and Fahad Kamal Alsheref and Ahmed I. ElSeddawy}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

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