A Proposed Model for Detecting Facebook News’ Credibility

Idrees, Amira M.; Alsheref, Fahad Kamal; ElSeddawy, Ahmed I.

doi:10.14569/ijacsa.2019.0100743

Cited by 10 publications

(5 citation statements)

References 20 publications

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“…It is for this reason that the population will have to develop new tools to detect the quality or authenticity of information. Likewise, it is not appropriate to continue to consider Facebook as an agency that disseminates news because that is not the purpose for which it was conceived (Melchior, & Oliveira, 2021), (Idrees, et al, 2019) and (Lunga, & Mthembu, 2020).…”

Section: Regarding the Most Cited Documentsmentioning

confidence: 99%

Untitled

2023

JISIS

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The spread of fake news on Facebook is a reality. This behavior is aimed at affecting reputation and generating distrust towards a certain person or on a specific topic. A bibliometric study of the scientific production in Scopus on Fake News on Facebook in the period 2013-2023 was carried out. The results showed that fake news is promoted in political issues to generate instability and in health issues to generate confusion and panic in the population. It was concluded that citizens must develop critical thinking to question the validity of news sources. Similarly, anonymity and misinformation are a natural part of society.

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Section: Regarding the Most Cited Documentsmentioning

confidence: 99%

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2023

JISIS

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“…We cannot include them, so we ought not to have their numeric representations. A relapse issue features a genuine number (a number with a decimal point) as its outcomes [46]- [48].…”

Section: Tools Of Emergence Technologiesmentioning

confidence: 99%

Towards a Better Urban Design in Respect to Environmental Behavior Using Knowledge Discovery

Abdel-Moneim¹,

Seddawy²

2021

Journal of Southwest Jiaotong University

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Urbanization growth is creeping, and the number of cities will increase each day more than before. Predictions say that approximately 70% of the world population will live in urban areas by 2050. Hence, new approaches must be considered while designing/upgrading/enhancing urban places. This article describes a new theoretical model to enhance the urban design process through Knowledge Discovery techniques based on Artificial Intelligence, data mining, machine learning, 5G, and Micro-World. The research findings enable assessing the whole process of Urban Design phases by proposing new techniques using the Knowledge Discovery by combining the traditional research methods used frequently in the Urban Design Process with the Knowledge Discovery Techniques. Through reviewing and analyzing the urban design and its relation to urban behavior, the authors deduct the correlations between the urban design process and the knowledge discovery techniques that can build solutions based on objective output, not only the traditional subjective ones. The new research results explain the exact application relevant to each step in the urban design process. Also, the research will supplement and improve urban studies to understand the profound relationship between Urban Environmental Psychology and Artificial Intelligence. It can serve the urban designers and planners by using the knowledge discovery to enhance urban practice by providing a more comprehensive range of solutions.

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“…The research in [3] , the concept of focusing on the most effective attributes has been considered through determining the attributes' correlations which were effective, yet other more effective approaches would be considered. While in [14], the attributes' determination considered identifying the news' credibility with identifying each considered attribute, the research had no clarification for other non-considered attributes as the focus was for proving the credibility target. Other researches such as in [15] and [16] considered different fields but with the scope of identifying the effective attributes with following the attributes' semantic relations with no consideration of the individual impact for these attributes.…”

Section: Related Workmentioning

confidence: 99%

A Proposed Method for Minimizing Mining Tasks’ Data Dimensionality

Idrees¹,

Gomaa²

2020

IJIES

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Knowledge discovery techniques have heavily contributed to many fields with significant success. However, with the continuous growth of data, these techniques suffer from bottlenecks in processing these data. One of the directions to hinder this effect is reducing the data dimensionality which focuses on eliminating the attributes which have no significant effect on the discovery technique accuracy. This research proposes a novel method for reducing data dimensionality. The proposed method is based on two main pillars, the first is applying the adapted Saaty method for determining the attributes' consistency with proposing further adaptation targeting more accurate accuracy determination. The second pillar is applying the clustering techniques on the consistent attributes to eliminate the least weighted attributes in each cluster which also have the least consistent measures. The result of applying the two steps is to highlight the most significant dataset attributes. The proposed method has been successfully applied on the Gastrology dataset which attributes have been reduced from 62 attributes to 31 attributes. A set of classification techniques have been applied on the dataset to prove that the dimensionality reduction has retained the classification task accuracy, the results presented that the Random Forest algorithm had an accuracy equal 95.36% when applied to the adopted dataset.

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A Proposed Model for Detecting Facebook News’ Credibility

Cited by 10 publications

References 20 publications

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Untitled

Towards a Better Urban Design in Respect to Environmental Behavior Using Knowledge Discovery

A Proposed Method for Minimizing Mining Tasks’ Data Dimensionality

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