Automating Credibility Assessment of Arabic News

Hammad, Mohamed; Hemayed, Elsayed E.

doi:10.1007/978-3-319-03260-3_13

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…This in turn caused many troubles for individuals, organizations and even governments. Fake news can lead to big negative changes among society, like what has happened in the Arabic world during 'Arabic Spring' where the fake news was widespread in social networks and was used as a weapon of Psychological war against individuals and governments [6]. In this article, a conceptual model is proposed for fake news detection from Twitter using several features of news content including of linguistic features, writing style features, semantic features and sentiment features as well as social context-based features including user-based features and network-based features.…”

Section: Introductionmentioning

confidence: 99%

Content-Social Based Features for Fake News Detection Model from Twitter

Hussein¹

2019

IJATCSE

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The advancement of social networks has facilitated the sharing and spread of news among people over the world. With the growth of these networks and growth of the volume of the news shared daily, the phenomena of fake news become more stronger and widely spread. In this paper, content-social based features for fake news detection model from Twitter data has been proposed. This work aims to analyze content-based features of news content including of linguistic features, writing style features, semantic features and sentiment features along with social-context based features of news diffusion over the social network including user-based features and network-based features to detect fake news from Twitter news posts. With using of unsupervised graph-based clustering approach, no labelled data is required and this make the proposed model more practical to detect online fake news.

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Section: Introductionmentioning

confidence: 99%