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        <title>Fake or Real? A Study of Arabic Satirical Fake News</title>
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    <name type="personal">
        <namePart type="given">Hadeel</namePart>
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    <name type="personal">
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        <namePart type="family">Orasan</namePart>
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    <name type="personal">
        <namePart type="given">Emad</namePart>
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            <title>Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)</title>
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            <namePart type="given">Ahmet</namePart>
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            <namePart type="given">Arkaitz</namePart>
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    <abstract>One very common type of fake news is satire which comes in a form of a news website or an online platform that parodies reputable real news agencies to create a sarcastic version of reality. This type of fake news is often disseminated by individuals on their online platforms as it has a much stronger effect in delivering criticism than through a straightforward message. However, when the satirical text is disseminated via social media without mention of its source, it can be mistaken for real news. This study conducts several exploratory analyses to identify the linguistic properties of Arabic fake news with satirical content. It shows that although it parodies real news, Arabic satirical news has distinguishing features on the lexico-grammatical level. We exploit these features to build a number of machine learning models capable of identifying satirical fake news with an accuracy of up to 98.6%. The study introduces a new dataset (3185 articles) scraped from two Arabic satirical news websites (‘Al-Hudood’ and ‘Al-Ahram Al-Mexici’) which consists of fake news. The real news dataset consists of 3710 articles collected from three official news sites: the ‘BBC-Arabic’, the ‘CNN-Arabic’ and ‘Al-Jazeera news’. Both datasets are concerned with political issues related to the Middle East.</abstract>
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        <url>https://aclanthology.org/2020.rdsm-1.7</url>
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%0 Conference Proceedings
%T Fake or Real? A Study of Arabic Satirical Fake News
%A Saadany, Hadeel
%A Orasan, Constantin
%A Mohamed, Emad
%Y Aker, Ahmet
%Y Zubiaga, Arkaitz
%S Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM)
%D 2020
%8 December
%I Association for Computational Linguistics
%C Barcelona, Spain (Online)
%F saadany-etal-2020-fake
%X One very common type of fake news is satire which comes in a form of a news website or an online platform that parodies reputable real news agencies to create a sarcastic version of reality. This type of fake news is often disseminated by individuals on their online platforms as it has a much stronger effect in delivering criticism than through a straightforward message. However, when the satirical text is disseminated via social media without mention of its source, it can be mistaken for real news. This study conducts several exploratory analyses to identify the linguistic properties of Arabic fake news with satirical content. It shows that although it parodies real news, Arabic satirical news has distinguishing features on the lexico-grammatical level. We exploit these features to build a number of machine learning models capable of identifying satirical fake news with an accuracy of up to 98.6%. The study introduces a new dataset (3185 articles) scraped from two Arabic satirical news websites (‘Al-Hudood’ and ‘Al-Ahram Al-Mexici’) which consists of fake news. The real news dataset consists of 3710 articles collected from three official news sites: the ‘BBC-Arabic’, the ‘CNN-Arabic’ and ‘Al-Jazeera news’. Both datasets are concerned with political issues related to the Middle East.
%U https://aclanthology.org/2020.rdsm-1.7
%P 70-80

Markdown (Informal)

[Fake or Real? A Study of Arabic Satirical Fake News](https://aclanthology.org/2020.rdsm-1.7) (Saadany et al., RDSM 2020)

• Fake or Real? A Study of Arabic Satirical Fake News (Saadany et al., RDSM 2020)

ACL

• Hadeel Saadany, Constantin Orasan, and Emad Mohamed. 2020. Fake or Real? A Study of Arabic Satirical Fake News. In Proceedings of the 3rd International Workshop on Rumours and Deception in Social Media (RDSM), pages 70–80, Barcelona, Spain (Online). Association for Computational Linguistics.