2022
DOI: 10.7717/peerj-cs.1151
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Multi-label multi-class COVID-19 Arabic Twitter dataset with fine-grained misinformation and situational information annotations

Abstract: Since the inception of the current COVID-19 pandemic, related misleading information has spread at a remarkable rate on social media, leading to serious implications for individuals and societies. Although COVID-19 looks to be ending for most places after the sharp shock of Omicron, severe new variants can emerge and cause new waves, especially if the variants can evade the insufficient immunity provided by prior infection and incomplete vaccination. Fighting the fake news that promotes vaccine hesitancy, for … Show more

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Cited by 10 publications
(9 citation statements)
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References 77 publications
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“…For model refinement and the identification of optimal hyperparameters, the Ray-Tune optimization framework, employing a population-based scheduler, was used in Python [ 128 ]. The resulting model achieved an F-score of 0.83, a metric value consistent with previous studies indicating a good model fit [ 38 , 101 , 129 , 130 ]. The developed classification model was then applied to unlabeled data, classifying tweets as “pro”, “anti”, and “neutral” respectfully.…”
Section: Methodssupporting
confidence: 86%
“…For model refinement and the identification of optimal hyperparameters, the Ray-Tune optimization framework, employing a population-based scheduler, was used in Python [ 128 ]. The resulting model achieved an F-score of 0.83, a metric value consistent with previous studies indicating a good model fit [ 38 , 101 , 129 , 130 ]. The developed classification model was then applied to unlabeled data, classifying tweets as “pro”, “anti”, and “neutral” respectfully.…”
Section: Methodssupporting
confidence: 86%
“…As many as six articles focus on sentiment analysis for different purposes ( Smetanin, 2022 ; Pratama & Firmansyah, 2022 ; Baxi, Philip & Mago, 2022 ; Nguyen & Gokhale, 2022 ; Shamoi et al., 2022 ; Ali, Irfan & Lashari, 2023 ). Four studies focused on tackling online harms of different kinds, with studies on abusive language detection ( Almerekhi, Kwak & Jansen, 2022 ; Ramponi et al., 2022 ), suicidal ideation detection ( Baghdadi et al., 2022 ) and misinformation detection ( Obeidat et al., 2022 ). Others studied NLP techniques for social media , focused on the analysis of Twitter discourse ( Heaton et al., 2023 ), language identification ( Hidayatullah et al., 2023 ) and named entity recognition ( Fudholi et al., 2023 ).…”
Section: Special Issue Themesmentioning
confidence: 99%
“… Obeidat et al. (2022) developed a novel Twitter dataset for misinformation detection from social media, with a specific focus on COVID-19 misinformation.…”
Section: Summary Of Contributionsmentioning
confidence: 99%
“…presents a machine learning-based system for detecting misinformation in Arabic tweets related to COVID-19 vaccination, achieving promising performance. The work by (Obeidat et al, 2022) introduces a comprehensive dataset annotated with fine-grained misinformation classes and situational information, and presents baseline results using various classifiers.…”
Section: Related Workmentioning
confidence: 99%