Towards Evaluating the COVID’19 related Fake News Problem: Case of Morocco

Maakoul, Oussama; Boucht, Sabah; Hachimi, Karima El; Azzouzi, Salma

doi:10.1109/icecocs50124.2020.9314517

Cited by 15 publications

(10 citation statements)

References 9 publications

(7 reference statements)

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“…On the other perspective, many authors expressed confidence regarding the development of predictive models. Maakoul et al [18] perspective on forecasting model construction is compatible with the research findings. Multiple researchers, including [46][47][48], performed a systematic review of COVID-19 analytical models.…”

Section: -Discussionsupporting

confidence: 86%

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

et al. 2022

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The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PDF

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Section: -Discussionsupporting

confidence: 86%

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

et al. 2022

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“…Similarly, [127] used conventional machine learning in the form of logistic regression to detect fake news from a dataset of Facebook comments. This work, however, is limited by the dataset used consisting only of 80 comments, and the use of only one model.…”

Section: A1 Fake News Identificationmentioning

confidence: 99%

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

Asaad¹,

Khaouja²,

Ghogho³

et al. 2022

Preprint

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BACKGROUND Epidemics and outbreaks present arduous challenges requiring both individual and communal efforts. Social media sites offer significant amounts of data that can be leveraged for bio-surveillance. They also provide a platform to quickly and efficiently reach a sizeable percentage of the population, hence their potential impact on various aspects of epidemic mitigation. OBJECTIVE The general objective of this systematic literature review is to provide a methodical overview of the integration of social media in different epidemic-related contexts. METHODS Three research questions were conceptualized for this review, resulting in over 10000 publications collected in the first PRISMA stage, 129 of which were selected for inclusion. A thematic method-oriented synthesis was undertaken and identified 5 main themes related to social media enabled epidemic surveillance, misinformation management, and mental health. RESULTS Findings uncover a need for more robust applications of the lessons learned from epidemic post-mortem documentation. A vast gap exists between retrospective analysis of epidemic management and result integration in prospective studies. CONCLUSIONS Harnessing the full potential of social media in epidemic related tasks requires streamlining the results of epidemic forecasting, public opinion understanding and misinformation propagation, all while keeping abreast of potential mental health implications. Pro-active prevention has thus become vital for epidemic curtailment and containment.

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“…The researchers Khanday et al (2020) and Maakoul et al (2020) proposed a hybrid feature engineering technique to build a supervised machine learning model for the binary classification of a novel dataset of 5k tweets into propaganda and nonpropaganda classes. A total of 100 valuable features were extracted from the collected dataset, which is then split into 70% training and 30% testing set for performing ten-fold cross-validation.…”

Section: Related Workmentioning

confidence: 99%

“…Maakoul et al (2020) proposed a novel algorithm based on logistic regression to detect and analyze fake Facebook posts and comments related to the pandemic in the Moroccan context. The authors suggest using the LR algorithm because it executes based on the data provided and does not require previous theoretical rules to link the input and output of the model.…”

Section: Related Workmentioning

confidence: 99%

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic

Varma

Verma

Vijayvargiya

et al. 2021

IJICC

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PurposeThe rapid advancement of technology in online communication and fingertip access to the Internet has resulted in the expedited dissemination of fake news to engage a global audience at a low cost by news channels, freelance reporters and websites. Amid the coronavirus disease 2019 (COVID-19) pandemic, individuals are inflicted with these false and potentially harmful claims and stories, which may harm the vaccination process. Psychological studies reveal that the human ability to detect deception is only slightly better than chance; therefore, there is a growing need for serious consideration for developing automated strategies to combat fake news that traverses these platforms at an alarming rate. This paper systematically reviews the existing fake news detection technologies by exploring various machine learning and deep learning techniques pre- and post-pandemic, which has never been done before to the best of the authors’ knowledge.Design/methodology/approachThe detailed literature review on fake news detection is divided into three major parts. The authors searched papers no later than 2017 on fake news detection approaches on deep learning and machine learning. The papers were initially searched through the Google scholar platform, and they have been scrutinized for quality. The authors kept “Scopus” and “Web of Science” as quality indexing parameters. All research gaps and available databases, data pre-processing, feature extraction techniques and evaluation methods for current fake news detection technologies have been explored, illustrating them using tables, charts and trees.FindingsThe paper is dissected into two approaches, namely machine learning and deep learning, to present a better understanding and a clear objective. Next, the authors present a viewpoint on which approach is better and future research trends, issues and challenges for researchers, given the relevance and urgency of a detailed and thorough analysis of existing models. This paper also delves into fake new detection during COVID-19, and it can be inferred that research and modeling are shifting toward the use of ensemble approaches.Originality/valueThe study also identifies several novel automated web-based approaches used by researchers to assess the validity of pandemic news that have proven to be successful, although currently reported accuracy has not yet reached consistent levels in the real world.

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Towards Evaluating the COVID’19 related Fake News Problem: Case of Morocco

Cited by 15 publications

References 9 publications

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

When Infodemic Meets Epidemic: A Systematic Literature Review (Preprint)

A systematic survey on deep learning and machine learning approaches of fake news detection in the pre- and post-COVID-19 pandemic

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