Using artificial intelligence techniques for detecting Covid-19 epidemic fake news in Moroccan tweets

Madani, Youness; Erritali, Mohammed; Bouikhalene, Belaid

doi:10.1016/j.rinp.2021.104266

Cited by 59 publications

(43 citation statements)

References 15 publications

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“…Previous and recent studies have almost exclusively focused on data from social media (e.g., Twitter) [8], fact-checking or reliable websites (e.g., snopes.com and politifact.com) [9], or existing datasets [10] which have the benefit to be cost-efficient. Due to the current difficult and unprecedented situation with the COVID-19 pandemic, never seen in the modern era [11], people have asked many questions about the novel coronavirus, such as the origin of the disease, treatment, prevention, cure, and transmission from or to pets, to face these challenges while staying informed and safe. In this study, we focus on news displayed by web search engines, since they represent the best tools for bringing up answers to people's current questions, extracting information related to COVID-19 outbreak, and proposing an approach based on both textual and uniform resource locator (URL) features to analyse and detect whether news is fake/misleading or reliable (real).…”

Section: Introductionmentioning

confidence: 99%

Detection of Fake News on COVID-19 on Web Search Engines

2021

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In early January 2020, after China reported the first cases of the new coronavirus (SARS-CoV-2) in the city of Wuhan, unreliable and not fully accurate information has started spreading faster than the virus itself. Alongside this pandemic, people have experienced a parallel infodemic, i.e., an overabundance of information, some of which is misleading or even harmful, which has widely spread around the globe. Although social media are increasingly being used as the information source, web search engines, such as Google or Yahoo!, still represent a powerful and trustworthy resource for finding information on the Web. This is due to their capability to capture the largest amount of information, helping users quickly identify the most relevant, useful, although not always the most reliable, results for their search queries. This study aims to detect potential misleading and fake contents by capturing and analysing textual information, which flow through search engines. By using a real-world dataset associated with recent COVID-19 pandemic, we first apply re-sampling techniques for class imbalance, and then we use existing machine learning algorithms for classification of not reliable news. By extracting lexical and host-based features of associated uniform resource locators (URLs) for news articles, we show that the proposed methods, so common in phishing and malicious URL detection, can improve the efficiency and performance of classifiers. Based on these findings, we suggest that the use of both textual and URL features can improve the effectiveness of fake news detection methods.

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Section: Introductionmentioning

confidence: 99%

Detection of Fake News on COVID-19 on Web Search Engines

2021

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“…In June 2020, a freely provided app named Wiqaytna, which means "our security" in Arabic, was generated by the MoH, the Ministry of the Interior, the ADD and the National Telecommunications Regulatory Agency (ANRT), working in partnership. This app, which uses Bluetooth technology on mobile devices, provides COVID-19 exposure notifications to facilitate COVID-19 contact tracing and tracking efforts (41). The app gathers information from infected individuals about the people they have previously been in contact with over a 21-day period.…”

Section: Tracking the Virus And Tracing Covid-19 Patients' Contact Networkmentioning

confidence: 99%

Moroccan Digital Health Response to the COVID-19 Crisis

Dehbi

Sedrati

Chaqsare

et al. 2021

Front. Public Health

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The COVID-19 pandemic and the draconian measures applied to limit its spread have accelerated the process of digitalizing many activities, including those within the health sector. In Morocco, a developing country in northern Africa, digital health has been deployed extensively, and in a remarkable way, to support the management of the current health crisis. Morocco is taking significant measures to become a key player in the process of achieving Sustainable Development Goals (SDG) goal 3. The government has comprehensively integrated digital technology throughout its coordinated containment and mitigation processes. These processes encompass testing and diagnostics; virus genomic surveillance; telecare of suspected and chronic patients; COVID-19 patient contact tracing and tracking; a laboratory information system for medical material dispatching, biological sample collection, and data processing nationwide; and smart vaccination management. Moreover, the pace of amending legislation for enabling efficient telemedicine practice has been achieved at a record-breaking. The successful implementation of all of these digital health strategies testify to the effectiveness of digitalization for managing the health aspects of the pandemic and for the future development of health systems in Morocco and in the African continent, where digital health and telemedicine is set to become the cornerstone of medical practice.

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“…Madani et al [25] focused on fake news that tweeted during the CORONA virus. They proposed a classification approach based on natural language processing, ML, and deep learning.…”

Section: Related Workmentioning

confidence: 99%

Detecting Fake News in Social Media Using Voting Classifier

Elsaeed

El-Daydamony

Elmogy³

et al. 2021

IEEE Access

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The availability of social media, blogs, and websites to everyone creates a lot of problems. False news is a critical issue that can affect individuals or entire countries. Fake news can be created and shared all over the world. The 2016 presidential election in the United States illustrates that problem. As a result, controlling social media is essential. Machine learning (ML) algorithms help to detect fake news automatically. This article proposes a framework for detecting fake news based on feature extraction and feature selection algorithms and a set of voting classifiers. The proposed system distinguishes fake news from real news. First, we preprocessed the data taking unnecessary characters and numbers and reducing the words in the dictionary (lemmatization). Second, we extracted some important features using two feature extraction types: the term frequency-inverse document frequency (TF-IDF) technique and the DOC2VEC algorithm, a word embedding technique. Third, the extracted characteristics were reduced with the help of the chi-square algorithm and the analysis of variance (ANOVA) algorithm. We used three data sets that are published online: Media-Eval, Fake-or-Real-News, and ISOT. To evaluate the proposed framework, we used five different performance metrics: accuracy (ACC), the area under the curve (AUC), precision, recall, and f1-score. Our system achieved 94.6% of ACC for the Fake-or-Real dataset. For the Media-Eval dataset, the system achieved 92.3% of ACC. For the ISOT dataset, the system achieved 100% of ACC. We contrast the proposed framework with several other classification algorithms. The experimental results show that the proposed framework outperforms the existing works in terms of ACC by 0.2% for the ISOT dataset. INDEX TERMSFake news, News classification, Voting classifier, Term frequency-inverse document frequency, Chi-square Eman Elsaeed received the B.Sc. degree in information technology department from the Faculty of Computers and Information, Mansoura University, Egypt, in 2014. Currently, she is an M.Sc. student at the Faculty of Computers and Information, Mansoura University, Egypt. Her research interests include Artificial intelligence, machine learning, and data mining.

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Using artificial intelligence techniques for detecting Covid-19 epidemic fake news in Moroccan tweets

Cited by 59 publications

References 15 publications

Detection of Fake News on COVID-19 on Web Search Engines

Detection of Fake News on COVID-19 on Web Search Engines

Moroccan Digital Health Response to the COVID-19 Crisis

Detecting Fake News in Social Media Using Voting Classifier

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