Fake News Types and Detection Models on Social Media A State-of-the-Art Survey

Collins, Botambu; Hoang, Dinh Tuyen; Nguyên, Ngoc Thanh; Hwang, Dosam

doi:10.1007/978-981-15-3380-8_49

Cited by 28 publications

(25 citation statements)

References 16 publications

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“…Similar to work composed by [42][43][44], were they relied on fact checking platforms to collect real statements or verify their collected statements, we used four online fact-checking platforms: NO_RUMORS; 2 Falsoo; 3 AKEED, 4 and Fatabyyano. 5 These fact checking platforms are popular in the Arab region and Fatabyyano is certified by International Fact-Checking Network (IFCN). IFCN certifies that the website complies under the IFCN's code of ethics.…”

Section: Real News Dataset Collectionmentioning

confidence: 99%

“…Misinformation does not have harmful intent; disinformation has harmful intent; and malformation shares genuine information in order to cause harm [4]. It also differs in terms of its purpose: satire, parody, fabrication, manipulation, advertising, and propaganda [5]. The overarching commonality, however, boils down to facticity and deception [3].…”

Section: Introductionmentioning

confidence: 99%

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Arabic Fake News Detection Based on Textual Analysis

et al. 2022

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Over the years, social media has had a considerable impact on the way we share information and send messages. With this comes the problem of the rapid distribution of fake news which can have negative impacts on both individuals and society. Given the potential negative influence, detecting unmonitored ‘fake news’ has become a critical issue in mainstream media. While there are recent studies that built machine learning models that detect fake news in several languages, lack of studies in detecting fake news in the Arabic language is scare. Hence, in this paper, we study the issue of fake news detection in the Arabic language based on textual analysis. In an attempt to address the challenges of authenticating news, we introduce a supervised machine learning model that classifies Arabic news articles based on their context’s credibility. We also introduce the first dataset of Arabic fake news articles composed through crowdsourcing. Subsequently, to extract textual features from the articles, we create a unique approach of forming Arabic lexical wordlists and design an Arabic Natural Language Processing tool to perform textual features extraction. The findings of this study promises great results and outperformed human performance in the same task.

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Section: Real News Dataset Collectionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Arabic Fake News Detection Based on Textual Analysis

et al. 2022

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“…Collins et al [22] described various human-based and machine-based fake news detection models. On the human side, professional fact-checkers are experts in various disciplines who are capable of verifying the veracity of certain news items and decide whether such information is fake or authentic.…”

Section: Related Workmentioning

confidence: 99%

Sentiment Analysis for Fake News Detection

et al. 2021

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In recent years, we have witnessed a rise in fake news, i.e., provably false pieces of information created with the intention of deception. The dissemination of this type of news poses a serious threat to cohesion and social well-being, since it fosters political polarization and the distrust of people with respect to their leaders. The huge amount of news that is disseminated through social media makes manual verification unfeasible, which has promoted the design and implementation of automatic systems for fake news detection. The creators of fake news use various stylistic tricks to promote the success of their creations, with one of them being to excite the sentiments of the recipients. This has led to sentiment analysis, the part of text analytics in charge of determining the polarity and strength of sentiments expressed in a text, to be used in fake news detection approaches, either as a basis of the system or as a complementary element. In this article, we study the different uses of sentiment analysis in the detection of fake news, with a discussion of the most relevant elements and shortcomings, and the requirements that should be met in the near future, such as multilingualism, explainability, mitigation of biases, or treatment of multimedia elements.

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“…Prior research in fake news detection and machine learning has been conducted. According to (Collins et al, 2020) an overview of the various models in detecting fake news such as Machine learning, Natural Language Processing, Crowdsourced techniques, Expert fact-checker, as well as Hybrid Expert-Machine are introduced. The aforementioned research also examined differing types of fake news, which is an essential criterion for detection.…”

Section: Literature Reviewmentioning

confidence: 99%

An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging

Ramkissoon

Goodridge

2022

Preprint

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Mobile Adhoc Networks (MANETs) are utilised in a variety of mission critical situations and as such it is important to detect any fake news that exists in such networks. This research combines the power of Veracity, a unique, computational social system with that of Legitimacy, a dedicated ensemble learning technique, to detect Fake News in MANET Messaging. Veracity uses five algorithms namely, VerifyNews, CompareText, PredictCred, CredScore and EyeTruth for the capture, computation and analysis of the credibility and content data features using computational social intelligence. To validate Veracity, a dataset of publisher credibility-based and message content-based features is generated to predict fake news. To analyse the data features, Legitimacy, a unique ensemble learning prediction model is used. Four analytical methodologies are used to analyse these experimental results. The analysis of the results reports a good performance of the Veracity architecture combined with the Legitimacy model for the task of fake news detection in MANET Messaging.

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Fake News Types and Detection Models on Social Media A State-of-the-Art Survey

Cited by 28 publications

References 16 publications

Arabic Fake News Detection Based on Textual Analysis

Arabic Fake News Detection Based on Textual Analysis

Sentiment Analysis for Fake News Detection

An Ensemble Based Computational Social System for Fake News Detection in MANET Messaging

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