Analyzing and distinguishing fake and real news to mitigate the problem of disinformation

Vereshchaka, Alina; Cosimini, Seth; Dong, Wen

doi:10.1007/s10588-020-09307-8

Cited by 18 publications

(12 citation statements)

References 24 publications

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“…The fake news problem has been addressed by researchers from various perspectives related to different topics. These topics include, but are not restricted to, social science studies, which investigate why and who falls for fake news (Altay et al 2022;Batailler et al 2022;Sterret et al 2018;Badawy et al 2019;Weiss et al 2020;Guadagno and Guttieri 2021), whom to trust and how perceptions of misinformation and disinformation relate to media trust and media consumption patterns (Hameleers et al 2022), how fake news differs from personal lies (Chiu and Oh 2021;Escolà-Gascón 2021), examine how can the law regulate digital disinformation and how governments can regulate the values of social media companies that themselves regulate disinformation spread on their platforms (Marsden et al 2020;Schuyler 2019;Vasu et al 2018;Burshtein 2017;Waldman 2017;Alemanno 2018;Verstraete et al 2017), and argue the challenges to democracy (Jungherr and Schroeder 2021); Behavioral interventions studies, which examine what literacy ideas mean in the age of dis/ mis-and malinformation (Carmi et al 2020), investigate whether media literacy helps identification of fake news (Jones-Jang et al 2021) and attempt to improve people's news literacy (Apuke et al 2022;Dame Adjin-Tettey 2022;Nagel 2022;Jones-Jang et al 2021;Mihailidis and Viotty 2017;García et al 2020) by encouraging people to pause to assess credibility of headlines (Fazio 2020), promote civic online reasoning (McGrew 2020;McGrew et al 2018) and critical thinking (Lutzke et al 2019), together with evaluations of credibility indicators (Bhuiyan et al 2020;Nygren et al 2019;Sh...…”

Section: Contributionmentioning

confidence: 99%

Fake news, disinformation and misinformation in social media: a review

Aïmeur

Amri

Brassard

2023

Soc. Netw. Anal. Min.

133

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Online social networks (OSNs) are rapidly growing and have become a huge source of all kinds of global and local news for millions of users. However, OSNs are a double-edged sword. Although the great advantages they offer such as unlimited easy communication and instant news and information, they can also have many disadvantages and issues. One of their major challenging issues is the spread of fake news. Fake news identification is still a complex unresolved issue. Furthermore, fake news detection on OSNs presents unique characteristics and challenges that make finding a solution anything but trivial. On the other hand, artificial intelligence (AI) approaches are still incapable of overcoming this challenging problem. To make matters worse, AI techniques such as machine learning and deep learning are leveraged to deceive people by creating and disseminating fake content. Consequently, automatic fake news detection remains a huge challenge, primarily because the content is designed in a way to closely resemble the truth, and it is often hard to determine its veracity by AI alone without additional information from third parties. This work aims to provide a comprehensive and systematic review of fake news research as well as a fundamental review of existing approaches used to detect and prevent fake news from spreading via OSNs. We present the research problem and the existing challenges, discuss the state of the art in existing approaches for fake news detection, and point out the future research directions in tackling the challenges.

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Section: Contributionmentioning

confidence: 99%

Fake news, disinformation and misinformation in social media: a review

Aïmeur

Amri

Brassard

2023

Soc. Netw. Anal. Min.

133

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“…This model has achieved higher accuracy than other existing approaches to datasets. In 2020, Vereshchaka et al (2020) have solved the issue of predicting fake news by getting the socio-cultural and textual characteristics of fake news features and by analyzing and detecting fake news features. Further, data analytics was investigated for constructing a concordance of phase and word frequency.…”

Section: Existing Fake News Detection Model Approachesmentioning

confidence: 99%

“…Few fake news detection models use social context features (Shu et al 2019) for getting suitable features from the news content. N-gram (Vereshchaka et al 2020;Agarwal et al 2020;Saleh et al 2021;Kaliyar et al 2021b) generates words and characters from contents with several n-gram orders. Finally, the N-gram vectors are grouped for getting one feature vector for each information.…”

Section: Nlp Techniques Used In Fake News Detectionmentioning

confidence: 99%

“…Fake news can have several ranges of impacts by misleading or influencing governments or whole populations (Kim and Ko November 2021). Some of the techniques in detecting fake news utilize several approaches like machine learning techniques, language techniques, and knowledge-based techniques (Vereshchaka et al 2020). Some of the strong social media platforms are Twitter, YouTube, Instagram, Facebook, and WhatsApp have offered news and entertainment with the rising employment of mobile devices and simpler WiFi connections (Ribeiro Bezerra 2021).…”

Section: Introductionmentioning

confidence: 99%

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A systematic literature review and existing challenges toward fake news detection models

Shah

Ganatra

2022

Soc. Netw. Anal. Min.

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Emerging of social media creates inconsistencies in online news, which causes confusion and uncertainty for consumers while making decisions regarding purchases. On the other hand, in existing studies, there is a lack of empirical and systematic examination observed in terms of inconsistency regarding reviews. The spreading of fake news and disinformation on social media platforms has adverse effects on stability and social harmony. Fake news is often emerging and spreading on social media day by day. It results in influencing or annoying and also misleading nations or societies. Several studies aim to recognize fake news from real news on online social media platforms. Accurate and timely detection of fake news prevents the propagation of fake news. This paper aims to conduct a review on fake news detection models that is contributed by a variety of machine learning and deep learning algorithms. The fundamental and well-performing approaches that existed in the past years are reviewed and categorized and described in different datasets. Further, the dataset utilized, simulation platforms, and recorded performance metrics are evaluated as an extended review model. Finally, the survey expedites the research findings and challenges that could have significant implications for the upcoming researchers and professionals to improve the trust worthiness of automated fake news detection models.

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“…The idea of mass sharing with the consensus of the general public, despite thorough checking on the authenticity of the information, forms a false perception which makes the publication. Such information is usually disseminated clandestinely on social media platforms as an effort to obfuscate the truth and eventually, manipulate public opinion (Vereshchaka et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

The Extrinsic Effects of Unauthenticated Publications on Social Media on Readers’ Judgement and Perception

Teckwani

Wei

2021

MJSSH

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The dissemination of unauthenticated publications on social media platforms has increased over the years and the factors, congruent to the extrinsic effects of these publications, are less studied and analyzed. It is important to understand the relationship between social media content deception and the sharing behavior of all the readers who engage with it on a daily basis. This research studies the perception of 16 Malaysian individuals on the antecedents of falling prey and sharing fake news as well as their perceptions on the general repercussions that are deemed prominent. Emphasizing on the prevalence of mass sharing, these publications continue to significantly impact readers with time, as exemplified in the findings of the research. Study findings also indicate that crucial periods, such as the COVID-19 pandemic, inculcate curiosity and vulnerability and thus making it easier for readers to fall prey to social media postings that serve as a facade for all misinformation and disinformation.

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Analyzing and distinguishing fake and real news to mitigate the problem of disinformation

Cited by 18 publications

References 24 publications

Fake news, disinformation and misinformation in social media: a review

Fake news, disinformation and misinformation in social media: a review

A systematic literature review and existing challenges toward fake news detection models

The Extrinsic Effects of Unauthenticated Publications on Social Media on Readers’ Judgement and Perception

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