An overview of online fake news: Characterization, detection, and discussion

Zhang, Xichen; Ghorbani, Ali A.

doi:10.1016/j.ipm.2019.03.004

Cited by 662 publications

(438 citation statements)

References 33 publications

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“…There has been a considerable amount of research done on the influence of fake news on the political environment [14,18]. By creating false political statements, voters can be convinced or persuaded to change their opinions [3]. Critics reported that in the national election in the UK (regarding the nation's withdrawal out of the EU) and the 2016 presidential election in the US, a number of false information was shared on social media platforms that have influenced the outcome of the results [8,22].…”

Section: Political Factorsmentioning

confidence: 99%

“…Webpages are purposely created to resemble real webpages for political gain [3]. News sources with URLs similar to the real website URL have been known to spread political fake news pieces, which could influence the opinion of the public [10].…”

Section: Political Factorsmentioning

confidence: 99%

“…In recent years, the rapid growth of fake news have led to the belief that cyborgs and bots are used to increase the spreading of misinformation on social media [22]. In the 2016 US election social bots were used to lead social media users to fake news websites to influence their opinions on the candidates [3]. Hundreds of bots were created in the 2016 US presidential elections to lure people to websites with false information [3] These social bots can spread information through social media platforms and participate in online social communities [3].…”

Section: Political Factorsmentioning

confidence: 99%

“…Studies debating the trustworthiness of information and veracity analytics of online content have increased recently due to the rise in fake news stories [37]. Social media has become a useful way for individuals to share information and opinions about various topics [3]. Unfortunately, many users share information with malicious intent [3].…”

Section: Malicious Factorsmentioning

confidence: 99%

“…The increase in use of social media exposes users to misleading information, satire and fake advertisements [3]. Fake news or misinformation is defined as fabricated information presented as the truth [6].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Systematic Review on Fake News Themes Reported in Literature

Celliers

Hattingh

2020

Lecture Notes in Computer Science

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In this systematic literature review, a study of the factors involved in the spreading of fake news, have been provided. In this review, the root causes of the spreading of fake news are identified to reduce the encouraging of such false information. To combat the spreading of fake news on social media, the reasons behind the spreading of fake news must first be identified. Therefore, this literature review takes an early initiative to identify the possible reasons behind the spreading of fake news. The purpose of this literature review is to identify why individuals tend to share false information and to possibly help in detecting fake news before it spreads.

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Section: Political Factorsmentioning

confidence: 99%

Section: Political Factorsmentioning

confidence: 99%

Section: Political Factorsmentioning

confidence: 99%

Section: Malicious Factorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Review on Fake News Themes Reported in Literature

Celliers

Hattingh

2020

Lecture Notes in Computer Science

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Are face masks a partisan issue during the COVID‐19 pandemic? Differentiating political ideology and political party affiliation

Howard

2021

Int J Psychol

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Popular press articles have asserted that those with certain political orientations are less likely to wear face masks during the COVID‐19 pandemic. We propose that this relation is due to differential information shared by political parties rather than values associated with face mask wearing. We further propose that, when assessed together, political party affiliation (e.g., Republican, Democrat) but not political ideology (e.g., conservative, liberal) predicts face mask wearing, and this effect is mediated by perceptions of efficacy doubts but not perceptions that face masks infringe upon the wearer's independence. We performed a three‐wave, time‐separated survey study with 226 participants. Each proposal was supported. When assessed together, political party affiliation but not political ideology significantly predicted face mask wearing, and a significant indirect effect was observed via perceptions of efficacy doubts but not independence. Our results support that face mask wearing is a unique preventative action, which should be understood using political theory.

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A hybrid intelligent model for early validation of infectious diseases: An explorative study of machine learning approaches

Bahaj

2023

Microscopy Res & Technique

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Literature reports several infectious diseases news validation approaches, but none is economically effective for collecting and classifying information on different infectious diseases. This work presents a hybrid machine‐learning model that could predict the validity of the infectious disease's news spread on the media. The proposed hybrid machine learning (ML) model uses the Dynamic Classifier Selection (DCS) process to validate news. Several machine learning models, such as K‐Neighbors‐Neighbor (KNN), AdaBoost (AB), Decision Tree (DT), Random Forest (RF), SVC, Gaussian Naïve Base (GNB), and Logistic Regression (LR) are tested in the simulation process on benchmark dataset. The simulation employs three DCS process methods: overall Local Accuracy (OLA), Meta Dynamic ensemble selection (META‐DES), and Bagging. From seven ML classifiers, the AdaBoost with Bagging DCS method got a 97.45% high accuracy rate for training samples and a 97.56% high accuracy rate for testing samples. The second high accuracy was obtained at 96.12% for training and 96.45% for testing samples from AdaBoost with the Meta‐DES method. Overall, the AdaBoost with Bagging model obtained higher accuracy, AUC, sensitivity, and specificity rate with minimum FPR and FNR for validation.

show abstract

An overview of online fake news: Characterization, detection, and discussion

Cited by 662 publications

References 33 publications

A Systematic Review on Fake News Themes Reported in Literature

A Systematic Review on Fake News Themes Reported in Literature

Are face masks a partisan issue during the COVID‐19 pandemic? Differentiating political ideology and political party affiliation

A hybrid intelligent model for early validation of infectious diseases: An explorative study of machine learning approaches

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