Who Posts Fake News? Authentic and Inauthentic Spreaders of Fabricated News on Facebook and Twitter

Dourado, Tatiana

doi:10.1080/17512786.2023.2176352

Cited by 4 publications

(3 citation statements)

References 45 publications

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“…However, as outlined above, the term "fake news" unhelpfully limits our understanding of problematic news content. This is because verifiably "false" news, as tracked by Vosoughi et al (2018) or Dourado (2023), is only a subset of such problematic content. Other forms include outright conspiracist news sites, hyperpartisan outlets that use selective sourcing and bad-faith news frames to push specific political agendas, state-backed disinformation campaigns, as well as more mundane forms of false information (i.e., misinformation) sharing on social media.…”

Section: Approaches To Studying the Spread Of Problematic "News" Contentmentioning

confidence: 99%

“…This produces valuable results, but its observations cannot easily be generalized, for example, to hyperpartisan news that is not explicitly false, but instead presents facts selectively and out of context, or to biased news commentary that makes its claims without providing a factual basis and is therefore more difficult to debunk effectively. Likewise, Dourado (2023) produced a compelling account of the nature of over 1,000 online accounts that shared any of 57 fact-checked "fake news" articles relating to the 2018 Brazilian presidential election, but this approach cannot capture the circulation of other problematic content that did not come to the attention of fact-checking organizations. We do not intend to diminish the importance and rigor of these studies, but point out their (acknowledged) limitations: they rely on a small database of fact-checked "fake" articles and limit their scope to a singular event.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Computational Communication Methods for Examining Problematic News-Sharing Practices on Facebook at Scale

Angus,

Bruns,

Hurcombe

et al. 2023

Social Media + Society

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Social media, in general, and Facebook in particular, have been clearly identified as important platforms for the dissemination of mis- and disinformation and related problematic content. However, the patterns and processes of such dissemination are still not sufficiently understood. We detail a novel computational methodology that focusses on the identification of high-profile vectors of “fake news” and other problematic information in public Facebook spaces. The method enables examination of networks of content sharing that emerge between public pages and groups, and external sources, and the study of longitudinal dynamics of these networks as interests and allegiances shift and new developments (such as the COVID-19 pandemic or the US presidential elections) drive the emergence or decline of dominant themes. Through a case study of content captured between 2016 and 2021, we demonstrate how this methodology allows the development of a new and more comprehensive picture of the overall impact of “fake news,” in all its forms, on contemporary societies.

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Section: Approaches To Studying the Spread Of Problematic "News" Contentmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Computational Communication Methods for Examining Problematic News-Sharing Practices on Facebook at Scale

Angus,

Bruns,

Hurcombe

et al. 2023

Social Media + Society

View full text Add to dashboard Cite

show abstract

“…The spread of fake news can have a huge detrimental effect on social issues and threatens to disrupt the news ecosystem, people, and society. The most famous fake news example is in the 2016 United States presidential election was widely circulated on Facebook [2]. Rapidly increasing the number of users of high-speed internet and hand-held devices and advanced digital media.…”

Section: Introductionmentioning

confidence: 99%

Web-Based Machine Learning Framework for Authenticating News: A Fake News Detection System

Rahman,

Sumon

et al. 2024

Preprint

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Today fake news is curse for the society. Fake news has a bad impact on every human life. Disinformation is being spread more and more via fake news, which manipulates people's perceptions and knowledge to affect their awareness and judgment. Social media are mainly responsible for propagating fake news. Misinformation disseminated through digital platforms creates not only confusion but also cultivates biased perspectives among individuals. To prevent the problematic effects of falsified information we have to identify it first more quickly. This paper suggests the implementation of a supervised machine learning system designed to identify fake news through website analysis in this research, TF-IDF Vectorizer model is utilized for features extraction and thirteen models such as Random Forest (RF), Decision Tree (DT), Bernoulli Naïve Bayes (BNB), Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Linear Support Vector Classifier (LSVC), K-Nearest Neighbor (KNN), XGBoost (XB), Multilayer Perceptron (MLP), Extra Trees (ET), AdaBoost (AB), Gradient Boosting (GB) classifier are used to classifier fake news. The proposed approach used about 20,800 groups of data to test the suggested framework on a significant number of articles published through Kaggle. The recommended Linear Support Vector Classifier model outperforms the other twelve techniques, according to numerous approaches on this dataset. LSVC algorithm gives the highest 99.38% accuracy to classifier the fake news from monolingual text dataset.

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Disinformation Studies: Global Perspectives

Jerónimo,

Amaral,

Correia

2023

Journalism Practice

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Who Posts Fake News? Authentic and Inauthentic Spreaders of Fabricated News on Facebook and Twitter

Cited by 4 publications

References 45 publications

Computational Communication Methods for Examining Problematic News-Sharing Practices on Facebook at Scale

Computational Communication Methods for Examining Problematic News-Sharing Practices on Facebook at Scale

Web-Based Machine Learning Framework for Authenticating News: A Fake News Detection System

Disinformation Studies: Global Perspectives

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