Fake News vs Satire

Golbeck, Jennifer; Mauriello, Matthew Louis; Auxier, Brooke E.; Bhanushali, Keval H.; Bonk, Christopher; Bouzaghrane, Mohamed Amine; Buntain, Cody; Chanduka, Riya; Cheakalos, Paul; Everett, Jennine B.; Falak, Waleed; Gieringer, Carl; Graney, Jack; Hoffman, Kelly M.; Huth, Lindsay; Ma, Zhenya; Jha, Mayanka; Khan, M. Ali; Kori, Varsha; Lewis, Elo; Mirano, George; Mohn, William T.; Mussenden, S.; Nelson, Tammie M.; Mcwillie, Sean; Pant, Akshat; Shetye, Priya; Shrestha, Rusha; Steinheimer, Alexandra; Subramanian, Aditya; Visnansky, Gina

doi:10.1145/3201064.3201100

Cited by 91 publications

(32 citation statements)

References 7 publications

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“…Markowitz and Hancock (2014) demonstrated how linguistic patterns related to discourse dimensions could be used as cues to differentiate between fraudulent and genuine publications of the social psychologist Diederik Stapel's. Golbeck et al (2018) utilized a word-based classification approach based on the Naive Bayes Multinomial Algorithm to identify the linguistic nuances between fake and satirical articles. Levi et al (2019) proposed a machine learning method using semantic representation to identify fake news and satire's nuances.…”

Section: Related Workmentioning

confidence: 99%

Linguistic-Based Detection of Fake News in Social Media

Mahyoob¹,

Algaraady²,

Alrahaili³

2020

IJEL

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The tremendous growth and impact of fake news as a hot research field gained the public’s attention and threatened their safety in recent years. However, there is a wide range of developed fashions to detect fake contents, either those human-based approaches or machine-based approaches; both have shown inadequacy and limitations, especially those fully automatic approaches. The purpose of this analytic study of media news language is to investigate and identify the linguistic features and their contribution in analyzing data to detect, filter, and differentiate between fake and authentic news texts. This study outlines promising uses of linguistic indicators and adds a rather unconventional outlook to prior literature. It utilizes qualitative and quantitative data analysis as an analytic method to identify systematic nuances between fake and factual news in terms of detecting and comparing 16 attributes under three main linguistic features categories (lexical, grammatical, and syntactic features) assigned manually to news texts. The obtained datasets consist of publicly available right documents on the Politi-fact website and the raw (test) data set collected randomly from news posts on Facebook pages. The results show that linguistic features, especially grammatical features, help determine untrustworthy texts and demonstrate that most of the test news tends to be unreliable articles.

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Section: Related Workmentioning

confidence: 99%

Linguistic-Based Detection of Fake News in Social Media

Mahyoob¹,

Algaraady²,

Alrahaili³

2020

IJEL

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“…In this project, we investigate 246 fake news websites synthesized from three extant datasets [4,3,5]. We manually identify authors who have written for these sites during 2016 and release this dataset of author connections as part of our contribution in this work.…”

Section: Discussionmentioning

confidence: 99%

“…To construct a network of fake news sites, we synthesize a new dataset of 246 fake/alternative-news sites from recent studies: Golbeck et al [3], Guess et al [4], and Starbird [5]. These datasets contain 96, 92, and 79 fake or alternative sites respectively, and we find little overlap across these datasets, with any pair of datasets having no more than nine sites in common.…”

Section: Dataset Construction and Author Collectionmentioning

confidence: 99%

Analyzing a fake news authorship network

Buntain¹,

Golbeck²,

Auxier³

et al. 2019

iConference 2019 Proceedings

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This paper analyzes 246 fake news websites previously identified in three research projects. From this dataset, we extract a set of authors who have written for these sites in 2016, which we make publicly available. Applying a novel shared authorship construct, we analyze a network of fake news sites. This analysis shows a tight cluster of sites, with a trend of article reposting, wherein sites copy content from each other but preserve author bylines. We also show the most central authors, while associated with different sites, share common affiliations with a single site: Infowars.

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“…Its over-thetop approach and clear comedic context provide viewers with the cues to understand that these are jokes. Golbeck et al (2018) point out that the differences between satire and fake news are often difficult to discern, as consumers may not see the context or understand the joke. That line gets blurred easily, it is true, but the key is that the loss of contextespecially on the Internetsometimes accounts for this confusion of satire and false news stories.…”

Section: Fake News As Rumor Misinformation and Conspiracy Theorymentioning

confidence: 99%

Surveying fake news: Assessing university faculty’s fragmented definition of fake news and its impact on teaching critical thinking

et al. 2020

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This paper reports on the results of a survey of faculty members at California State University, Northridge (CSUN) in Los Angeles, California regarding their understanding of and familiarity with the concept of fake news. With very few studies published on the attitudes of teaching faculty at universities, this study is a unique approach to the issues facing educators, knowledge creators, and information specialists. The paper examines the origins of the term "fake news", the factors contributing to its current prevalence, and proposes a new definition. It also reports upon the attitudes that teaching faculty hold, and how they define fake news within their specific disciplines. Though nearly all surveyed faculty felt fake news was an important topic that impacted them professionally, the researchers also find that faculty across all disciplines and ranks, ages, and gender, hold widely differing conceptions of fake news. This lack of consensus may have future implications for students in particular and higher education in general and are worth exploring further.

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Fake News vs Satire

Cited by 91 publications

References 7 publications

Linguistic-Based Detection of Fake News in Social Media

Linguistic-Based Detection of Fake News in Social Media

Analyzing a fake news authorship network

Surveying fake news: Assessing university faculty’s fragmented definition of fake news and its impact on teaching critical thinking

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