Comparing features of fabricated and legitimate political news in digital environments (2016‐2017)

Asubiaro, Toluwase Victor; Rubin, Victoria L.

doi:10.1002/pra2.2018.14505501100

Cited by 12 publications

(12 citation statements)

References 4 publications

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“…2. For a database of 137 political falsified news (matched to their likely 137 legitimate counterpart from which each news originated), see “POLIT-FALSE-n-LEGIT NEWS DB 2016-2017.” Available at: http://victoriarubin.fims.uwo.ca/news-verification/access-polit-false-n-legit-news-db-2016-2017/ (accessed December 15, 2018). The statistical differences in linguistic and stylistic features are described in Asubiaro and Rubin (2018).…”

Section: Notesmentioning

confidence: 99%

Disinformation and misinformation triangle

Rubin

2019

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Purpose The purpose of this paper is to treat disinformation and misinformation (intentionally deceptive and unintentionally inaccurate misleading information, respectively) as a socio-cultural technology-enabled epidemic in digital news, propagated via social media. Design/methodology/approach The proposed disinformation and misinformation triangle is a conceptual model that identifies the three minimal causal factors occurring simultaneously to facilitate the spread of the epidemic at the societal level. Findings Following the epidemiological disease triangle model, the three interacting causal factors are translated into the digital news context: the virulent pathogens are falsifications, clickbait, satirical “fakes” and other deceptive or misleading news content; the susceptible hosts are information-overloaded, time-pressed news readers lacking media literacy skills; and the conducive environments are polluted poorly regulated social media platforms that propagate and encourage the spread of various “fakes.” Originality/value The three types of interventions – automation, education and regulation – are proposed as a set of holistic measures to reveal, and potentially control, predict and prevent further proliferation of the epidemic. Partial automated solutions with natural language processing, machine learning and various automated detection techniques are currently available, as exemplified here briefly. Automated solutions assist (but not replace) human judgments about whether news is truthful and credible. Information literacy efforts require further in-depth understanding of the phenomenon and interdisciplinary collaboration outside of the traditional library and information science, incorporating media studies, journalism, interpersonal psychology and communication perspectives.

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Section: Notesmentioning

confidence: 99%

Disinformation and misinformation triangle

Rubin

2019

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“…Editors of trustworthy sources are possibly quite rigorous about removing language that seems too personal, while such processes do not play a role in the production of fabricated news items (see also the findings about source use). In a similar vein, Asubiaro and Rubin (2018) find that the use of (all types of) pronouns is typically higher in deceptive news. Applied to social media, the aforementioned study by Van Der Zee et al (2018) finds similar patterns (for third-person pronouns) for tweets by former President Trump that were established to be deceptive by The Washington Post.…”

Section: Use Of Pronounsmentioning

confidence: 66%

“…To develop methods of automated detection, scholars rely on benchmark labeled datasets (e.g., PolitiFact) that contain truthful and deceptive news content that has been fact-checked for its veracity. Based on such datasets, machine learning models are developed to automatically detect disinformation based on linguistic features, with varying degrees of success (e.g., Asubiaro and Rubin 2018;Wang 2017). Horne and Adali (2017), for example, use three separate datasets to study the linguistic features of real news, fake news, and satire.…”

Section: Use and Presence Of Emotionsmentioning

confidence: 99%

“…In general, the headlines of intentionally deceptive news items tend to be eyecatching, with a propensity for exaggeration, sensationalism, and scaremongering (Chen, Conroy, and Rubin 2015;Potthast et al 2016;Sahoo and Gupta 2021;Shu et al 2017;Tucker et al 2018). In addition, these headlines tend to be longer (Asubiaro and Rubin 2018 ;Liu et al 2019), using more capitalized words, proper nouns and verb phrases. By doing so, titles of fabricated news items try to get many points across, while titles of truthful texts most often opt for brief and general summary statements (Horne and Adali 2017, 764).…”

Section: Use Of Headlinesmentioning

confidence: 99%

“…However, it is reasonable to assume that the content of deceptive information is structurally different compared to fully correct information. For example, work in the field of linguistics suggests that deceptive language contains more tentative, angry, and emotional words than truthful language does (Asubiaro and Rubin 2018).…”

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confidence: 99%

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What Does Fake Look Like? A Review of the Literature on Intentional Deception in the News and on Social Media

et al. 2021

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This paper focuses on the content features of intentional deceptive information in the news (i.e., fake news) and on social media. Based on an extensive review of relevant literature (i.e., political journalism and communication, computational linguistics), we take stock of existing knowledge and present an overview of the structural characteristics that are indicative of intentionally deceptive information. We discuss the strength of underlying empirical evidence and identify underdeveloped areas of research. With this paper, we aim to contribute to the systematic study of intentional deception in the news and on social media and to help setting up new lines of research in which intentionally deceptive news items can be operationalized in consistent ways.

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