Deception detection for news: Three types of fakes

By Victoria L. Rubin, Yimin Chen, Niall Conroy. ASIS&T Annual Meeting, 2015

520 citations recorded by the source corpus.

Subfields: Information Systems, Sociology and Political Science, Signal Processing

View Paper: https://www.semanticscholar.org/paper/77552068c33783e8a1bc35b8d3018edbd825ed92

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

In Sociology and Political Science

In Signal Processing

Citations in the corpus

A survey on fake news and rumour detection techniques (2019, background): “In [79], the authors have provided an interesting overview of key requirements for a reliable fake news detection corpus.”
Detecting opinion spams and fake news using text classification (2018, background): “Rubin et al. 29 also proposed a model to identify satire and humor news articles.”
Separating Facts from Fiction: Linguistic Models to Classify Suspicious and Trusted News Posts on Twitter (2017, methodology): “However, satirical news and hoaxes may also be harmful, especially when they are shared out of context (Rubin et al., 2015).”
Automatic deception detection: Methods for finding fake news (2015, background): “More recently, efforts of methodological cross-pollination and hybrid approaches have produced promising results (Rubin et al., 2015A).”
Truth of Varying Shades: Analyzing Language in Fake News and Political Fact-Checking (2017, background): “Analysis indicates that falsehoods often arise from subtle differences in phrasing rather than outright fabrication (Rubin et al., 2015).”
CSI: A Hybrid Deep Model for Fake News Detection (2017, methodology): “[34] explained that there are many types of fake news, each with dierent potential textual indicators.”
Automatic Detection of Fake News (2017, background): “re are three important lines of research into the automated classiﬁcation of genuine and fake news items. First, on a conceptual level, a distinction has been made between ’three types of fake news’ (Rubin et al., 2015): serious fabrications (i.e. news items about false and nonexisting events or inf”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “Other literature directly treats deceptive news as fake news [66], which includes serious fabrications, hoaxes, and satires.”
A Survey of Fake News (2020, background): “2017] (deceptive news), or “a news article or message published and propagated through media, carrying false information regardless of the means and motives behind it,” which overlaps with false news, disinformation [Kshetri and Voas 2017], misinformation [Kucharski 2016], satire news [Rubin et al. ”
Detecting Fake News in Social Media Networks (2018, background): “For instance, some authors have observed that fake news is no longer a preserve of the marketing and public relations departments [21].”