Fake news.

By B. Morton. Marine Pollution Bulletin, 2018

950 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/4fbc5fef2404baa04138e3ff25f84f5cbc1e76c4

How researchers see this paper

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

Citations in the corpus

Fake news, disinformation and misinformation in social media: a review (2023, unknown): “…2020c), false information (Kumar and Shah 2018; Guo et al. 2020; Habib et al. 2019), information disorder (Shu et al. 2020c; Wardle and Derakhshan 2017; Wardle 2018; Derakhshan and Wardle 2017), information warfare (Gua-dagno and Guttieri 2021) and information pollution (Meel and Vishwakarma 2020).”
Effects of COVID-19 Misinformation on Information Seeking, Avoidance, and Processing: A Multicountry Comparative Study (2020, background): “Researchers also differentiate misinformation from misperception and disinformation: Misperception is holding a belief that is incorrect or false (Southwell et al., 2018), whereas disinformation is driven by the intention to deceive (Wardle, 2017).”
Fake News Detection Using Machine Learning Ensemble Methods (2020, background): “The ﬁrst dataset is ISOT Fake News Dataset [23]; the second and third datasets are publicly available at Kaggle [24, 25].”
“Fake News” Is Not Simply False Information: A Concept Explication and Taxonomy of Online Content (2019, background): “…we now have a seemingly simple dictionary definition of “fake news” as “false stories that appear to be news, spread on the Internet or using other media, usually created to influence political views or as a joke” (Fake News, 2018), determining what is and what is not false is rather complex.”
Combating Fake News (2019, background): “…a phenomenon of a dimension that [they] have not seen before”, This definition allows us to capture the different types of fake news identified in [119] which can be differentiated by the means employed to falsify information, such as fabricated content (completely false), misleading content…”
On the Importance of Delexicalization for Fact Verification (2019, methodology): “…are: (1) To verify that models trained on lexicalized data transfer poorly, we implement a domain transfer experiment where a state-of-the-art RTE model (Parikh et al., 2016) is trained on the FEVER data, and tested on the Fake News Challenge (FNC) (Pomerleau and Rao, 2017) dataset, and vice versa.”
A Survey on Natural Language Processing for Fake News Detection (2018, background): “There are 7 different types of fake news as defined by Claire Wardle (Wardle, 2017).”