Detecting Hoaxes, Frauds, and Deception in Writing Style Online

By Sadia Afroz, Michael Brennan, Rachel Greenstadt. IEEE Symposium on Security and Privacy, 2012

326 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/e210390cdc1da92ec3405f15d1bf79331cc237e8

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Citations in the corpus

Geometric deep learning (2016, background): “Content-based approaches, which are used in the majority of works on fake news detection, rely on linguistic (lexical and syntactical) features that can capture deceptive cues or writing styles (Afroz et al. (2012); Rubin et al. (2016); Rashkin et al. (2017); Potthast et al. (2017); Pérez-Rosas et a”
Fake News Detection on Social Media: A Data Mining Perspective (2017, background): “(KNN), and support vector machines (SVM), and then selecting the classiﬁer that performs the best [62; 75; 1].”
On the Feasibility of Internet-Scale Author Identification (2012, methodology): “of [28] for sharing the notes of their reimplementation of the Writeprints algorithm from [23] and the authors of [46] for sharing their Google profiles dataset.”
Fake News Detection on Social Media using Geometric Deep Learning (2019, background): “Content-based approaches, which are used in the majority of works on fake news detection, rely on linguistic (lexical and syntactical) features that can capture deceptive cues or writing styles [1, 32, 30, 29, 28].”
A Stylometric Inquiry into Hyperpartisan and Fake News (2017, background): “For example, Afroz et al. (2012) attempt to detect texts whose authors tried to obfuscate their writing style to deﬂect author identiﬁcation.”
Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “2011], essays and short text [Afroz et al. 2012; Braud and Søgaard 2017; Pérez-Rosas and Mihalcea 2014, 2015], images and videos [Abouelenien et al.”
Adversarial stylometry: Circumventing authorship recognition to preserve privacy and anonymity (2012, methodology): “This fraudulent blog was uncovered through forensic means but this research shows that stylometry demonstrates a strong correlation between the pseudonymous blog and writing samples by the true identity of the author [Afroz et al. 2012].”
A Survey of Fake News (2020, methodology): “2004b] and testimonies [Afroz et al. 2012], and recently used in fake news detection [Bond et al.”
The Limitations of Stylometry for Detecting Machine-Generated Fake News (2019, background): “…Detecting misinformation in text due to deception (Enos et al. 2007; Mihalcea and Strapparava 2009; Ott et al. 2011; Feng, Banerjee, and Choi 2012; Afroz, Brennan, and Greenstadt 2012), fake news (Rashkin et al. 2017; Pérez-Rosas et al. 2018), or other false or illegal content (Choshen et al.…”
A survey on fake news and rumour detection techniques (2019, methodology): “linguistic and visual features) were exploited in most SVM-based approaches to fake news and deception detection [1, 12, 72, 78, 108].”