Web-based statistical fact checking of textual documents
By Amr Magdy, Nayer M. Wanas. 2010
59 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Communication, Information Systems, Natural Language Processing
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Citations in the corpus
- Survey of Hallucination in Natural Language Generation (2022, background): “Some literature attempt to use the whole web as the knowledge source [38, 102].”
- Fake News: A Survey of Research, Detection Methods, and Opportunities (2018, background): “…15http://service.account.weibo.com/ (sign in required) Manuscript submitted to ACM efficiency, but more complete knowledge (see related studies, e.g., in [Carlson et al. 2010; Dong et al. 2014; Magdy and Wanas 2010; Nakashole et al. 2011, 2012; Niu et al. 2012]). ii.”
- Fake News Detection on Social Media: A Data Mining Perspective (2017, methodology): “Open web sources are utilized as references that can be compared with given claims in terms of both the consistency and frequency [5; 50].”
- Beyond News Contents: The Role of Social Context for Fake News Detection (2017, background): “News content based models include i) knowledge-based: using external sources to fact-checking claims in news content [17, 37], and 2) style-based: capturing the manipulators in writing style, such as deception [7, 27] and non-objectivity [24].”
- A Stylometric Inquiry into Hyperpartisan and Fake News (2017, background): “However, the problem Etzioni et al., 2008 Magdy and Wanas, 2010 Ginsca et al., 2015 Wu et al., 2014 Ciampaglia et al., 2015 Shi and Weninger, 2016 Acemoglu et al., 2010 Budak et al., 2011 Nguyen et al., 2012 Kwon et al., 2013 Mocanu et al., 2015 Tambuscio et al., 2015 Wei et al., 2013 Chen et al.,…”
- A survey on fake news and rumour detection techniques (2019, methodology): “[66] has automated the process of web-based fact checking, by comparing facts extracted from a given document against facts extracted from URLs related to such documents.”