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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 7 of 7 1 Stretching Conceptual Structures in Classifications Across Languages and Cultures. Kwasnik, Barbara H.; Rubin, Victoria L. In: Victoria Rubin (2015) BASE Show details 2 Certainty Categorization Model Liddy, Elizabeth D.; Kando, Noriko; Rubin, Victoria L. In: Victoria Rubin (2015) BASE Show details 3 Towards News Verification: Deception Detection Methods for News Discourse Rubin, Victoria L.; Conroy, Niall J.; Chen, Yimin In: Victoria Rubin (2015) BASE Show details 4 Certainty Identification in Texts: Categorization Model and Manual Tagging Results Liddy, Elizabeth D.; Rubin, Victoria L.; Kando, Noriko In: Victoria Rubin (2015) BASE Show details 5 Discerning Emotions in Texts Rubin, Victoria L.; Stanton, Jeffrey M.; Liddy, Elizabeth D. In: Victoria Rubin (2015) BASE Show details 6 Towards News Verification: Deception Detection Methods for News Discourse Conroy, Niall; Chen, Yimin; Rubin, Victoria L In: FIMS Presentations (2015) Abstract: News verification is a process of determining whether a particular news report is truthful or deceptive. Deliberately deceptive (fabricated) news creates false conclusions in the readers’ minds. Truthful (authentic) news matches the writer’s knowledge. How do you tell the difference between the two in an automated way? To investigate this question, we analyzed rhetorical structures, discourse constituent parts and their coherence relations in deceptive and truthful news sample from NPR’s “Bluff the Listener”. Subsequently, we applied a vector space model to cluster the news by discourse feature similarity, achieving 63% accuracy. Our predictive model is not significantly better than chance (56% accuracy), though comparable to average human lie detection abilities (54%). Methodological limitations and future improvements are discussed. The long-term goal is to uncover systematic language differences and inform the core methodology of the news verification system. Keyword: Computational Linguistics; deception detection; Journalism Studies; Library and Information Science; Linguistics; Mass Communication; news verification; Semantics and Pragmatics; truth identification URL: https://ir.lib.uwo.ca/cgi/viewcontent.cgi?article=1046&context=fimspres https://ir.lib.uwo.ca/fimspres/46 BASE Hide details 7 Differences Over Discourse Structure Differences: A Reply to Urquhart and Urquhart Abrahamson, Jennie A; Rubin, Victoria L In: FIMS Publications (2015) BASE Show details

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