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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 Page: 1 2 Hits 1 – 20 of 24 1 Comparing Features of Fabricated and Legitimate Political News in Digital Environments (2016-2017) Rubin, Victoria; Asubiaro, Toluwase Victor In: FIMS Presentations (2018) BASE Show details 2 Perceptions of Clickbait: A Q-Methodology Approach Chen, Yimin; Rubin, Victoria L In: FIMS Presentations (2017) BASE Show details 3 Comparative Stylistic Fanfiction Analysis: Popular and Unpopular Fics across Eleven Fandoms Rubin, Victoria L; Girouard, Vanessa In: Victoria Rubin (2017) BASE Show details 4 Truth and Deception at the Rhetorical Structure Level Rubin, Victoria L; Lukoianova, Tatiana In: Victoria Rubin (2017) BASE Show details 5 Pragmatic and Cultural Considerations for Deception Detection in Asian Languages Rubin, Victoria L In: Victoria Rubin (2017) BASE Show details 6 Discerning truth from deception: Human judgments and automation efforts Rubin, Victoria L; Conroy, Niall In: Victoria Rubin (2017) BASE Show details 7 Differences Over Discourse Structure Differences: A Reply to Urquhart and Urquhart Abrahamson, Jennie A; Rubin, Victoria L In: Victoria Rubin (2017) BASE Show details 8 Stretching Conceptual Structures in Classifications Across Languages and Cultures. Kwasnik, Barbara H.; Rubin, Victoria L. In: Victoria Rubin (2015) BASE Show details 9 Certainty Categorization Model Liddy, Elizabeth D.; Kando, Noriko; Rubin, Victoria L. In: Victoria Rubin (2015) BASE Show details 10 Towards News Verification: Deception Detection Methods for News Discourse Rubin, Victoria L.; Conroy, Niall J.; Chen, Yimin In: Victoria Rubin (2015) BASE Show details 11 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 12 Discerning Emotions in Texts Rubin, Victoria L.; Stanton, Jeffrey M.; Liddy, Elizabeth D. In: Victoria Rubin (2015) BASE Show details 13 Towards News Verification: Deception Detection Methods for News Discourse Conroy, Niall; Chen, Yimin; Rubin, Victoria L In: FIMS Presentations (2015) BASE Show details 14 Differences Over Discourse Structure Differences: A Reply to Urquhart and Urquhart Abrahamson, Jennie A; Rubin, Victoria L In: FIMS Publications (2015) BASE Show details 15 Truth and Deception at the Rhetorical Structure Level Rubin, Victoria L; Lukoianova, Tatiana In: FIMS Publications (2014) BASE Show details 16 Pragmatic and Cultural Considerations for Deception Detection in Asian Languages Rubin, Victoria L In: FIMS Publications (2014) BASE Show details 17 Comparative Stylistic Fanfiction Analysis: Popular and Unpopular Fics across Eleven Fandoms Rubin, Victoria L; Girouard, Vanessa In: FIMS Publications (2013) BASE Show details 18 Discerning truth from deception: Human judgments and automation efforts Rubin, Victoria L.; Conroy, Niall In: First Monday; Volume 17, Number 3 - 5 March 2012 ; 1396-0466 (2012) Abstract: Recent improvements in effectiveness and accuracy of the emerging field of automated deception detection and the associated potential of language technologies have triggered increased interest in mass media and general public. Computational tools capable of alerting users to potentially deceptive content in computer–mediated messages are invaluable for supporting undisrupted, computer–mediated communication and information practices, credibility assessment and decision–making. The goal of this ongoing research is to inform creation of such automated capabilities. In this study we elicit a sample of 90 computer–mediated personal stories with varying levels of deception. Each story has 10 associated human deception level judgments, confidence scores, and explanations. In total, 990 unique respondents participated in the study. Three approaches are taken to the data analysis of the sample: human judges, linguistic detection cues, and machine learning. Comparable to previous research results, human judgments achieve 50–63 percent success rates, depending on what is considered deceptive. Actual deception levels negatively correlate with their confident judgment as being deceptive (r = -0.35, df = 88, ρ = 0.008). The highest-performing machine learning algorithms reach 65 percent accuracy. Linguistic cues are extracted, calculated, and modeled with logistic regression, but are found not to be significant predictors of deception level, confidence score, or an authors’ ability to fool a reader. We address the associated challenges with error analysis. The respondents’ stories and explanations are manually content–analyzed and result in a faceted deception classification (theme, centrality, realism, essence, self–distancing) and a stated perceived cue typology. Deception detection remains novel, challenging, and important in natural language processing, machine learning, and the broader library information science and technology community. URL: https://doi.org/10.5210/fm.v17i3.3933 https://journals.uic.edu/ojs/index.php/fm/article/view/3933 BASE Hide details 19 Discerning truth from deception: Human judgments and automation efforts Rubin, Victoria L; Conroy, Niall In: FIMS Publications (2012) BASE Show details 20 Certainty Identification in Texts: Categorization Model and Manual Tagging Results Liddy, Elizabeth D.; Rubin, Victoria L.; Kando, Noriko In: School of Information Studies - Faculty Scholarship (2006) BASE Show details Page: 1 2

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