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Connective Comprehension: An individual differences study ...
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Considering Commonsense in Solving QA: Reading Comprehension with Semantic Search and Continual Learning
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4099 (2022)
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Re-Evaluating Early Memorization of the Qurʾān in Medieval Muslim Cultures
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In: Religions; Volume 13; Issue 2; Pages: 179 (2022)
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Knowledge Representation and Reasoning with an Extended Dynamic Uncertain Causality Graph under the Pythagorean Uncertain Linguistic Environment
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In: Applied Sciences; Volume 12; Issue 9; Pages: 4670 (2022)
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A Deep Fusion Matching Network Semantic Reasoning Model
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In: Applied Sciences; Volume 12; Issue 7; Pages: 3416 (2022)
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OntoDomus: A Semantic Model for Ambient Assisted Living System Based on Smart Homes
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In: Electronics; Volume 11; Issue 7; Pages: 1143 (2022)
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Is Early Bilingual Experience Associated with Greater Fluid Intelligence in Adults?
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In: Languages; Volume 7; Issue 2; Pages: 100 (2022)
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Design of an Artificial Intelligence of Things Based Indoor Planting Model for Mentha Spicata
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In: Processes; Volume 10; Issue 1; Pages: 116 (2022)
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Formalization of AMR Inference via Hybrid Logic Tableaux ...
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Child Social Understanding: How Theory of Mind Development is Influenced by Socio-Cultural Factors
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Neural-based Knowledge Transfer in Natural Language Processing
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Ranking Semantics for Argumentation Systems With Necessities
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In: IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence ; https://hal.archives-ouvertes.fr/hal-03002056 ; IJCAI 2020 - 29th International Joint Conference on Artificial Intelligence, Jan 2021, Yokohama / Virtual, Japan. pp.1912-1918, ⟨10.24963/ijcai.2020/265⟩ (2021)
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Ontological Formalisation of Mathematical Equations for Phenomic Data Exploitation
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In: The Semantic Web: ESWC 2021 Satellite Events ; https://hal.inrae.fr/hal-03408000 ; Ruben Verborgh; Anastasia Dimou; Aidan Hogan; Claudia d'Amato; Ilaria Tiddi; Arne Bröring; Simon Mayer; Femke Ongenae; Riccardo Tommasini; Mehwish Alam. The Semantic Web: ESWC 2021 Satellite Events, 12739, Springer International Publishing, pp.176-185, 2021, Lecture Notes in Computer Science, 978-3-030-80417-6. ⟨10.1007/978-3-030-80418-3_30⟩ (2021)
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Multimodal Conversation Modeling via Neural Perception, Structure Learning, and Communication
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How Do Language Intensity and Artificial Intelligence (AI) Affect Perceptions of Fact-checking Messages and Evaluations of Fact-checking Agencies?
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Xue, Haoning. - : eScholarship, University of California, 2021
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Abstract:
Fact-checking agencies are essential to correct misinformation and inform the public, while how people evaluate these agencies and their messages remain unclear (Brandtzaeg et al., 2017). Two factors about the messages and the sources – two essential factors in the theories of persuasion – were examined: language intensity of fact-checking labels and AI as a fact-checking agency. Language intensity, a linguistic feature that reflects message specificity and emotionality, may implicitly influence the acceptance of misinformation corrections and behavior intentions (Bowers, 1963). While AI has the potential to automate the fact-checking process and improve the acceptance of misinformation corrections as an unbiased automated decision maker, the social acceptance of AI in fact checking is unclear. This study investigated how language intensity and fact-checking agency (human vs. AI) influence the evaluations of fact-checking messages and agencies with an observational study of fact-checking messages on social media (N = 33755) and two online experiments (combined N = 1449) in the U.S. Both the observational study and the experiments showed that fact-checking messages with high language intensity would elicit low message credibility, while this effect diminished when the messages were counter-attitudinal in the experiments. Besides, participants perceived AI fact-checking agencies the same as human agencies. Individual differences in conspiracy ideation, political ideology and demographics significantly affected message credibility and engagement intentions as well. These findings suggest that language nuances such as language intensity in fact-checking messages affected message perception and the acceptance of misinformation corrections. Theoretical and practical implications were discussed in detail.
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Keyword:
AI perception; Communication; corrections; fact-checking; misinformation; motivated reasoning
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URL: https://escholarship.org/uc/item/95j036c6
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