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Abstract

As fake news and disinformation continue to proliferate in digital journalism, the development of artificial intelligence (AI) analytics to identify synthetic content inconsistencies is increasingly important. This study explores trained AI analytics’ struggle to detect semantic gaps within AI-generated media. Findings reinforce human semantic capabilities and a direction for detection tools. AI analytics designed for semantic detection-related tasks are evaluated through the application of the Theory of Content Consistency, with insights for combatting social media news truth erosion.

Keywords

artificial intelligence detection semantics journalism social media

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Social Media Semantics: Enhancing Manipulated Media Detection Through An Artificial Intelligence Weakness

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