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

Author(s)/Creator(s)

Regina Luttrell, Syracuse UniversityFollow
Jason Davis, Syracuse UniversityFollow
Carrie Welch, Syracuse UniversityFollow

ORCID

Regina Luttrell: 0000-0003-2328-7414 | Jason Davis: 0000-0002-0265-2219 | Carrie Welch: 0009-0000-8339-4225

Document Type

Article

Date

2025

Keywords

artificial intelligence, detection, semantics, journalism, social media

Language

english

Disciplines

Film and Media Studies

Description/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.

Recommended Citation

Luttrell, R., Davis, J., & Welch, C. (2025). Social Media Semantics: Enhancing Manipulated Media Detection Through An Artificial Intelligence Weakness. Electronic News, 0(0). https://doi.org/10.1177/19312431251355220

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.