Language, Credibility, and AI-Assisted Verification: Evidence from Jordanian Digital Journalism

The rapid circulation of misleading information through digital media has made credibility assessment increasingly challenging for journalists and audiences. This study examines how linguistic and visual credibility cues contribute to the appearance of reliability in misleading digital news and explores how AI-assisted systems respond to these cues in Arabic and English. The study adopts a qualitative comparative case-study design based on five prominent and analytically suitable misinformation cases documented by AKEED, the Jordanian Media Credibility Monitor, in 2026. The cases represent different mechanisms of misleading communication, including AI-related visual content, numerical claims, recycled visual material, institutional attribution, and financial interpretation. Each case was evaluated by an AI system in Arabic and English using the same verification-oriented prompt structure. The findings from the five cases suggest that AI-assisted verification may help identify questionable claims and possible verification pathways, although the responses varied in their treatment of evidence, context, and interpretation. Greater challenges emerge when misleadingness depends on contextual displacement or unsupported interpretation. The study argues that AI can support digital journalism and verification, but effective assessment continues to require source checking, contextual knowledge, and human editorial judgment.