Tracing truth: How traceability enhances fake news detection design

Abstract The spread of fake news poses significant challenges across social, political, and economic domains. Existing countermeasures, like media literacy and fact-checking, have shown limited effectiveness due to user skepticism and cognitive biases. To address this issue, we introduce decision support traceability (DST), which integrates system traceability (explainable AI) and object traceability (chain of sources) to improve users’ ability to assess information credibility. System traceability enhances transparency by explaining AI-generated classifications, while object traceability enables users to track news articles’ origin and evolution. Prior research has examined these mechanisms separately, but their combined effect on disinformation detection remains underexplored. We hypothesize that their integration results in superior performance in identifying fake news. Using design science research (DSR) methodology, we developed and tested a prototype incorporating DST. A controlled online experiment compared three treatments: explainable AI, chain of sources, and their combination. Results show that while both mechanisms independently improve disinformation detection, their combination yields the highest accuracy, demonstrating the complementary nature of DST. This study advances information systems research by empirically validating DST as a unified framework for supporting news credibility assessments. The findings provide actionable design knowledge, emphasizing the need for AI transparency and provenance-tracking features to combat digital disinformation.