Detecting Misinformation Through Framing Theory: The Frame Element-Based Model

Abstract

In this paper, we delve into the rapidly evolving challenge of misinformation detection, specifically focusing on the nuanced manipulation of narrative frames, an under-explored area within the Artificial Intelligence (AI) community. The potential for Generative AI models to generate misleading narratives highlights the urgency of addressing this issue. Drawing from communication and framing theories, we posit that the presentation or ‘framing’ of accurate information can dramatically alter its interpretation, potentially leading to misinformation. In particular, the intricate user interaction in social networks plays an important role in this process, as these platforms provide an unsupervised environment for disseminating misinformation among individuals. We highlight this issue through real-world examples, demonstrating how shifts in narrative frames can transmute fact-based information into misinformation. To tackle this challenge, we propose an innovative approach that leverages the power of pre-trained large language models and deep neural networks to detect misinformation originating from accurate facts, which are portrayed under different frames. These advanced AI techniques offer unprecedented capabilities in identifying complex patterns within unstructured data, critical for examining the subtleties of narrative frames. The objective of this paper is to bridge a significant research gap in the AI domain, providing valuable insights and methodologies for tackling framing-induced misinformation, thus contributing to the advancement of responsible and trustworthy AI technologies. Several experiments are conducted, and the experimental results explicitly demonstrate the various impacts of elements of framing theory, thereby proving the rationale for applying framing theory to increase performance in misinformation detection.