Detecting misinformation through Framing Theory: the Frame Element-based
  Model
Guan Wang Rebecca Frederick Jinglong Duan William Wong Verica Rupar Weihua Li Quan Bai
In this paper, we delve into the rapidly evolving challenge of misinformation
detection, with a specific focus on the nuanced manipulation of narrative
frames - an under-explored area within the AI community. The potential for
Generative AI models to generate misleading narratives underscores the urgency
of this problem. 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. 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 leveraging the power of
pre-trained Large Language Models and deep neural networks to detect
misinformation originating from accurate facts 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 intensively conducted and experimental results explicitly
demonstrate the various impact of elements of framing theory proving the
rationale of applying framing theory to increase the performance in
misinformation detection.