A Lightweight and Effective Polynomial Network for Multimodal Fake News and Sarcasm Detection

What is it about?

This paper address the growing problem of misleading posts on social media that mix text and images. They introduce a new model that treats the text and image as two “ingredients” and uses a mathematical idea (Taylor series) to mix them in layers, so that both simple and more subtle connections between words and visuals are captured in a clear, step-by-step way . Because this paper approach simplifies how many parameters are needed, it stays efficient even as it looks for deeper patterns. In tests on three standard datasets (covering fake news and sarcastic posts), this model consistently beats existing methods, all while offering better insight into what it’s learning and why.

Featured Image

Photo by Hartono Creative Studio on Unsplash

Photo by Hartono Creative Studio on Unsplash

Why is it important?

This work introduces a novel, mathematically grounded way to fuse text and image features—treating them as terms in a layered Taylor-series expansion—rather than the typical “black-box” concatenation or attention schemes. This approach is both parameter-efficient and inherently interpretable, allowing practitioners to pinpoint whether linear or higher-order interactions drove a particular decision . It is especially timely because the explosion of AI-generated deepfakes and increasingly strict regulations (e.g., UK’s Online Safety Act) demand detection tools that are not only accurate at spotting multimodal deception but also transparent and lightweight enough for real-world, at-scale deployment. Together, these innovations mean the model can be more readily understood, trusted, and adopted by journalists, platform moderators, and policy-makers—broadening its impact and increasing readership by offering clear, explainable insights into why a post is flagged.

Perspectives