F2IND-IT! -- Multimodal Fuzzy Fake Indian News Detection using Images and Text
It addresses the problem of fake news detection in the linguistically and culturally diverse Indian media landscape, but the approach is incremental, combining existing components.
The paper proposes a multimodal fake news detection framework for Indian media, combining ResNet-50 for images, DistilBERT for text, and ANFIS with attention fusion. It achieves superior accuracy, precision, recall, and F1-score on the IFND dataset compared to prior work.
Biased manipulation of facts across regional and national media outlets complicates misinformation detection in diverse landscapes like India. This paper introduces a novel multimodal framework combining visual and textual modalities for enhanced fake news detection on Indian media. The architecture utilizes a ResNet-50 Convolutional Neural Network to extract visual features from news images, a DistilBERT encoder to obtain textual semantic embeddings, and an Adaptive Neuro-Fuzzy Inference System (ANFIS) to generate a fuzzy reliability score. A lightweight attention-based fusion module assigns learnable weights to each modality prior to classification. Evaluated on the IFND dataset, the proposed model is validated through an in-depth comparative analysis against previous research. Experimental results demonstrate superior performance across accuracy, precision, recall, and $F_1$-scores, confirming the efficacy of the architecture.