FNR: A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media

Abstract

<div>FNR (Fake News Revealer): A Similarity and Transformer-Based Approach to Detect Multi-Modal Fake News in Social Media.</div>

How Risky is Multimodal Fake News Detection? A Review of Cross-Modal Learning Approaches under EU AI Act Constrains

“…Thus, in most cases, fake news detection (FND) is a multi-modal problem. Recently, many existing approaches primarily focus on integrating unimodal features to produce multimodal news representations [20,22,31]. However, the effective aggregation of features from different modalities to enhance the overall performance is still an open question.…”

“…Year Method COOLANT [16] 2023 Cross-modal contrastive learning MMFN [4] 2023 Multi-grained information fusion MPFN [17] 2023 Multimodal progressive fusion SAMPLE [18] 2023 Similarity-aware multimodal prompt learning TieFake [19] 2023 Integration of multimodal context and author sentiment: focusing on title-text similarity and emotion awareness FNR [20] 2023 Similarity and transformer-based learning DGM [21] 2023 Transformer based on manipulation-aware contrastive learning and modality-aware cross-attention CAFE [22] 2022 Cross-modal ambiguity learning CMC [23] 2022 Cross-modal knowledge distillation FND-CLIP [24] 2022 Contrastive language-image pretraining-guided learning LIIMR [25] 2022 Leveraging intra and inter modality relationship FMFN [26] 2022 Fine-grained multimodal fusion network MCAN [27] 2021 Multimodal co-attention networks AMFB [28] 2021 Attention-based multimodal factorized bilinear pooling HMCAN [29] 2021 Hierarchical multi-modal contextual attention network CARMN [30] 2021 Crossmodal attention residual and multichannel CNN SAFE [31] 2020 Cross-modal similarity measurement stages or layers of a neural network. The main idea is to enable the model to progressively integrate information from different modalities, instead of attempting to do so in a single step.…”