Corpus ID: 237940774

Multi-modal Fusion using Fine-tuned Self-attention and Transfer Learning for Veracity Analysis of Web Information

@article{Meel2021MultimodalFU,
  title={Multi-modal Fusion using Fine-tuned Self-attention and Transfer Learning for Veracity Analysis of Web Information},
  author={Priyanka Meel and Dinesh Kumar Vishwakarma},
  journal={ArXiv},
  year={2021},
  volume={abs/2109.12547}
}
The nuisance of misinformation and fake news has escalated many folds since the advent of online social networks. Human consciousness and decision-making capabilities are negatively influenced by manipulated, fabricated, biased or unverified news posts. Therefore, there is a high demand for designing veracity analysis systems to detect fake information contents in multiple data modalities. In an attempt to find a sophisticated solution to this critical issue, we proposed an architecture to… Expand

References

SHOWING 1-10 OF 48 REFERENCES
A temporal ensembling based semi-supervised ConvNet for the detection of fake news articles
TLDR
An innovative Convolutional Neural Network semi-supervised framework built on the self-ensembling concept to take leverage of the linguistic and stylometric information of annotated news articles, at the same time explore the hidden patterns in unlabelled data as well. Expand
Multimodal Fusion with Recurrent Neural Networks for Rumor Detection on Microblogs
TLDR
A novel Recurrent Neural Network with an attention mechanism (att-RNN) to fuse multimodal features for effective rumor detection and the results demonstrate the effectiveness of the proposed end-to-end att- RNN in detecting rumors with multi-modal contents. Expand
EANN: Event Adversarial Neural Networks for Multi-Modal Fake News Detection
TLDR
An end-to-end framework named Event Adversarial Neural Network (EANN), which can derive event-invariant features and thus benefit the detection of fake news on newly arrived events, is proposed. Expand
A unified approach for detection of Clickbait videos on YouTube using cognitive evidences
TLDR
A Clickbait Video Detector (CVD) scheme that leverages to learn three sets of latent features based on User Profiling, Video-Content, and Human Consensus to retrieve cognitive evidence for the detection of clickbait videos on YouTube. Expand
Multimodal Fake News Detection with Textual, Visual and Semantic Information
TLDR
A multimodal system based on a neural network that combines textual, visual and semantic information with the aim to differentiate between fake and real posts is proposed. Expand
Attention is All you Need
TLDR
A new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely is proposed, which generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data. Expand
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been shown to achieveExpand
TI-CNN: Convolutional Neural Networks for Fake News Detection
TLDR
A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed, which is proposed to study the fake news detection problem and is trained with both the text and image information simultaneously. Expand
Detection of GAN-Generated Fake Images over Social Networks
The diffusion of fake images and videos on social networks is a fast growing problem. Commercial media editing tools allow anyone to remove, add, or clone people and objects, to generate fake images.Expand
Multimodal fake news detection using a Cultural Algorithm with situational and normative knowledge
TLDR
This paper presents a novel approach using a Cultural Algorithm with situational and normative knowledge to detect fake news using both text and images that outperforms the state-of-the-art methods for identifying fake news in terms of accuracy. Expand
...
1
2
3
4
5
...