SpotFake: A Multi-modal Framework for Fake News Detection

By Shivangi Singhal, Rajiv Ratn Shah, Tanmoy Chakraborty, Ponnurangam Kumaraguru, S. Satoh. IEEE International Conference on Multimedia Big Data, 2019

516 citations recorded by the source corpus.

A rapid growth in the amount of fake news on social media is a very serious concern in our society. It is usually created by manipulating images, text, audio, and videos. This indicates that there is a need of multimodal system for fake news detection. Though, there are multimodal fake news detection systems but they tend to solve the problem of fake news by considering an additional sub-task like event discriminator and finding correlations across the modalities. The results of fake news detection are heavily dependent on the subtask and in absence of subtask training, the performance of fake news detection degrade by 10% on an average. To solve this issue, we introduce SpotFake-a multi-modal framework for fake news detection. Our proposed solution detects fake news without taking into account any other subtasks. It exploits both the textual and visual features of an article. Specifically, we made use of language models (like BERT) to learn text features, and image features are learned from VGG-19 pre-trained on ImageNet dataset. All the experiments are performed on two publicly available datasets, i.e., Twitter and Weibo. The proposed model performs better than the current state-of-the-art on Twitter and Weibo datasets by 3.27% and 6.83%, respectively.

Subfields: Artificial Intelligence, Information Systems, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/1b0bf62c7baabdb41dcaf2a641befb9c1d54779d

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Peng Zhang
2. Changsheng Xu
3. Shengsheng Qian
4. Jinguang Wang
5. Jun Hu
6. Quan Fang

In Information Systems

1. Changsheng Xu
2. Shengsheng Qian
3. Jinguang Wang
4. Jun Hu
5. Quan Fang

In Sociology and Political Science

1. Changsheng Xu
2. Shengsheng Qian
3. Jinguang Wang
4. Jun Hu
5. Quan Fang

Citations in the corpus

• Cross-modal Ambiguity Learning for Multimodal Fake News Detection (2022, background): “While earlyworks on fake news detection focused on text-only content analysis, cross-modal content analysis can offer complementary benefits to assist with fake news detection [22, 25, 26].”
• Hierarchical Multi-modal Contextual Attention Network for Fake News Detection (2021, methodology): “• SpotFake [29]: SpotFake utilizes the pre-trained language models (like BERT) to learn the textual information, and employs VGG-19 (pre-trained on ImageNet [6] dataset) to obtain image features.”