# TI-CNN: Convolutional Neural Networks for Fake News Detection

arXiv: https://arxiv.org/abs/1806.00749 · HTML: https://mlsanity.com/paper/1806.00749/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2018.

## Built on by (most cited first, 22 of 22)

- [A Survey on Multimodal Disinformation Detection](https://mlsanity.com/paper/2103.12541/) (2021)
- [Combat COVID-19 Infodemic Using Explainable Natural Language Processing Models](https://mlsanity.com/paper/2103.00747/) (2021)
- [GAMED: Knowledge Adaptive Multi-Experts Decoupling for Multimodal Fake News Detection](https://mlsanity.com/paper/2412.12164/) (2024)
- [Multimodal Fusion with BERT and Attention Mechanism for Fake News Detection](https://mlsanity.com/paper/2104.11476/) (2021)
- [Fake News Detection: Experiments and Approaches beyond Linguistic Features](https://mlsanity.com/paper/2109.12914/) (2021)
- [TIB's Visual Analytics Group at MediaEval '20: Detecting Fake News on Corona Virus and 5G Conspiracy](https://mlsanity.com/paper/2101.03529/) (2021)
- [An ensemble deep learning technique for detecting suicidal ideation from posts in social media platforms](https://mlsanity.com/paper/2112.10609/) (2021)
- [An Adversarial Benchmark for Fake News Detection Models](https://mlsanity.com/paper/2201.00912/) (2022)
- [HKD4VLM: A Progressive Hybrid Knowledge Distillation Framework for Robust Multimodal Hallucination and Factuality Detection in VLMs](https://mlsanity.com/paper/2506.13038/) (2025)
- [Detecting the Role of an Entity in Harmful Memes: Techniques and Their Limitations](https://mlsanity.com/paper/2205.04402/) (2022)
- [SEMI-FND: Stacked Ensemble Based Multimodal Inference For Faster Fake News Detection](https://mlsanity.com/paper/2205.08159/) (2022)
- [Claim Verification using a Multi-GAN based Model](https://mlsanity.com/paper/2103.08001/) (2021)
- [FR-Detect: A Multi-Modal Framework for Early Fake News Detection on Social Media Using Publishers Features](https://mlsanity.com/paper/2109.04835/) (2021)
- [Science Checker: Extractive-Boolean Question Answering For Scientific Fact Checking](https://mlsanity.com/paper/2204.12263/) (2022)
- [RELIANCE: Reliable Ensemble Learning for Information and News Credibility Evaluation](https://mlsanity.com/paper/2401.10940/) (2024)
- [HAT4RD: Hierarchical Adversarial Training for Rumor Detection on Social Media](https://mlsanity.com/paper/2110.00425/) (2021)
- [A Review of Web Infodemic Analysis and Detection Trends across Multi-modalities using Deep Neural Networks](https://mlsanity.com/paper/2112.00803/) (2021)
- [An Emotion-Aware Multi-Task Approach to Fake News and Rumour Detection using Transfer Learning](https://mlsanity.com/paper/2211.12374/) (2022)
- [Dac-Fake: A Divide and Conquer Framework for Detecting Fake News on Social Media](https://mlsanity.com/paper/2508.16223/) (2025)
- [When retrieval outperforms generation: Dense evidence retrieval for scalable fake news detection](https://mlsanity.com/paper/2511.04643/) (2025)
- [Toward Effective Multi-Domain Rumor Detection in Social Networks Using Domain-Gated Mixture-of-Experts](https://mlsanity.com/paper/2602.21214/) (2026)
- [RAEGNet: Relation-Aware Evidence Graph Network for Harm-Aware Multimodal Fake News Detection](https://mlsanity.com/paper/2609.36902/) (2026)

## Abstract (arXiv)

With the development of social networks, fake news for various commercial and political purposes has been appearing in large numbers and gotten widespread in the online world. With deceptive words, people can get infected by the fake news very easily and will share them without any fact-checking. For instance, during the 2016 US president election, various kinds of fake news about the candidates widely spread through both official news media and the online social networks. These fake news is usually released to either smear the opponents or support the candidate on their side. The erroneous information in the fake news is usually written to motivate the voters' irrational emotion and enthusiasm. Such kinds of fake news sometimes can bring about devastating effects, and an important goal in improving the credibility of online social networks is to identify the fake news timely. In this paper, we propose to study the fake news detection problem. Automatic fake news identification is extremely hard, since pure model based fact-checking for news is still an open problem, and few existing models can be applied to solve the problem. With a thorough investigation of a fake news data, lots of useful explicit features are identified from both the text words and images used in the fake news. Besides the explicit features, there also exist some hidden patterns in the words and images used in fake news, which can be captured with a set of latent features extracted via the multiple convolutional layers in our model. A model named as TI-CNN (Text and Image information based Convolutinal Neural Network) is proposed in this paper. By projecting the explicit and latent features into a unified feature space, TI-CNN is trained with both the text and image information simultaneously. Extensive experiments carried on the real-world fake news datasets have demonstrate the effectiveness of TI-CNN.