# Knowledge Graph informed Fake News Classification via Heterogeneous Representation Ensembles

arXiv: https://arxiv.org/abs/2110.10457 · HTML: https://mlsanity.com/paper/2110.10457/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 45 references that are in the map.
- First posted on arXiv 20 Oct 2021.

## Builds on (most cited first, 23 of 23)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Cross-lingual Language Model Pretraining](https://mlsanity.com/paper/1901.07291/) (2019)
- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [SimplE Embedding for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1802.04868/) (2018)
- [Quaternion Knowledge Graph Embeddings](https://mlsanity.com/paper/1904.10281/) (2019)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter](https://mlsanity.com/paper/2005.07503/) (2020)
- [Fighting an Infodemic: COVID-19 Fake News Dataset](https://mlsanity.com/paper/2011.03327/) (2020)
- [A Stylometric Inquiry into Hyperpartisan and Fake News](https://mlsanity.com/paper/1702.05638/) (2017)
- [The Limitations of Stylometry for Detecting Machine-Generated Fake News](https://mlsanity.com/paper/1908.09805/) (2019)
- [GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding](https://mlsanity.com/paper/1903.00757/) (2019)
- [Enriching BERT with Knowledge Graph Embeddings for Document Classification](https://mlsanity.com/paper/1909.08402/) (2019)
- [Mining Disinformation and Fake News: Concepts, Methods, and Recent Advancements](https://mlsanity.com/paper/2001.00623/) (2020)
- [g2tmn at Constraint@AAAI2021: Exploiting CT-BERT and Ensembling Learning for COVID-19 Fake News Detection](https://mlsanity.com/paper/2012.11967/) (2020)
- [Semantic Reasoning from Model-Agnostic Explanations](https://mlsanity.com/paper/2106.15433/) (2021)

## Built on by (most cited first, 14 of 14)

- [MetaAdapt: Domain Adaptive Few-Shot Misinformation Detection via Meta Learning](https://mlsanity.com/paper/2305.12692/) (2023)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking](https://mlsanity.com/paper/2505.07891/) (2025)
- [AutoML-guided Fusion of Entity and LLM-based Representations for Document Classification](https://mlsanity.com/paper/2408.09794/) (2024)
- [From Symbolic to Neural and Back: Exploring Knowledge Graph-Large Language Model Synergies](https://mlsanity.com/paper/2506.09566/) (2025)
- [Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination](https://mlsanity.com/paper/2310.19503/) (2023)
- [From Fake to Hyperpartisan News Detection Using Domain Adaptation](https://mlsanity.com/paper/2308.02185/) (2023)
- [Latent Graphs for Semi-Supervised Learning on Biomedical Tabular Data](https://mlsanity.com/paper/2309.15757/) (2023)
- [A topic-aware graph neural network model for knowledge base updating](https://mlsanity.com/paper/2208.14601/) (2022)
- [Classification of news spreading barriers](https://mlsanity.com/paper/2304.08167/) (2023)
- [AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System](https://mlsanity.com/paper/2310.19834/) (2023)
- [Knowledge Bases in Support of Large Language Models for Processing Web News](https://mlsanity.com/paper/2411.08278/) (2024)
- [SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics](https://mlsanity.com/paper/2504.08776/) (2025)
- [FuDoBa: Fusing Document and Knowledge Graph-based Representations with Bayesian Optimisation](https://mlsanity.com/paper/2507.06622/) (2025)

## Abstract (arXiv)

Increasing amounts of freely available data both in textual and relational form offers exploration of richer document representations, potentially improving the model performance and robustness. An emerging problem in the modern era is fake news detection -- many easily available pieces of information are not necessarily factually correct, and can lead to wrong conclusions or are used for manipulation. In this work we explore how different document representations, ranging from simple symbolic bag-of-words, to contextual, neural language model-based ones can be used for efficient fake news identification. One of the key contributions is a set of novel document representation learning methods based solely on knowledge graphs, i.e. extensive collections of (grounded) subject-predicate-object triplets. We demonstrate that knowledge graph-based representations already achieve competitive performance to conventionally accepted representation learners. Furthermore, when combined with existing, contextual representations, knowledge graph-based document representations can achieve state-of-the-art performance. To our knowledge this is the first larger-scale evaluation of how knowledge graph-based representations can be systematically incorporated into the process of fake news classification.