# DEAP-FAKED: Knowledge Graph based Approach for Fake News Detection

arXiv: https://arxiv.org/abs/2107.10648 · HTML: https://mlsanity.com/paper/2107.10648/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 52 references that are in the map.
- First posted on arXiv 4 Jul 2021.

## Builds on (most cited first, 20 of 20)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- ["Liar, Liar Pants on Fire": A New Benchmark Dataset for Fake News Detection](https://mlsanity.com/paper/1705.00648/) (2017)
- [KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation](https://mlsanity.com/paper/1911.06136/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [GCAN: Graph-aware Co-Attention Networks for Explainable Fake News Detection on Social Media](https://mlsanity.com/paper/2004.11648/) (2020)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Beyond News Contents: The Role of Social Context for Fake News Detection](https://mlsanity.com/paper/1712.07709/) (2017)
- [CoAID: COVID-19 Healthcare Misinformation Dataset](https://mlsanity.com/paper/2006.00885/) (2020)
- [Natural Language Processing Advancements By Deep Learning: A Survey](https://mlsanity.com/paper/2003.01200/) (2020)
- [The Role of User Profile for Fake News Detection](https://mlsanity.com/paper/1904.13355/) (2019)
- [Weak Supervision for Fake News Detection via Reinforcement Learning](https://mlsanity.com/paper/1912.12520/) (2019)
- [Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository](https://mlsanity.com/paper/2002.00837/) (2020)
- [Adversarial Active Learning based Heterogeneous Graph Neural Network for Fake News Detection](https://mlsanity.com/paper/2101.11206/) (2021)
- [Studying Fake News via Network Analysis: Detection and Mitigation](https://mlsanity.com/paper/1804.10233/) (2018)
- [Identifying Possible Rumor Spreaders on Twitter: A Weak Supervised Learning Approach](https://mlsanity.com/paper/2010.07647/) (2020)
- [Misinformation Detection on YouTube Using Video Captions](https://mlsanity.com/paper/2107.00941/) (2021)
- [Fake News Detection on News-Oriented Heterogeneous Information Networks through Hierarchical Graph Attention](https://mlsanity.com/paper/2002.04397/) (2020)
- [Building Knowledge Graphs About Political Agents in the Age of Misinformation](https://mlsanity.com/paper/1901.11408/) (2019)
- [Intrinsic analysis for dual word embedding space models](https://mlsanity.com/paper/2012.00728/) (2020)

## Built on by (most cited first, 13 of 13)

- [Knowledge Graphs: Opportunities and Challenges](https://mlsanity.com/paper/2303.13948/) (2023)
- [A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises](https://mlsanity.com/paper/2306.04802/) (2023)
- [GAME-ON: Graph Attention Network based Multimodal Fusion for Fake News Detection](https://mlsanity.com/paper/2202.12478/) (2022)
- [Combating Health Misinformation in Social Media: Characterization, Detection, Intervention, and Open Issues](https://mlsanity.com/paper/2211.05289/) (2022)
- [Misleading the Covid-19 vaccination discourse on Twitter: An exploratory study of infodemic around the pandemic](https://mlsanity.com/paper/2108.10735/) (2021)
- [Misinformation Detection in Social Media Video Posts](https://mlsanity.com/paper/2202.07706/) (2022)
- [MCWDST: a Minimum-Cost Weighted Directed Spanning Tree Algorithm for Real-Time Fake News Mitigation in Social Media](https://mlsanity.com/paper/2302.12190/) (2023)
- [TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking](https://mlsanity.com/paper/2505.07891/) (2025)
- [Reinforcement Learning-based Knowledge Graph Reasoning for Explainable Fact-checking](https://mlsanity.com/paper/2310.07613/) (2023)
- [Misinformation Concierge: A Proof-of-Concept with Curated Twitter Dataset on COVID-19 Vaccination](https://mlsanity.com/paper/2309.00639/) (2023)
- [Trust, Accountability, and Autonomy in Knowledge Graph-based AI for Self-determination](https://mlsanity.com/paper/2310.19503/) (2023)
- [AMIR: Automated MisInformation Rebuttal -- A COVID-19 Vaccination Datasets based Recommendation System](https://mlsanity.com/paper/2310.19834/) (2023)
- [Language of Persuasion and Misrepresentation in Business Communication: A Textual Detection Approach](https://mlsanity.com/paper/2508.09935/) (2025)

## Abstract (arXiv)

Fake News on social media platforms has attracted a lot of attention in recent times, primarily for events related to politics (2016 US Presidential elections), healthcare (infodemic during COVID-19), to name a few. Various methods have been proposed for detecting Fake News. The approaches span from exploiting techniques related to network analysis, Natural Language Processing (NLP), and the usage of Graph Neural Networks (GNNs). In this work, we propose DEAP-FAKED, a knowleDgE grAPh FAKe nEws Detection framework for identifying Fake News. Our approach is a combination of the NLP -- where we encode the news content, and the GNN technique -- where we encode the Knowledge Graph (KG). A variety of these encodings provides a complementary advantage to our detector. We evaluate our framework using two publicly available datasets containing articles from domains such as politics, business, technology, and healthcare. As part of dataset pre-processing, we also remove the bias, such as the source of the articles, which could impact the performance of the models. DEAP-FAKED obtains an F1-score of 88% and 78% for the two datasets, which is an improvement of 21%, and 3% respectively, which shows the effectiveness of the approach.