# DISCO: Comprehensive and Explainable Disinformation Detection

arXiv: https://arxiv.org/abs/2203.04928 · HTML: https://mlsanity.com/paper/2203.04928/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 37 references that are in the map.
- First posted on arXiv 9 Mar 2022.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Predict then Propagate: Graph Neural Networks meet Personalized PageRank](https://mlsanity.com/paper/1810.05997/) (2018)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Scaling Graph Neural Networks with Approximate PageRank](https://mlsanity.com/paper/2007.01570/) (2020)
- [HDMI: High-order Deep Multiplex Infomax](https://mlsanity.com/paper/2102.07810/) (2021)
- [Network of Tensor Time Series](https://mlsanity.com/paper/2102.07736/) (2021)
- [BRENDA: Browser Extension for Fake News Detection](https://mlsanity.com/paper/2005.13270/) (2020)
- [Multi-facet Contextual Bandits: A Neural Network Perspective](https://mlsanity.com/paper/2106.03039/) (2021)
- [DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Data](https://mlsanity.com/paper/2107.02168/) (2021)
- [Automated Fact Checking in the News Room](https://mlsanity.com/paper/1904.02037/) (2019)
- [From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems](https://mlsanity.com/paper/2110.14844/) (2021)
- [Deeper-GXX: Deepening Arbitrary GNNs](https://mlsanity.com/paper/2110.13798/) (2021)
- [Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guarantees](https://mlsanity.com/paper/1712.00595/) (2017)
- [TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphs](https://mlsanity.com/paper/1708.02574/) (2017)
- [Domain Adapted Word Embeddings for Improved Sentiment Classification](https://mlsanity.com/paper/1805.04576/) (2018)
- [FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loop](https://mlsanity.com/paper/2110.10144/) (2021)
- [Shallow Domain Adaptive Embeddings for Sentiment Analysis](https://mlsanity.com/paper/1908.06082/) (2019)

## Built on by (most cited first, 9 of 9)

- [VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connections](https://mlsanity.com/paper/2403.16030/) (2024)
- [PageRank Bandits for Link Prediction](https://mlsanity.com/paper/2411.01410/) (2024)
- [Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection](https://mlsanity.com/paper/2412.05672/) (2024)
- [Fake News Detection Through Graph-based Neural Networks: A Survey](https://mlsanity.com/paper/2307.12639/) (2023)
- [OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detection](https://mlsanity.com/paper/2601.19102/) (2026)
- [Privacy-preserving Graph Analytics: Secure Generation and Federated Learning](https://mlsanity.com/paper/2207.00048/) (2022)
- [Efficient Edge Rewiring Strategies for Enhancing PageRank Fairness](https://mlsanity.com/paper/2602.02512/) (2026)
- [Experiments with truth using Machine Learning: Spectral analysis and explainable classification of synthetic, false, and genuine information](https://mlsanity.com/paper/2407.05464/) (2024)
- [A Social Data-Driven System for Identifying Estate-related Events and Topics](https://mlsanity.com/paper/2508.03711/) (2025)

## Abstract (arXiv)

Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets. In this paper, we identify prevalent challenges and advances related to automated disinformation detection from multiple aspects and propose a comprehensive and explainable disinformation detection framework called DISCO. It leverages the heterogeneity of disinformation and addresses the opaqueness of prediction. Then we provide a demonstration of DISCO on a real-world fake news detection task with satisfactory detection accuracy and explanation. The demo video and source code of DISCO is now publicly available https://github.com/DongqiFu/DISCO. We expect that our demo could pave the way for addressing the limitations of identification, comprehension, and explainability as a whole.