mlsanityData to 7 Oct 2026

DISCO: Comprehensive and Explainable Disinformation Detection

arXiv 2203.04928 ↗First posted 9 Mar 2022cs.LG cs.IRcited by 9

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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.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 19 of its 37 references that are in the map. First posted on arXiv 9 Mar 2022.

Builds on

19 of 37 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2013Distributed Representations of Words and Phrases and their Compositionalitycited by 3,462
  3. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  4. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 786
  5. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 334
  6. 2020Scaling Graph Neural Networks with Approximate PageRankcited by 174
  7. 2021HDMI: High-order Deep Multiplex Infomaxcited by 82
  8. 2021Network of Tensor Time Seriescited by 32
  9. 2020BRENDA: Browser Extension for Fake News Detectioncited by 20
  10. 2021Multi-facet Contextual Bandits: A Neural Network Perspectivecited by 20
  11. 2021DPPIN: A Biological Repository of Dynamic Protein-Protein Interaction Network Datacited by 17
  12. 2019Automated Fact Checking in the News Roomcited by 13
7 more
  1. 2021From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systemscited by 9
  2. 2021Deeper-GXX: Deepening Arbitrary GNNscited by 7
  3. 2017Fast and Accurate Random Walk with Restart on Dynamic Graphs with Guaranteescited by 6
  4. 2017TPA: Fast, Scalable, and Accurate Method for Approximate Random Walk with Restart on Billion Scale Graphscited by 4
  5. 2018Domain Adapted Word Embeddings for Improved Sentiment Classificationcited by 4
  6. 2021FaxPlainAC: A Fact-Checking Tool Based on EXPLAINable Models with HumAn Correction in the Loopcited by 4
  7. 2019Shallow Domain Adaptive Embeddings for Sentiment Analysiscited by 3

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 4; 2025: 1; 2026: 2.

9 papers.

  1. 2024VCR-Graphormer: A Mini-batch Graph Transformer via Virtual Connectionscited by 34
  2. 2024PageRank Bandits for Link Predictioncited by 14
  3. 2024Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detectioncited by 4
  4. 2023Fake News Detection Through Graph-based Neural Networks: A Surveycited by 3
  5. 2022Privacy-preserving Graph Analytics: Secure Generation and Federated Learningcited by 1
  6. 2026OWLEYE: Zero-Shot Learner for Cross-Domain Graph Data Anomaly Detectioncited by 1
  7. 2026Efficient Edge Rewiring Strategies for Enhancing PageRank Fairnesscited by 1
  8. 2024Experiments with truth using Machine Learning: Spectral analysis and explainable classification of synthetic, false, and genuine informationcited by 0
  9. 2025A Social Data-Driven System for Identifying Estate-related Events and Topicscited by 0