mlsanityData to 14 Oct 2026

Detecting Rumours with Latency Guarantees using Massive Streaming Data

arXiv 2205.06580 ↗First posted 13 May 2022cs.SI cs.LGcited by 12

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Abstract (arXiv)

Today's social networks continuously generate massive streams of data, which provide a valuable starting point for the detection of rumours as soon as they start to propagate. However, rumour detection faces tight latency bounds, which cannot be met by contemporary algorithms, given the sheer volume of high-velocity streaming data emitted by social networks. Hence, in this paper, we argue for best-effort rumour detection that detects most rumours quickly rather than all rumours with a high delay. To this end, we combine techniques for efficient, graph-based matching of rumour patterns with effective load shedding that discards some of the input data while minimising the loss in accuracy. Experiments with large-scale real-world datasets illustrate the robustness of our approach in terms of runtime performance and detection accuracy under diverse streaming conditions.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 17 of its 77 references that are in the map. First posted on arXiv 13 May 2022.

Builds on

17 of 77 references.

References · 17

  1. 2017Fake News Detection on Social Media: A Data Mining Perspectivecited by 336
  2. 2020Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networkscited by 153
  3. 2015A Survey of Heterogeneous Information Network Analysiscited by 110
  4. 2017Detection and Resolution of Rumours in Social Media: A Surveycited by 104
  5. 2017Fake News Mitigation via Point Process Based Interventioncited by 24
  6. 2018Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergencescited by 21
  7. 2019A Survey of Community Search Over Big Graphscited by 21
  8. 2021Prototype Learning for Interpretable Respiratory Sound Analysiscited by 21
  9. 2016A Survey on Social Media Anomaly Detectioncited by 17
  10. 2013One-Class Support Measure Machines for Group Anomaly Detectioncited by 12
  11. 2014GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstractcited by 11
  12. 2018Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labelscited by 11
5 more
  1. 2020eSPICE: Probabilistic Load Shedding from Input Event Streams in Complex Event Processingcited by 3
  2. 2013On Load Shedding in Complex Event Processingcited by 2
  3. 2017A Survey of State Management in Big Data Processing Systemscited by 2
  4. 2016Incremental Maintenance of Maximal Cliques in a Dynamic Graphcited by 1
  5. 2016Sub-Story Detection in Twitter with Hierarchical Dirichlet Processescited by 1

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 1; 2024: 2; 2025: 5; 2026: 3.

12 papers.

  1. 2025VLM as Policy: Common-Law Content Moderation Framework for Short Video Platformcited by 21
  2. 2022Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Predictioncited by 18
  3. 2024Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resiliencecited by 8
  4. 2023A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Eracited by 7
  5. 2026Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platformscited by 2
  6. 2024Heterogeneous Hypergraph Embedding for Recommendation Systemscited by 1
  7. 2025Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusioncited by 1
  8. 2025Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learningcited by 1
  9. 2025Hypergraph Diffusion for High-Order Recommender Systemscited by 0
  10. 2025Model-Free Counterfactual Subset Selection at Scalecited by 0
  11. 2026Empowering Contrastive Federated Sequential Recommendation with LLMscited by 0
  12. 2026FINER-SQL: Boosting Small Language Models for Text-to-SQLcited by 0