# Detecting Rumours with Latency Guarantees using Massive Streaming Data

arXiv: https://arxiv.org/abs/2205.06580 · HTML: https://mlsanity.com/paper/2205.06580/

## 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 (most cited first, 17 of 17)

- [Fake News Detection on Social Media: A Data Mining Perspective](https://mlsanity.com/paper/1708.01967/) (2017)
- [Rumor Detection on Social Media with Bi-Directional Graph Convolutional Networks](https://mlsanity.com/paper/2001.06362/) (2020)
- [A Survey of Heterogeneous Information Network Analysis](https://mlsanity.com/paper/1511.04854/) (2015)
- [Detection and Resolution of Rumours in Social Media: A Survey](https://mlsanity.com/paper/1704.00656/) (2017)
- [Fake News Mitigation via Point Process Based Intervention](https://mlsanity.com/paper/1703.07823/) (2017)
- [Doubly Robust Bayesian Inference for Non-Stationary Streaming Data with β-Divergences](https://mlsanity.com/paper/1806.02261/) (2018)
- [A Survey of Community Search Over Big Graphs](https://mlsanity.com/paper/1904.12539/) (2019)
- [Prototype Learning for Interpretable Respiratory Sound Analysis](https://mlsanity.com/paper/2110.03536/) (2021)
- [A Survey on Social Media Anomaly Detection](https://mlsanity.com/paper/1601.01102/) (2016)
- [One-Class Support Measure Machines for Group Anomaly Detection](https://mlsanity.com/paper/1303.0309/) (2013)
- [GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract](https://mlsanity.com/paper/1410.1940/) (2014)
- [Request-and-Reverify: Hierarchical Hypothesis Testing for Concept Drift Detection with Expensive Labels](https://mlsanity.com/paper/1806.10131/) (2018)
- [eSPICE: Probabilistic Load Shedding from Input Event Streams in Complex Event Processing](https://mlsanity.com/paper/2002.05896/) (2020)
- [On Load Shedding in Complex Event Processing](https://mlsanity.com/paper/1312.4283/) (2013)
- [A Survey of State Management in Big Data Processing Systems](https://mlsanity.com/paper/1702.01596/) (2017)
- [Incremental Maintenance of Maximal Cliques in a Dynamic Graph](https://mlsanity.com/paper/1601.06311/) (2016)
- [Sub-Story Detection in Twitter with Hierarchical Dirichlet Processes](https://mlsanity.com/paper/1606.03561/) (2016)

## Built on by (most cited first, 12 of 12)

- [VLM as Policy: Common-Law Content Moderation Framework for Short Video Platform](https://mlsanity.com/paper/2504.14904/) (2025)
- [Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction](https://mlsanity.com/paper/2211.07400/) (2022)
- [Fast-FedUL: A Training-Free Federated Unlearning with Provable Skew Resilience](https://mlsanity.com/paper/2405.18040/) (2024)
- [A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era](https://mlsanity.com/paper/2301.09362/) (2023)
- [Live or Lie: Action-Aware Capsule Multiple Instance Learning for Risk Assessment in Live Streaming Platforms](https://mlsanity.com/paper/2602.03520/) (2026)
- [Heterogeneous Hypergraph Embedding for Recommendation Systems](https://mlsanity.com/paper/2407.03665/) (2024)
- [Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion](https://mlsanity.com/paper/2501.14399/) (2025)
- [Towards Real-World Rumor Detection: Anomaly Detection Framework with Graph Supervised Contrastive Learning](https://mlsanity.com/paper/2508.07205/) (2025)
- [Hypergraph Diffusion for High-Order Recommender Systems](https://mlsanity.com/paper/2501.16722/) (2025)
- [Model-Free Counterfactual Subset Selection at Scale](https://mlsanity.com/paper/2502.08326/) (2025)
- [Empowering Contrastive Federated Sequential Recommendation with LLMs](https://mlsanity.com/paper/2602.09306/) (2026)
- [FINER-SQL: Boosting Small Language Models for Text-to-SQL](https://mlsanity.com/paper/2605.03465/) (2026)

## 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.