# Real-time Event Detection on Social Data Streams

arXiv: https://arxiv.org/abs/1907.11229 · HTML: https://mlsanity.com/paper/1907.11229/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2019.

## Built on by (most cited first, 21 of 21)

- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection](https://mlsanity.com/paper/2312.11891/) (2023)
- [Uncertainty-guided Boundary Learning for Imbalanced Social Event Detection](https://mlsanity.com/paper/2310.19247/) (2023)
- [Transferring Knowledge Distillation for Multilingual Social Event Detection](https://mlsanity.com/paper/2108.03084/) (2021)
- [Relational Prompt-based Pre-trained Language Models for Social Event Detection](https://mlsanity.com/paper/2404.08263/) (2024)
- [Heterogeneous Social Event Detection via Hyperbolic Graph Representations](https://mlsanity.com/paper/2302.10362/) (2023)
- [Event Detection from Social Media Stream: Methods, Datasets and Opportunities](https://mlsanity.com/paper/2306.16495/) (2023)
- [Cross-Modal Augmentation for Few-Shot Multimodal Fake News Detection](https://mlsanity.com/paper/2407.12880/) (2024)
- [Graph-of-Tweets: A Graph Merging Approach to Sub-event Identification](https://mlsanity.com/paper/2101.03208/) (2021)
- [Early Discovery of Emerging Entities in Persian Twitter with Semantic Similarity](https://mlsanity.com/paper/2207.02434/) (2022)
- [A Semantic Modular Framework for Events Topic Modeling in Social Media](https://mlsanity.com/paper/2301.09009/) (2023)
- [HumVI: A Multilingual Dataset for Detecting Violent Incidents Impacting Humanitarian Aid](https://mlsanity.com/paper/2410.06370/) (2024)
- [EventHunter: Dynamic Clustering and Ranking of Security Events from Hacker Forum Discussions](https://mlsanity.com/paper/2507.09762/) (2025)
- [Tracing Topic Transitions with Temporal Graph Clusters](https://mlsanity.com/paper/2104.07836/) (2021)
- [Modeling Fuzzy Cluster Transitions for Topic Tracing](https://mlsanity.com/paper/2104.08258/) (2021)
- [EnrichEvent: Enriching Social Data with Contextual Information for Emerging Event Extraction](https://mlsanity.com/paper/2307.16082/) (2023)
- [LEARN: An Invex Loss for Outlier Oblivious Robust Online Optimization](https://mlsanity.com/paper/2408.06297/) (2024)
- [Towards Effective, Efficient and Unsupervised Social Event Detection in the Hyperbolic Space](https://mlsanity.com/paper/2412.10712/) (2024)
- [Multi-Order Hyperbolic Graph Convolution and Aggregated Attention for Social Event Detection](https://mlsanity.com/paper/2502.00351/) (2025)
- [TingIS: Real-time Risk Event Discovery from Noisy Customer Incidents at Enterprise Scale](https://mlsanity.com/paper/2604.21889/) (2026)
- [CLUE: A Contrastive Learning Framework for Unsupervised Event Type Induction and Annotation](https://mlsanity.com/paper/2610.12580/) (2026)

## Abstract (arXiv)

Social networks are quickly becoming the primary medium for discussing what is happening around real-world events. The information that is generated on social platforms like Twitter can produce rich data streams for immediate insights into ongoing matters and the conversations around them. To tackle the problem of event detection, we model events as a list of clusters of trending entities over time. We describe a real-time system for discovering events that is modular in design and novel in scale and speed: it applies clustering on a large stream with millions of entities per minute and produces a dynamically updated set of events. In order to assess clustering methodologies, we build an evaluation dataset derived from a snapshot of the full Twitter Firehose and propose novel metrics for measuring clustering quality. Through experiments and system profiling, we highlight key results from the offline and online pipelines. Finally, we visualize a high profile event on Twitter to show the importance of modeling the evolution of events, especially those detected from social data streams.