# Event Detection from Social Media Stream: Methods, Datasets and Opportunities

arXiv: https://arxiv.org/abs/2306.16495 · HTML: https://mlsanity.com/paper/2306.16495/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 102 references that are in the map.
- First posted on arXiv 28 Jun 2023.

## Builds on (most cited first, 13 of 13)

- [Latent Dirichlet Allocation (LDA) and Topic modeling: models, applications, a survey](https://mlsanity.com/paper/1711.04305/) (2017)
- [Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs](https://mlsanity.com/paper/2101.08747/) (2021)
- [Real-time Event Detection on Social Data Streams](https://mlsanity.com/paper/1907.11229/) (2019)
- [Rumor Detection on Social Media: Datasets, Methods and Opportunities](https://mlsanity.com/paper/1911.07199/) (2019)
- [Event detection, tracking, and visualization in Twitter: a mention-anomaly-based approach](https://mlsanity.com/paper/1505.05657/) (2015)
- [Data Sets: Word Embeddings Learned from Tweets and General Data](https://mlsanity.com/paper/1708.03994/) (2017)
- [Embed2Detect: Temporally Clustered Embedded Words for Event Detection in Social Media](https://mlsanity.com/paper/2006.05908/) (2020)
- [Event Detection in Twitter Stream using Weighted Dynamic Heartbeat Graph Approach](https://mlsanity.com/paper/1902.08522/) (2019)
- [Event Detection with Neural Networks: A Rigorous Empirical Evaluation](https://mlsanity.com/paper/1808.08504/) (2018)
- [Multiscale Event Detection in Social Media](https://mlsanity.com/paper/1404.7048/) (2014)
- ["Breaking" Disasters: Predicting and Characterizing the Global News Value of Natural and Man-made Disasters](https://mlsanity.com/paper/1709.02510/) (2017)
- [Reuters Tracer: Toward Automated News Production Using Large Scale Social Media Data](https://mlsanity.com/paper/1711.04068/) (2017)
- [From Tweets to Events: Exploring a Scalable Solution for Twitter Streams](https://mlsanity.com/paper/1405.1392/) (2014)

## Built on by (most cited first, 3 of 3)

- [A Comparative Study on Enhancing Prediction in Social Network Advertisement through Data Augmentation](https://mlsanity.com/paper/2404.13812/) (2024)
- [Synthetic Non-stationary Data Streams for Recognition of the Unknown](https://mlsanity.com/paper/2505.13745/) (2025)
- [Large Language Model Enhanced Clustering for News Event Detection](https://mlsanity.com/paper/2406.10552/) (2024)

## Abstract (arXiv)

Social media streams contain large and diverse amount of information, ranging from daily-life stories to the latest global and local events and news. Twitter, especially, allows a fast spread of events happening real time, and enables individuals and organizations to stay informed of the events happening now. Event detection from social media data poses different challenges from traditional text and is a research area that has attracted much attention in recent years. In this paper, we survey a wide range of event detection methods for Twitter data stream, helping readers understand the recent development in this area. We present the datasets available to the public. Furthermore, a few research opportunities