# Event Detection in Twitter Stream using Weighted Dynamic Heartbeat Graph Approach

arXiv: https://arxiv.org/abs/1902.08522 · HTML: https://mlsanity.com/paper/1902.08522/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Feb 2019.

## Built on by (most cited first, 4 of 4)

- [Event Detection from Social Media Stream: Methods, Datasets and Opportunities](https://mlsanity.com/paper/2306.16495/) (2023)
- [A Human Word Association based model for topic detection in social networks](https://mlsanity.com/paper/2301.13066/) (2023)
- [Persian topic detection based on Human Word association and graph embedding](https://mlsanity.com/paper/2302.09775/) (2023)
- [Understanding Information Spreading Mechanisms During COVID-19 Pandemic by Analyzing the Impact of Tweet Text and User Features for Retweet Prediction](https://mlsanity.com/paper/2106.07344/) (2021)

## Abstract (arXiv)

Tweets about everyday events are published on Twitter. Detecting such events is a challenging task due to the diverse and noisy contents of Twitter. In this paper, we propose a novel approach named Weighted Dynamic Heartbeat Graph (WDHG) to detect events from the Twitter stream. Once an event is detected in a Twitter stream, WDHG suppresses it in later stages, in order to detect new emerging events. This unique characteristic makes the proposed approach sensitive to capture emerging events efficiently. Experiments are performed on three real-life benchmark datasets: FA Cup Final 2012, Super Tuesday 2012, and the US Elections 2012. Results show considerable improvement over existing event detection methods in most cases.