An Incremental Clustering Baseline for Event Detection on Twitter

TL;DR

This paper introduces an incremental clustering approach utilizing sentence embeddings for event detection on Twitter, aiming to establish a computationally efficient baseline with improved performance.

Contribution

The study presents a novel incremental clustering method combined with recent sentence embeddings, providing a new baseline for Twitter event detection.

Findings

Significant performance improvements over previous methods

Effective in maintaining low computational complexity

Serves as a benchmark for future research

Abstract

Event detection in text streams is a crucial task for the analysis of online media and social networks. One of the current challenges in this field is establishing a performance standard while maintaining an acceptable level of computational complexity. In our study, we use an incremental clustering algorithm combined with recent advancements in sentence embeddings. Our objective is to compare our findings with previous studies, specifically those by Cao et al. (2024) and Mazoyer et al. (2020). Our results demonstrate significant improvements and could serve as a relevant baseline for future research in this area.