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An Incremental Clustering Baseline for Event Detection on Twitter

arXiv 2412.15257 ↗First posted 16 Dec 2024cs.CL cs.SIcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 7 of its 23 references that are in the map. First posted on arXiv 16 Dec 2024.

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References · 7

  1. 2017Attention Is All You Needcited by 60,361
  2. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  5. 2019CamemBERT: a Tasty French Language Modelcited by 368
  6. 2023Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detectioncited by 15
  7. 2022From Known to Unknown: Quality-aware Self-improving Graph Neural Network for Open Set Social Event Detectioncited by 12

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