Disaster Tweets Classification using BERT-Based Language Model
Not on the map. Why?
Abstract (arXiv)
Social networking services have became an important communication channel in time of emergency. The aim of this study is to create a machine learning language model that is able to investigate if a person or area was in danger or not. The ubiquitousness of smartphones enables people to announce an emergency they are observing in real-time. Because of this, more agencies are interested in programmatically monitoring Twitter (i.e. disaster relief organizations and news agencies). Design a language model that is able to understand and acknowledge when a disaster is happening based on the social network posts will become more and more necessary over time.
Key facts. Cited by 2 papers in the map (data through 13 Oct 2026). Builds on 5 of its 12 references that are in the map. First posted on arXiv 31 Jan 2022.
Builds on
5 of 12 references.
References · 5
- 2017Attention Is All You Needcited by 60,106
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2014Adam: A Method for Stochastic Optimizationcited by 37,093
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,322
- 2019ERNIE: Enhanced Language Representation with Informative Entitiescited by 526
Built on by
Citing papers per year: 2025: 1; 2026: 1.
2 papers.