# On Informative Tweet Identification For Tracking Mass Events

arXiv: https://arxiv.org/abs/2101.05656 · HTML: https://mlsanity.com/paper/2101.05656/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 28 references that are in the map.
- First posted on arXiv 14 Jan 2021.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [CrisisMMD: Multimodal Twitter Datasets from Natural Disasters](https://mlsanity.com/paper/1805.00713/) (2018)
- [Processing Social Media Messages in Mass Emergency: A Survey](https://mlsanity.com/paper/1407.7071/) (2014)
- [WNUT-2020 Task 2: Identification of Informative COVID-19 English Tweets](https://mlsanity.com/paper/2010.08232/) (2020)

## Abstract (arXiv)

Twitter has been heavily used as an important channel for communicating and discussing about events in real-time. In such major events, many uninformative tweets are also published rapidly by many users, making it hard to follow the events. In this paper, we address this problem by investigating machine learning methods for automatically identifying informative tweets among those that are relevant to a target event. We examine both traditional approaches with a rich set of handcrafted features and state of the art approaches with automatically learned features. We further propose a hybrid model that leverages both the handcrafted features and the automatically learned ones. Our experiments on several large datasets of real-world events show that the latter approaches significantly outperform the former and our proposed model performs the best, suggesting highly effective mechanisms for tracking mass events.