# Floods Relevancy and Identification of Location from Twitter Posts using NLP Techniques

arXiv: https://arxiv.org/abs/2301.00321 · HTML: https://mlsanity.com/paper/2301.00321/

## Key facts

- Cited by 3 papers in the map (data through 7 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 1 Jan 2023.

## Builds on (most cited first, 10 of 10)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [ALBERT: A Lite BERT for Self-supervised Learning of Language Representations](https://mlsanity.com/paper/1909.11942/) (2019)
- [A Survey on Deep Learning for Named Entity Recognition](https://mlsanity.com/paper/1812.09449/) (2018)
- [CrisisMMD: Multimodal Twitter Datasets from Natural Disasters](https://mlsanity.com/paper/1805.00713/) (2018)
- [Natural Disasters Detection in Social Media and Satellite imagery: a survey](https://mlsanity.com/paper/1901.04277/) (2019)
- [Visual Sentiment Analysis from Disaster Images in Social Media](https://mlsanity.com/paper/2009.03051/) (2020)
- [Social Media as an Instant Source of Feedback on Water Quality](https://mlsanity.com/paper/2202.04462/) (2022)
- [Automatic detection of passable roads after floods in remote sensed and social media data](https://mlsanity.com/paper/1901.03298/) (2019)

## Built on by (most cited first, 3 of 3)

- [A Named Entity Recognition and Topic Modeling-based Solution for Locating and Better Assessment of Natural Disasters in Social Media](https://mlsanity.com/paper/2405.00903/) (2024)
- [Deep Learning Based Named Entity Recognition Models for Recipes](https://mlsanity.com/paper/2402.17447/) (2024)
- [Social Media Informatics for Sustainable Cities and Societies: An Overview of the Applications, associated Challenges, and Potential Solutions](https://mlsanity.com/paper/2412.03600/) (2024)

## Abstract (arXiv)

This paper presents our solutions for the MediaEval 2022 task on DisasterMM. The task is composed of two subtasks, namely (i) Relevance Classification of Twitter Posts (RCTP), and (ii) Location Extraction from Twitter Texts (LETT). The RCTP subtask aims at differentiating flood-related and non-relevant social posts while LETT is a Named Entity Recognition (NER) task and aims at the extraction of location information from the text. For RCTP, we proposed four different solutions based on BERT, RoBERTa, Distil BERT, and ALBERT obtaining an F1-score of 0.7934, 0.7970, 0.7613, and 0.7924, respectively. For LETT, we used three models namely BERT, RoBERTa, and Distil BERTA obtaining an F1-score of 0.6256, 0.6744, and 0.6723, respectively.