Over the last decade, similar to other application domains, social media
content has been proven very effective in disaster informatics. However, due to
the unstructured nature of the data, several challenges are associated with
disaster analysis in social media content. To fully explore the potential of
social media content in disaster informatics, access to relevant content and
the correct geo-location information is very critical. In this paper, we
propose a three-step solution to tackling these challenges. Firstly, the
proposed solution aims to classify social media posts into relevant and
irrelevant posts followed by the automatic extraction of location information
from the posts' text through Named Entity Recognition (NER) analysis. Finally,
to quickly analyze the topics covered in large volumes of social media posts,
we perform topic modeling resulting in a list of top keywords, that highlight
the issues discussed in the tweet. For the Relevant Classification of Twitter
Posts (RCTP), we proposed a merit-based fusion framework combining the
capabilities of four different models namely BERT, RoBERTa, Distil BERT, and
ALBERT obtaining the highest F1-score of 0.933 on a benchmark dataset. For the
Location Extraction from Twitter Text (LETT), we evaluated four models namely
BERT, RoBERTa, Distil BERTA, and Electra in an NER framework obtaining the
highest F1-score of 0.960. For topic modeling, we used the BERTopic library to
discover the hidden topic patterns in the relevant tweets. The experimental
results of all the components of the proposed end-to-end solution are very
encouraging and hint at the potential of social media content and NLP in
disaster management.

在这篇论文中，我们提出了一个三步解决方案，旨在充分利用社交媒体内容和自然语言处理技术，实现灾害信息学中的相关分类、地点提取和主题建模，以应对社交媒体内容中的挑战。

基于命名实体识别和主题建模的社交媒体中自然灾害定位和评估的解决方案

A Named Entity Recognition and Topic Modeling-based Solution for  Locating and Better Assessment of Natural Disasters in Social Media

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.

本文报告了在 DisasterMM 任务中，提出了基于 BERT、RoBERTa、Distil BERT 和 ALBERT 的四种不同解决方案用于推特帖子的相关分类， AND 基于 BERT、RoBERTa 和 Distil BERTA 的三种模型用于从文本中提取地理位置信息。我们得到了相应的 F1 分数，这些训练模型对于 DisasterMM 任务中推特的相关分类和地理位置提取得到了良好的性能。