In the field of crisis/disaster informatics, social media is increasingly
being used for improving situational awareness to inform response and relief
efforts. Efficient and accurate text classification tools have been a focal
area of investigation in crisis informatics. However, current methods mostly
rely on single-label text classification models, which fails to capture
different insights embedded in dynamic and multifaceted disaster-related social
media data. This study introduces a novel approach to disaster text
classification by enhancing a pre-trained Large Language Model (LLM) through
instruction fine-tuning targeted for multi-label classification of
disaster-related tweets. Our methodology involves creating a comprehensive
instruction dataset from disaster-related tweets, which is then used to
fine-tune an open-source LLM, thereby embedding it with disaster-specific
knowledge. This fine-tuned model can classify multiple aspects of
disaster-related information simultaneously, such as the type of event,
informativeness, and involvement of human aid, significantly improving the
utility of social media data for situational awareness in disasters. The
results demonstrate that this approach enhances the categorization of critical
information from social media posts, thereby facilitating a more effective
deployment for situational awareness during emergencies. This research paves
the way for more advanced, adaptable, and robust disaster management tools,
leveraging the capabilities of LLMs to improve real-time situational awareness
and response strategies in disaster scenarios.

通过对事先训练的大型语言模型（LLM）进行指令微调，将其嵌入灾害特定知识，从而实现对灾害相关推文的多标签分类，提高社交媒体数据在灾难情境感知中的效用。

CrisisSense-LLM：灾难信息学中用于多标签社交媒体文本分类的指令微调大型语言模型

CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for  Multi-label Social Media Text Classification in Disaster Informatics

Tasks such as semantic search and clustering on crisis-related social media
texts enhance our comprehension of crisis discourse, aiding decision-making and
targeted interventions. Pre-trained language models have advanced performance
in crisis informatics, but their contextual embeddings lack semantic
meaningfulness. Although the CrisisTransformers family includes a sentence
encoder to address the semanticity issue, it remains monolingual, processing
only English texts. Furthermore, employing separate models for different
languages leads to embeddings in distinct vector spaces, introducing challenges
when comparing semantic similarities between multi-lingual texts. Therefore, we
propose multi-lingual sentence encoders (CT-XLMR-SE and CT-mBERT-SE) that embed
crisis-related social media texts for over 50 languages, such that texts with
similar meanings are in close proximity within the same vector space,
irrespective of language diversity. Results in sentence encoding and sentence
matching tasks are promising, suggesting these models could serve as robust
baselines when embedding multi-lingual crisis-related social media texts. The
models are publicly available at: this https URL

通过使用多语言句子编码器，我们提出了一种能够为 50 多种语言中的相关社交媒体文本进行嵌入的模型，以使具有相似含义的文本在相同的向量空间中靠近，无论语言多样性如何。研究结果对句子编码和匹配任务表现出良好的性能，表明这些模型可以作为嵌入多语言危机相关社交媒体文本时的强大基准模型。

危机相关社交媒体文本的语义丰富跨语言句子嵌入

Semantically Enriched Cross-Lingual Sentence Embeddings for  Crisis-related Social Media Texts

Time-critical analysis of social media streams is important for humanitarian
organizations for planing rapid response during disasters. The \textit{crisis
informatics} research community has developed several techniques and systems
for processing and classifying big crisis-related data posted on social media.
However, due to the dispersed nature of the datasets used in the literature
(e.g., for training models), it is not possible to compare the results and
measure the progress made towards building better models for crisis informatics
tasks. In this work, we attempt to bridge this gap by combining various
existing crisis-related datasets. We consolidate eight human-annotated datasets
and provide 166.1k and 141.5k tweets for \textit{informativeness} and
\textit{humanitarian} classification tasks, respectively. We believe that the
consolidated dataset will help train more sophisticated models. Moreover, we
provide benchmarks for both binary and multiclass classification tasks using
several deep learning architecrures including, CNN, fastText, and transformers.
We make the dataset and scripts available at:
this https URL

本论文旨在通过整合多个数据集，提高处理社交媒体上危机相关数据的深度学习模型的性能，从而增强人道主义应对能力。研究集中在危机信息学的相关技术和系统，以及二元或多类分类任务的性能指标。