Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey

• URL: http://arxiv.org/abs/2408.11133v1
• Date: Tue, 20 Aug 2024 18:31:20 GMT
• Title: Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey
• Authors: Thomas Hoang, Quynh Anh Nguyen, Long Nguyen,
• Abstract summary: We collected a dataset of approximately 400,000 public tweets related to the storm. Using a BERT-based model, we predicted the emotions associated with each tweet. We further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM)
• Score: 1.433758865948252
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Countless disasters have resulted from climate change, causing severe damage to infrastructure and the economy. These disasters have significant societal impacts, necessitating mental health services for the millions affected. To prepare for and respond effectively to such events, it is important to understand people's emotions and the life incidents they experience before and after a disaster strikes. In this case study, we collected a dataset of approximately 400,000 public tweets related to the storm. Using a BERT-based model, we predicted the emotions associated with each tweet. To efficiently identify these topics, we utilized the Latent Dirichlet Allocation (LDA) technique for topic modeling, which allowed us to bypass manual content analysis and extract meaningful patterns from the data. However, rather than stopping at topic identification like previous methods \cite{math11244910}, we further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM). The GNN was employed to generate embeddings and construct a similarity graph of the tweets, which was then used to optimize clustering. Subsequently, we used an LLM to automatically generate descriptive names for each event cluster, offering critical insights for disaster preparedness and response strategies.

Related papers

• CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [49.2719253711215]
  This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM) Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM. 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.
  arXiv  Detail & Related papers  (2024-06-16T23:01:10Z)
• CrisisMatch: Semi-Supervised Few-Shot Learning for Fine-Grained Disaster Tweet Classification [51.58605842457186]
  We present a fine-grained disaster tweet classification model under the semi-supervised, few-shot learning setting. Our model, CrisisMatch, effectively classifies tweets into fine-grained classes of interest using few labeled data and large amounts of unlabeled data.
  arXiv  Detail & Related papers  (2023-10-23T07:01:09Z)
• Sarcasm Detection in a Disaster Context [103.93691731605163]
  We introduce HurricaneSARC, a dataset of 15,000 tweets annotated for intended sarcasm. Our best model is able to obtain as much as 0.70 F1 on our dataset.
  arXiv  Detail & Related papers  (2023-08-16T05:58:12Z)
• IKDSumm: Incorporating Key-phrases into BERT for extractive Disaster Tweet Summarization [5.299958874647294]
  We propose a disaster-specific tweet summarization framework, IKDSumm. IKDSumm identifies the crucial and important information from each tweet related to a disaster through key-phrases of that tweet. We utilize these key-phrases to automatically generate a summary of the tweets.
  arXiv  Detail & Related papers  (2023-05-19T11:05:55Z)
• When a crisis strikes: Emotion analysis and detection during COVID-19 [96.03869351276478]
  We present CovidEmo, 1K tweets labeled with emotions. We examine how well large pre-trained language models generalize across domains and crises.
  arXiv  Detail & Related papers  (2021-07-23T04:07:14Z)
• Event-Related Bias Removal for Real-time Disaster Events [67.2965372987723]
  Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks. Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time. We train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
  arXiv  Detail & Related papers  (2020-11-02T02:03:07Z)
• Detecting Perceived Emotions in Hurricane Disasters [62.760131661847986]
  We introduce HurricaneEmo, an emotion dataset of 15,000 English tweets spanning three hurricanes: Harvey, Irma, and Maria. We present a comprehensive study of fine-grained emotions and propose classification tasks to discriminate between coarse-grained emotion groups.
  arXiv  Detail & Related papers  (2020-04-29T16:17:49Z)
• Semantic-based End-to-End Learning for Typhoon Intensity Prediction [0.2580765958706853]
  Existing technologies employ different machine learning approaches to predict incoming disasters from historical environmental data. Social media posts (e.g., tweets) is very informal and contains only limited content. We propose an end-to-end based framework that learns from disaster-related tweets and environmental data to improve typhoon intensity prediction.
  arXiv  Detail & Related papers  (2020-03-22T01:13:20Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.