Using Twitter Data to Determine Hurricane Category: An Experiment
- URL: http://arxiv.org/abs/2308.05866v1
- Date: Thu, 10 Aug 2023 22:30:24 GMT
- Title: Using Twitter Data to Determine Hurricane Category: An Experiment
- Authors: Songhui Yue, Jyothsna Kondari, Aibek Musaev, Randy K. Smith, Songqing
Yue
- Abstract summary: Social media posts contain an abundant amount of information about public opinion on major events.
This paper investigates the correlation between the Twitter data of a specific area and the hurricane level in that area.
We also present a method to predict the hurricane category for a specific area using relevant Twitter data.
- Score: 0.19573380763700707
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Social media posts contain an abundant amount of information about public
opinion on major events, especially natural disasters such as hurricanes. Posts
related to an event, are usually published by the users who live near the place
of the event at the time of the event. Special correlation between the social
media data and the events can be obtained using data mining approaches. This
paper presents research work to find the mappings between social media data and
the severity level of a disaster. Specifically, we have investigated the
Twitter data posted during hurricanes Harvey and Irma, and attempted to find
the correlation between the Twitter data of a specific area and the hurricane
level in that area. Our experimental results indicate a positive correlation
between them. We also present a method to predict the hurricane category for a
specific area using relevant Twitter data.
Related papers
- Public Health in Disaster: Emotional Health and Life Incidents Extraction during Hurricane Harvey [1.433758865948252]
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)
arXiv Detail & Related papers (2024-08-20T18:31:20Z) - 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) - Relevance Classification of Flood-related Twitter Posts via Multiple
Transformers [3.7399138244928145]
We propose a text classification framework to deal with Twitter noisy data.
More specifically, we employed several transformers both individually and in combination, so as to differentiate between relevant and non-relevant Twitter posts.
arXiv Detail & Related papers (2023-01-01T01:34:15Z) - CrisisLTLSum: A Benchmark for Local Crisis Event Timeline Extraction and
Summarization [62.77066949111921]
This paper presents CrisisLTLSum, the largest dataset of local crisis event timelines available to date.
CrisisLTLSum contains 1,000 crisis event timelines across four domains: wildfires, local fires, traffic, and storms.
Our initial experiments indicate a significant gap between the performance of strong baselines compared to the human performance on both tasks.
arXiv Detail & Related papers (2022-10-25T17:32:40Z) - A multi-modal approach towards mining social media data during natural
disasters -- a case study of Hurricane Irma [1.9259288012724252]
We use 54,383 Twitter messages (out of 784K geolocated messages) from 16,598 users to develop 4 independent models to filter data for relevance.
All four models are independently tested, and can be combined to quickly filter and visualize tweets.
arXiv Detail & Related papers (2021-01-02T17:08:53Z) - 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) - Understanding the Hoarding Behaviors during the COVID-19 Pandemic using
Large Scale Social Media Data [77.34726150561087]
We analyze the hoarding and anti-hoarding patterns of over 42,000 unique Twitter users in the United States from March 1 to April 30, 2020.
We find the percentage of females in both hoarding and anti-hoarding groups is higher than that of the general Twitter user population.
The LIWC anxiety mean for the hoarding-related tweets is significantly higher than the baseline Twitter anxiety mean.
arXiv Detail & Related papers (2020-10-15T16:02:25Z) - Social Media Information Sharing for Natural Disaster Response [0.0]
Social media has become an essential channel for posting disaster-related information, which provide governments and relief agencies real-time data for better disaster management.
This paper aims to improve disaster relief efficiency via mining and analyzing social media data like public attitudes towards disaster response and public demands for targeted relief supplies during different types of disasters.
arXiv Detail & Related papers (2020-05-08T21:11:39Z) - 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)
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.