Related papers: Evacuation decisions in response to natural disasters: Insights from a large-scale social media survey

Related papers

RESCUE: Crowd Evacuation Simulation via Controlling SDM-United Characters [48.356346584588906]
Current evacuation models overlook the complex human behaviors that occur during evacuation.<n>We propose a real-time 3D crowd evacuation simulation framework that integrates a 3D-adaptive SFM (Social Force Model) Decision Mechanism and a Personalized Gait Control Motor.
arXiv Detail & Related papers (2025-07-27T03:50:18Z)
Behavioral response to mobile phone evacuation alerts [1.4434037690965207]
This study examines behavioral responses to mobile phone evacuation alerts during the February 2024 wildfires in Valpara'iso, Chile.<n>Using anonymized mobile network data from 580,000 devices, we analyze population movement following emergency SMS notifications.
arXiv Detail & Related papers (2025-03-27T13:33:56Z)
Transforming Social Science Research with Transfer Learning: Social Science Survey Data Integration with AI [0.4944564023471818]
Large-N nationally representative surveys, which have profoundly shaped American politics scholarship, represent related but distinct domains. Our study introduces a novel application of transfer learning (TL) to address these gaps. Models pre-trained on the Cooperative Election Study dataset are fine-tuned for use in the American National Election Studies dataset.
arXiv Detail & Related papers (2025-01-11T16:01:44Z)
Labeled Datasets for Research on Information Operations [71.34999856621306]
We present new labeled datasets about 26 campaigns, which contain both IO posts verified by a social media platform and over 13M posts by 303k accounts that discussed similar topics in the same time frames (control data) The datasets will facilitate the study of narratives, network interactions, and engagement strategies employed by coordinated accounts across various campaigns and countries.
arXiv Detail & Related papers (2024-11-15T22:15:01Z)
How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users [50.699390248359265]
Browser fingerprinting can be used to identify and track users across the Web, even without cookies. This technique and resulting privacy risks have been studied for over a decade. We provide a first-of-its-kind dataset to enable further research.
arXiv Detail & Related papers (2024-10-09T14:51:58Z)
Evacuation patterns and socioeconomic stratification in the context of wildfires in Chile [1.5157842912803314]
We use high-definition mobile phone records to analyse evacuation patterns during the wildfires in Valpara'iso, Chile. We find that many people spent nights away from home, with those in the lowest socioeconomic segment stayed away the longest. Our results show that socioeconomic differences play a role in evacuation dynamics, providing useful insights for response planning.
arXiv Detail & Related papers (2024-10-08T13:18:49Z)
Tracing the Unseen: Uncovering Human Trafficking Patterns in Job Listings [9.450459784653196]
We analyze over a quarter million job postings collected from eight relevant regions across the United States, spanning nearly two decades (2006-2024) Our investigation into the types of advertised opportunities, the modes of preferred contact, and the frequency of postings uncovers the patterns characterizing suspicious ads. This research underscores the imperative for a deeper dive into how online job boards and communication platforms could be unwitting facilitators of human trafficking.
arXiv Detail & Related papers (2024-06-18T10:18:15Z)
CrisisSense-LLM: Instruction Fine-Tuned Large Language Model for Multi-label Social Media Text Classification in Disaster Informatics [50.122541222825156]
This study introduces a novel approach to disaster text classification by enhancing a pre-trained Large Language Model (LLM)<n>Our methodology involves creating a comprehensive instruction dataset from disaster-related tweets, which is then used to fine-tune an open-source LLM.<n>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)
What we can learn from TikTok through its Research API [3.424635462664968]
The recent release of a free Research API opens the door to collecting data on posted videos, associated comments, and user activities. Our study focuses on evaluating the reliability of the results returned by the Research API, by collecting and analyzing a random sample of TikTok videos posted in a span of 6 years.
arXiv Detail & Related papers (2024-02-21T14:59:49Z)
Data-Driven Bayesian Network Models of Hurricane Evacuation Decision Making [2.749589513485177]
We propose using Bayesian Networks (BNs) to model evacuation decisions during hurricanes. We collected questionnaire data from two significant hurricane events: Hurricane Harvey and Hurricane Irma. We examined and compared the learned structures of both hurricanes, revealing potential causal relationships among key predictors of evacuation.
arXiv Detail & Related papers (2023-11-16T22:58:57Z)
Investigating disaster response through social media data and the Susceptible-Infected-Recovered (SIR) model: A case study of 2020 Western U.S. wildfire season [0.8999666725996975]
Social media can reflect public concerns and demands during a disaster. We used Bidirectional Representations from Transformers (BERT) topic modeling to cluster topics from Twitter data. Our study details how the SIR model and topic modeling using social media data can provide decision-makers with a quantitative approach to measure disaster response.
arXiv Detail & Related papers (2023-08-10T01:51:33Z)
Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
arXiv Detail & Related papers (2023-01-02T12:40:05Z)
Fast Few shot Self-attentive Semi-supervised Political Inclination Prediction [12.472629584751509]
It is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations. We introduce a self-attentive semi-supervised framework for political inclination detection to further that objective. We found that the model is highly efficient even in resource-constrained settings.
arXiv Detail & Related papers (2022-09-21T12:07:16Z)
Releasing survey microdata with exact cluster locations and additional privacy safeguards [77.34726150561087]
We propose an alternative microdata dissemination strategy that leverages the utility of the original microdata with additional privacy safeguards. Our strategy reduces the respondents' re-identification risk for any number of disclosed attributes by 60-80% even under re-identification attempts.
arXiv Detail & Related papers (2022-05-24T19:37:11Z)
Inverse Online Learning: Understanding Non-Stationary and Reactionary Policies [79.60322329952453]
We show how to develop interpretable representations of how agents make decisions. By understanding the decision-making processes underlying a set of observed trajectories, we cast the policy inference problem as the inverse to this online learning problem. We introduce a practical algorithm for retrospectively estimating such perceived effects, alongside the process through which agents update them. Through application to the analysis of UNOS organ donation acceptance decisions, we demonstrate that our approach can bring valuable insights into the factors that govern decision processes and how they change over time.
arXiv Detail & Related papers (2022-03-14T17:40:42Z)
Detecting Damage Building Using Real-time Crowdsourced Images and Transfer Learning [53.26496452886417]
This paper presents an automated way to extract the damaged building images after earthquakes from social media platforms such as Twitter. Using transfer learning and 6500 manually labelled images, we trained a deep learning model to recognize images with damaged buildings in the scene. The trained model achieved good performance when tested on newly acquired images of earthquakes at different locations and ran in near real-time on Twitter feed after the 2020 M7.0 earthquake in Turkey.
arXiv Detail & Related papers (2021-10-12T06:31:54Z)
MigrationsKB: A Knowledge Base of Public Attitudes towards Migrations and their Driving Factors [1.6973426830397942]
This study is the analysis of social media platform to quantify public attitudes towards migrations. The tweets spanning from 2013 to Jul-2021 in the European countries which are hosts to immigrants are collected. The external databases are used to identify the potential social and economic factors causing negative attitudes of the people about migration.
arXiv Detail & Related papers (2021-08-17T12:50:39Z)
From Static to Dynamic Prediction: Wildfire Risk Assessment Based on Multiple Environmental Factors [69.9674326582747]
Wildfire is one of the biggest disasters that frequently occurs on the west coast of the United States. We propose static and dynamic prediction models to analyze and assess the areas with high wildfire risks in California.
arXiv Detail & Related papers (2021-03-14T17:56:17Z)
Constructing Evacuation Evolution Patterns and Decisions Using Mobile Device Location Data: A Case Study of Hurricane Irma [5.902556437760098]
This paper utilized a large mobile phone Location-Based Services (LBS) data to construct the evacuation pattern during the landfall of Hurricane Irma. We studied users' evacuation decisions, departure and reentry date distribution, and destination choice. Our analysis revealed the importance of the individuals' mobility behavior in modeling the evacuation decision choice.
arXiv Detail & Related papers (2021-02-24T23:24:10Z)
Face Off: Polarized Public Opinions on Personal Face Mask Usage during the COVID-19 Pandemic [77.34726150561087]
A series of policy shifts by various governmental bodies have been speculated to have contributed to the polarization of face masks. We propose a novel approach to accurately gauge public sentiment towards face masks in the United States during COVID-19. We find two key policy-shift events contributed to statistically significant changes in sentiment for both Republicans and Democrats.
arXiv Detail & Related papers (2020-10-31T18:52:41Z)
Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z)
Towards robust sensing for Autonomous Vehicles: An adversarial perspective [82.83630604517249]
It is of primary importance that the resulting decisions are robust to perturbations. Adversarial perturbations are purposefully crafted alterations of the environment or of the sensory measurements. A careful evaluation of the vulnerabilities of their sensing system(s) is necessary in order to build and deploy safer systems.
arXiv Detail & Related papers (2020-07-14T05:25:15Z)
Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide [6.61600499731972]
We examine the usefulness of the Facebook Advertising platform, which offers a digital "census" of over two billions of its users. We show that the population statistics that Facebook produces suffer from instability across time and incomplete coverage of sparsely populated municipalities. Using official national census data, we evaluate our approach and confirm known significant urban-rural divides in terms of educational attainment and income.
arXiv Detail & Related papers (2020-02-26T17:19:24Z)

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