Dataset Submitted

Social media data (texts, images and videos URLs) with geo-referenced locations during major hurricanes that occured in Texas since 2010

Authors: Uday Vysyaraju, Yalong Pi, and David Retchless
Identified On: Apr 30 2024 16:25 UTC
DOI: https://doi.org/10.7266/9eb44a2x
UDI: O1.x158.000:0003
Project Information

Funded By:
Texas OneGulf

Funding Cycle:
Texas OneGulf 2023

Research Group:
Towards Targeted Risk Mitigation: Community Engaged, Fast Impact Estimation of Extreme Weather using Big Social and Climate Data

Point of Contact

Yalong Pi
Texas A&M University
piyalong@tamu.edu

Data Collection Period
2017-08-23
to
2017-09-05
Theme keywords

Social media, Disaster self reports, geo-tag, Images

Abstract:

We collect tweets using X.com's API, dividing the Texas bounding box into 1,332 25x25 miles cells for geolocation search. Each record contains the tweet text, place coordinates, image URL, and other metadata. Retweets and tweets without images or geo-tags are not considered. We collected two weeks before to two weeks after each hurricane event. The results contain roughly 600,000 to 700,000 tweets. The URLs can be directly used to download the images.

Suggested Citation:

Uday Vysyaraju, Yalong Pi, and David Retchless. Social media data (texts, images and videos URLs) with geo-referenced locations during major hurricanes that occured in Texas since 2010. Distributed by: GRIIDC, Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/9eb44a2x

Purpose:

This data can serve as social media input for the deep learning models to estimate the disaster damage. This input can be text, image, or a combination of all meta data such as climate and demographic information.

Data Parameters and Units:

Description (text), lang (language identifier, text), geo (X's geo object information, text), preview_image_url (URL to the static placeholder preview, URL), url (URL to the media file on Twitter, URL), media_key (unique identifier of the expanded media content, text), text (UTF-8 text of the Tweet, text), place_id (place IDs that can be retrieved from geo/reverse_geocode, text), Place (named locations with corresponding geo in coordinates, text), id (unique ID of each tweet, text), images (compressed RGB image)

Methods:

The researchers used X.com API with a $5000/month subscription to collect the data. The code and time windows were researched and defined before the start of the data collection to maximize the data size. Eventually, the 1 million tweet limit was reached in the third week of the data collection process. Therefore, the subscription was canceled after one month.