For over a decade, social media has proved to be a functional and convenient data source in the Internet of things. Social platforms such as Facebook, Twitter, Instagram, and Reddit have their own styles and purposes. Twitter, among them, has become the most popular platform in the research community due to its nature of attracting people to write brief posts about current and unexpected events (e.g., natural disasters). The immense popularity of such sites has opened a new horizon in `social sensing' to manage disaster response. Sensing through social media platforms can be used to track and analyze natural disasters and evaluate the overall response (e.g., resource allocation, relief, cost and damage estimation). In this paper, we propose a two-step methodology: i) wavelet analysis and ii) predictive modeling to track the progression of a disaster aftermath and predict the time-line. We demonstrate that wavelet features can preserve text semantics and predict the total duration for localized small scale disasters. The experimental results and observations on two real data traces (flash flood in Cummins Falls state park and Arizona swimming hole) showcase that the wavelet features can predict disaster time-line with an error lower than 20% with less than 50% ofmore »
Identifying the Context of Hurricane Posts on Twitter using Wavelet Features
With the increase of natural disasters all over the world, we are in crucial need of innovative solutions with inexpensive implementations to assist the emergency response systems. Information collected through conventional sources (e.g., incident reports, 911 calls, physical volunteers, etc.) are proving to be insufficient . Responsible organizations are now leaning towards research grounds that explore digital human connectivity and freely available sources of information. U.S. Geological Survey and Federal Emergency Management Agency (FEMA) introduced Critical Lifeline (CLL) s which identifies the most significant areas that require immediate attention in case of natural disasters. These organizations applied crowdsourcing by connecting digital volunteer networks to collect data on the critical lifelines from data sources including social media , , . In the past couple of years, during some of the deadly hurricanes (e.g., Harvey, IRMA, Maria, Michael, Florence, etc.), people took on different social media platforms like never seen before, in search of help for rescue, shelter, and relief. Their posts reflect crisis updates and their real-time observations on the devastation that they witness. In this paper, we propose a methodology to build and analyze time-frequency features of words on social media to assist the volunteer networks in identifying the context more »
- Award ID(s):
- Publication Date:
- NSF-PAR ID:
- Journal Name:
- 2019 IEEE International Conference on Smart Computing (SMARTCOMP)
- Page Range or eLocation-ID:
- 350 to 358
- Sponsoring Org:
- National Science Foundation
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Timely delivery of the right information to the right first responders can help improve the outcomes of their efforts and save lives. With social media communications (Twitter, Facebook, etc.) being increasingly used to send and get information during disasters, forwarding them to the right first responders in a timely manner can be very helpful. We use Natural Language Processing and Machine Learning, to steer the social media posts to the most appropriate first responder.An important goal is to retrieve and deliver only the critical, actionable information to first responders in real-time. We examine the overall pipeline starting from retrieving tweets from the social media platforms, to their classification, and dissemination to first responders.We propose improvements in the area of data retrieval, relevance prediction and prioritizing information sent to the first responders by fusing NLP and ML classification techniques thus improving emergency response. We demonstrate the effectiveness of our proposed approach in retrieving and extracting 37,295 actionable tweets related to the IDA hurricane that occurred in the US in Aug.–Sep, 2021.
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