skip to main content

Title: 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 [1]. 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 [3], [4], [5]. 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 » before, during and after a natural disaster and distinguishing contexts connected to the critical lifelines. We employ Continuous Wavelet Transform to help create word features and propose two ways to reduce the dimensions which we use to create word clusters to identify themes of conversations associated with stages of a disaster and these lifelines. We compare two different methodologies of wavelet features and word clusters both qualitatively and quantitatively, to show that wavelet features can identify and separate context without using semantic information as inputs. « less
; ;
Award ID(s):
Publication Date:
Journal Name:
2019 IEEE International Conference on Smart Computing (SMARTCOMP)
Page Range or eLocation-ID:
350 to 358
Sponsoring Org:
National Science Foundation
More Like this
  1. 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 »the data when compared to ground truth.« less
  2. The objective of this paper is to propose and test a system analytics framework based on social sensing and text mining to detect topic evolution associated with the performance of infrastructure systems in disasters. Social media, like Twitter, as active channels of communication and information dissemination, provide insights into real-time information and first-hand experience from affected areas in mass emergencies. While the existing studies show the importance of social sensing in improving situational awareness and emergency response in disasters, the use of social sensing for detection and analysis of infrastructure systems and their resilience performance has been rather limited. This limitation is due to the lack of frameworks to model the events and topics (e.g., grid interruption and road closure) evolution associated with infrastructure systems (e.g., power, highway, airport, and oil) in times of disasters. The proposed framework detects infrastructure-related topics of the tweets posted in disasters and their evolutions by integrating searching relevant keywords, text lemmatization, Part-of-Speech (POS) tagging, TF-IDF vectorization, topic modeling by using Latent Dirichlet Allocation (LDA), and K-Means clustering. The application of the proposed framework was demonstrated in a study of infrastructure systems in Houston during Hurricane Harvey. In this case study, more than sixty thousandmore »tweets were retrieved from 150-mile radius in Houston over 39 days. The analysis of topic detection and evolution from user-generated data were conducted, and the clusters of tweets pertaining to certain topics were mapped in networks over time. The results show that the proposed framework enables to summarize topics and track the movement of situations in different disaster phases. The analytics elements of the proposed framework can improve the recognition of infrastructure performance through text-based representation and provide evidence for decision-makers to take actionable measurements.« less
  3. 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.
  4. The increasing popularity of multimedia messages shared through public or private social media spills into diverse information dissemination contexts. To date, public social media has been explored as a potential alert system during natural disasters, but high levels of noise (i.e., non-relevant content) present challenges in both understanding social experiences of a disaster and in facilitating disaster recovery. This study builds on current research by uniquely using social media data, collected in the field through qualitative interviews, to create a supervised machine learning model. Collected data represents rescuers and rescuees during the 2017 Hurricane Harvey. Preliminary findings indicate a 99% accuracy in classifying data between signal and noise for signal-to-noise ratios (SNR) of 1:1, 1:2, 1:4, and 1:8. We also find 99% accuracy in classification between respondent types (volunteer rescuer, official rescuer, and rescuee). We furthermore compare human and machine coded attributes, finding that Google Vision API is a more reliable source of detecting attributes for the training set.
  5. The social media have been increasingly used for disaster management (DM) via providing real time data on a broad scale. For example, some smartphone applications (e.g. Disaster Alert and Federal Emergency Management Agency (FEMA) App) can be used to increase the efficiency of prepositioning supplies and to enhance the effectiveness of disaster relief efforts. To maximize utilities of these apps, it is imperative to have robust human behavior models in social networks with detailed expressions of individual decision-making processes and of the interactions among people. In this paper, we introduce a hierarchical human behavior model by associating extended Decision Field Theory (e-DFT) with the opinion formation and innovation diffusion models. Particularly, its expressiveness and validity are addressed in three ways. First, we estimate individual’s choice patterns in social networks by deriving people’s asymptotic choice probabilities within e-DFT. Second, by analyzing opinion formation models and innovation diffusion models in different types of social networks, the effects of neighbor’s opinions on people and their interactions are demonstrated. Finally, an agent-based simulation is used to trace agents’ dynamic behaviors in different scenarios. The simulated results reveal that the proposed models can be used to establish better disaster management strategies in natural disasters.