Machine Learning-Based Models for Assessing Impacts Before, During and After Hurricane Events

Social media provides an abundant amount of real-time information that can be used before, during, and after extreme weather events. Government officials, emergency managers, and other decision makers can use social media data for decision-making, preparation, and assistance. Machine learning-based models can be used to analyze data collected from social media. Social media data and cloud cover temperature as physical sensor data was analyzed in this study using machine learning techniques. Data was collected from Twitter regarding Hurricane Florence from September 11, 2018 through September 20, 2018 and Hurricane Michael from October 1, 2018 through October 18, 2018. Natural language processing models were developed to demonstrate sentiment among the data. Forecasting models for future events were developed for better emergency management during extreme weather events. Relationships among data were explored using social media data and physical sensor data to analyze extreme weather events as these events become more prevalent in our lives. In this study, social media sentiment analysis was performed that can be used by emergency managers, government officials, and decision makers. Different machine learning algorithms and natural language processing techniques were used to examine sentiment classification. The approach is multi-modal, which will help stakeholders develop a more comprehensive understanding of the social impacts of a storm and how to help prepare for future storms. Of all the classification algorithms used in this study to analyze sentiment, the naive Bayes classifier displayed the highest accuracy for this data. The results demonstrate that machine learning and natural language processing techniques, using Twitter data, are a practical method for sentiment analysis. The data can be used for correlation analysis between social sentiment and physical data and can be used by decision makers for better emergency management decisions

A Graph-based Approach for Detecting Critical Infrastructure Disruptions on Social Media in Disasters

The objective of this paper is to propose and test a graph-based approach for detection of critical infrastructure disruptions in social media data in disasters. Understanding the situation and disruptive events of critical infrastructure is essential to effective disaster response and recovery of communities. The potential of social media data for situation awareness during disasters has been highlighted in recent studies. However, the application of social sensing in detecting disruptions of critical infrastructure is limited because existing approaches cannot provide complete and non-ambiguous situational information about critical infrastructure. Therefore, to address this methodological gap, we developed a graph-based approach including data filtering, burst time-frame detection, content similarity and graph analysis. A case study of Hurricane Harvey in 2017 in Houston was conducted to illustrate the application of the proposed approach. The findings highlighted the temporal patterns of critical infrastructure events that occurred in disasters including disruptive events and their adverse impacts on communities. The findings also provided insights for better understanding critical infrastructure interdependencies in disasters. From the practical perspective, the proposed methodology study can improve the ability of community members, first responders and decision makers to detect and respond to infrastructure disruptions in disasters

A Study of the Diffusion of Innovations and Hurricane Response Communication in the U.S. Coast Guard

Hurricane Harvey (HH) is considered to be the first natural disaster where social-network applications to request help surpassed already overloaded 911 systems (Seetharaman & Wells, 2017). Increasing interpersonal connectivity via Facebook, Twitter, and other social media sites correspond to an increasing need for researchers and responders to recognize how people use social media platforms to connect, share, and receive information especially during times of crisis such as natural disasters. Heightened public perceptions and expectations of response efforts in the digital era make it especially important for first responders to evaluate, monitor, and adapt to these shifts in communication. Disaster-relief groups and emergency responders are looking for help to navigate in this new landscape in order to better serve their constituents and explore new, innovative ways to improve both their efficiency and their empathy. Emergency-response managers must act fast to prevent incorrect or misleading information from reaching the public. Some organizations are expressing interest in social media as a potentially cost-efficient way to disseminate information and official communication. However, as research has shown, innovations take time to diffuse (Rogers, 2003). In this thesis, I examined the diffusion of social media in the ways the U.S. Coast Guard (USCG) (first responder) and the public communicate during crises. Moreover, I examined facilitative and inhibitive factors shaping the diffusion of digital innovations within the USCG. I conclude that the pacing of the diffusion of social media among everyday users is incredibly rapid and, concurrently, is pressuring crisis communication systems like the USCG to quickly adopt these new innovations. I further conclude that Hurricane Harvey should function as a historical catalyst, a clarion call, that government agencies should incorporate social media and associated digital media to improve their future emergency response operations because lives will depend on it

The Use of Social Media in Emergency Management by Public Agencies and Non-Governmental Organizations: Lessons Learned From Areas Affected by Hurricanes Isaac, Sandy, and Harvey

Natural disasters are increasingly costly for the United States. The literature suggests emergency managers may improve disaster outcomes and enhance disaster resilience by supplementing their official public-communications methods with more bi-directional communication tactics using social media. This study aims to understand how social media is used within the “whole community” of emergency management in areas affected by recent hurricanes. The first research objective examines how social media is used by governmental and non-governmental organizations across the four phases of emergency management (preparedness, response, recovery, mitigation). The second objective is to identify challenges governmental and non-governmental groups have encountered and strategies they recommend addressing these problems. The third objective is to examine how social media was used by disaster responders specifically during the response phase of Hurricane Harvey in 2017. We conducted a survey of 269 organizations in areas affected by Hurricanes Isaac and Sandy in 2012 to address research objectives one and two, and for the third objective, surveyed 64 organizations who contributed to the rescue and response efforts during Hurricane Harvey. The first survey found respondents representing government-related organizations use social media more during the response and the preparedness phases, while non-governmental groups report more social media activity during the recovery phase. This finding suggests that organizations performing primary and secondary roles in emergency management play complementary roles in risk and crisis communication with the public. The results also suggest that the emergency management community primarily uses social media to “push” information to the public through established communication networks and could benefit from additional efforts to “pull” information from their networks. Survey respondents report greatest concern about challenges external to their organizations, with the accuracy of information found on social media to be most concerning. The third research objective finds generally high levels of social media use among Hurricane Harvey responders, but also evidence of technical challenges including an inability to convert web-based communications to dispatchable missions due to limited functionality of their 911 systems. The results of the study provide insights regarding uses, challenges, and strategies to improve social media for the whole community of emergency management

Diffusion of Falsehoods on Social Media

Misinformation has captured the interest of academia in recent years with several studies looking at the topic broadly. However, these studies mostly focused on rumors which are social in nature and can be either classified as false or real. In this research, we attempt to bridge the gap in the literature by examining the impacts of user characteristics and feature contents on the diffusion of (mis)information using verified true and false information. We apply a topic allocation model augmented by both supervised and unsupervised machine learning algorithms to identify tweets on novel topics. We find that retweet count is higher for fake news, novel tweets, and tweets with negative sentiment and lower lexical structure. In addition, our results show that the impacts of sentiment are opposite for fake news versus real news. We also find that tweets on the environment have a lower retweet count than the baseline religious news and real social news tweets are shared more often than fake social news. Furthermore, our studies show the counter intuitive nature of current correction endeavors by FEMA and other fact checking organizations in combating falsehoods. Specifically, we show that even though fake news causes an increase in correction messages, they influenced the propagation of falsehoods. Finally our empirical results reveal that correction messages, positive tweets and emotionally charged tweets morph faster. Furthermore, we show that tweets with positive sentiment or are emotionally charged morph faster over time. Word count and past morphing history also positively affect morphing behavior

Rethinking Infrastructure Resilience Assessment with Human Sentiment Reactions on Social Media in Disasters

The objective of this study is to propose and test a theoretical framework which integrates the human sentiment reactions on social media in disasters into infrastructure resilience assessment. Infrastructure resilience assessment is important for reducing adverse consequences of infrastructure failures and promoting human well-being in natural disasters. Integrating societal impacts of infrastructure disruptions can enable a better understanding of infrastructure performance in disasters and human capacities under the stress of disruptions. However, the consideration of societal impacts of infrastructure disruptions is limited in existing studies for infrastructure resilience assessment. The reasons are twofold: first, an integrative theoretical framework for connecting the societal impacts to infrastructure resilience is missing; and second, gathering empirical data for capturing societal impacts of disaster disruptions is challenging. This study proposed a theoretical framework to examine the relationship between the societal impacts and infrastructure performance in disasters using social media data. Sentiments of human messages for relevant infrastructure systems are adopted as an indicator of societal impacts of infrastructure disruptions. A case study for electricity and transportation systems in Houston during the 2017 Hurricane Harvey was conducted to illustrate the application of the proposed framework. We find a relation between human sentiment and infrastructure status and validate it by extracting situational information from relevant tweets and official public data. The findings enable a better understanding of societal expectations and collective sentiments regarding the infrastructure disruptions. Practically, the findings also improve the ability of infrastructure management agencies in infrastructure prioritization and planning decisions

Characterizing Collective Attention via Descriptor Context: A Case Study of Public Discussions of Crisis Events

Social media datasets make it possible to rapidly quantify collective attention to emerging topics and breaking news, such as crisis events. Collective attention is typically measured by aggregate counts, such as the number of posts that mention a name or hashtag. But according to rationalist models of natural language communication, the collective salience of each entity will be expressed not only in how often it is mentioned, but in the form that those mentions take. This is because natural language communication is premised on (and customized to) the expectations that speakers and writers have about how their messages will be interpreted by the intended audience. We test this idea by conducting a large-scale analysis of public online discussions of breaking news events on Facebook and Twitter, focusing on five recent crisis events. We examine how people refer to locations, focusing specifically on contextual descriptors, such as "San Juan" versus "San Juan, Puerto Rico." Rationalist accounts of natural language communication predict that such descriptors will be unnecessary (and therefore omitted) when the named entity is expected to have high prior salience to the reader. We find that the use of contextual descriptors is indeed associated with proxies for social and informational expectations, including macro-level factors like the location's global salience and micro-level factors like audience engagement. We also find a consistent decrease in descriptor context use over the lifespan of each crisis event. These findings provide evidence about how social media users communicate with their audiences, and point towards more fine-grained models of collective attention that may help researchers and crisis response organizations to better understand public perception of unfolding crisis events.Comment: ICWSM 202

Social Media Analytics in Disaster Response: A Comprehensive Review

Social media has emerged as a valuable resource for disaster management, revolutionizing the way emergency response and recovery efforts are conducted during natural disasters. This review paper aims to provide a comprehensive analysis of social media analytics for disaster management. The abstract begins by highlighting the increasing prevalence of natural disasters and the need for effective strategies to mitigate their impact. It then emphasizes the growing influence of social media in disaster situations, discussing its role in disaster detection, situational awareness, and emergency communication. The abstract explores the challenges and opportunities associated with leveraging social media data for disaster management purposes. It examines methodologies and techniques used in social media analytics, including data collection, preprocessing, and analysis, with a focus on data mining and machine learning approaches. The abstract also presents a thorough examination of case studies and best practices that demonstrate the successful application of social media analytics in disaster response and recovery. Ethical considerations and privacy concerns related to the use of social media data in disaster scenarios are addressed. The abstract concludes by identifying future research directions and potential advancements in social media analytics for disaster management. The review paper aims to provide practitioners and researchers with a comprehensive understanding of the current state of social media analytics in disaster management, while highlighting the need for continued research and innovation in this field.Comment: 11 page

Houston College Sport Programs’ Hurricane Harvey Communication: A Twitter Content Analysis

This study provides a Twitter content analysis of tweets by Houston-based Division I college sport programs during Hurricane Harvey. A content analysis was performed on the tweets appearing on the main intercollegiate athletics Twitter pages of University of Houston, Houston Baptist University, Prairie View A&M University, Rice University, and Texas Southern University in response to Hurricane Harvey. The researchers based their study on grounded theory informed by a study conducted by Inoue and Havard (2015). While this study examined tweets rather than newspaper and magazine articles like Inoue and Havard (2015), this study confirmed the theme findings in Inoue and Havard (2015) applied well in a Twitter social media setting as well. New themes that were added by the researchers in the current study proved to be applicable

CrisisMMD: Multimodal Twitter Datasets from Natural Disasters

During natural and man-made disasters, people use social media platforms such as Twitter to post textual and multime- dia content to report updates about injured or dead people, infrastructure damage, and missing or found people among other information types. Studies have revealed that this on- line information, if processed timely and effectively, is ex- tremely useful for humanitarian organizations to gain situational awareness and plan relief operations. In addition to the analysis of textual content, recent studies have shown that imagery content on social media can boost disaster response significantly. Despite extensive research that mainly focuses on textual content to extract useful information, limited work has focused on the use of imagery content or the combination of both content types. One of the reasons is the lack of labeled imagery data in this domain. Therefore, in this paper, we aim to tackle this limitation by releasing a large multi-modal dataset collected from Twitter during different natural disasters. We provide three types of annotations, which are useful to address a number of crisis response and management tasks for different humanitarian organizations.Comment: 9 page