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

An Evaluation of Geotagged Twitter Data during Hurricane Irma using Sentiment Analysis and Topic Modeling for Disaster Resilience

Disasters require quick response times, thought-out preparations, overall community, and government support. These efforts will ensure prevention of loss of life and reduce possible damages. The United States has been battered by multiple major hurricanes in the recent years and multiple avenues of disaster response efforts were being tested. Hurricane Irma can be recognized as the most popular hurricane in terms of social media attention. Irma made landfall in Florida as a Category 4 storm and preparation measures taken were intensive thus providing a good measure to evaluate in terms of efficacy. The effectiveness of the response methods utilized are evaluated using Twitter data that was collected from September 1st to September 16th, 2017. About 221,598 geotagged tweets were analyzed using sentiment analysis, text visualization, and exploratory analysis. The objective of this research is to establish an observable pattern regarding sentiment trends over the progression of the storm and produce a viable set of topic models for its totality. The study contributed to the literature by identifying which topics and keywords were most frequently used in tweets through sentiment analysis and topic modeling to determine what resources or concerns were most significant within a region during the hurricane Irma. The results from this study demonstrate that the sentiment analysis can measure people’s emotions during the natural disaster, which the authorities can use to limit the damage and effectively recover from the disaster. In this work, we have also reviewed the related works from the text/sentiment analysis, social media analysis from hurricanes/disaster perspective. This research can be further improved by incorporating sentiment analysis methods for classifying emoticons and non-textual components such as videos or images

Lexicon-Based Sentiment Analysis and Emotion Classification of Climate Change Related Tweets

The concerns for a potential future climate jeopardy has steered actions by youths globally to call the governments to immediately address challenges relating to climate change. In this paper, using natural language processing techniques in data science domain, we analyzed twitter micro-blogging streams to detect emotions and sentiments that surround the Global youth Climate Protest (GloClimePro) with respect to #ThisIsZeroHour, #ClimateJustice and #WeDontHaveTime hashtags. The analysis follows tweet scrapping, cleaning and preprocessing, extraction of GloClimePro-related items, sentiment analysis, emotion classification, and visualization. The results obtained reveal that most people expressed joy, anticipation and trust emotions in the #ThisIsZeroHour and #ClimateJustice action than the few who expressed disgust, sadness and surprise. #ClimateJustice conveys the most positive sentiments, followed by #ThisIsZeroHour and the #WeDontHaveTime. In all the evaluations, a considerable number of people express fear in the climate action and consequences. Thus, climate change stakeholders and policy makers should consider the sentiments and emotions expressed by people and incorporate such outcomes in their various programmes toward addressing the climate change challenges especially as it affects the ecosystem

Examining the Impact of Emojis on Disaster Communication: A Perspective from the Uncertainty Reduction Theory

Communication is a purposeful process, especially during disasters, when emergency management officials and citizen journalists attempt to disseminate relevant information to as many affected people as possible. X (previously Twitter), a popular computer-mediated communication (CMC) platform, has become an essential resource for disaster information given its ability to facilitate real-time communication. Past studies on disasters have mainly concentrated on the verbal-linguistic conventions of words and hashtags as the means to convey disaster-related information. Little attention has been given to non-verbal linguistic cues, such as emojis. In this study, we investigate the use of emojis in disaster communication on X by using uncertainty reduction theory as the theoretical framework. We measured information uncertainty in individual tweets and assessed whether information conveyed in external URLs mitigated such uncertainty. We also examined how emojis affect information uncertainty and information dissemination. The statistical results from analyzing tweets related to the 2018 California Camp Fire disaster show that information uncertainty has a negative impact on information dissemination, and the negative impact was amplified when emojis depicted items and objects instead of facial expressions. Conversely, external URLs reduced the negative impact. This study sheds light on the influence of emojis on the dissemination of disaster information on X and provides insights for both academia and emergency management practitioners in using CMC platforms

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 social media services analysis

The growing adoption of social media services such as YouTube, Facebook, and Twitter have created opportunities for information dissemination practise that has not existed before. This practice improves situational awareness and eases dissemination of information. However, the major challenge is to efficiently extract relevant information from the large volumes of noises. It makes the social data analysis task is extremely labour intensive and time-consuming. To overcome these challenges, we propose a service-based approach to illustrate and analyse social media services. First, we assign social media as a service, called social media service. We also identify its functional and non- functional features. Secondly, we plan experiments on real- world datasets for a variety of topics. We analytically evaluate the functional and non-functional features of social media services. The analysis has depicted that classifying functional features and identifying non-functional features such as relatedness, preferences, and engagement of social media services is important regarding analysing social sensors data. This work demonstrates the competence of social media services regarding the Social Media Service- Based Analysis Model. The identified functional and non- functional features in this work help data analyst to understand and comprehend the nature of social media services particularly in the subject of Syria war, dengue outbreak and natural disaster. Furthermore, we have provided future research issues from where our work has ended

Human dynamics in the age of big data: a theory-data-driven approach

The revolution of information and communication technology (ICT) in the past two decades have transformed the world and people’s lives with the ways that knowledge is produced. With the advancements in location-aware technologies, a large volume of data so-called “big data” is now available through various sources to explore the world. This dissertation examines the potential use of such data in understanding human dynamics by focusing on both theory- and data-driven approaches. Specifically, human dynamics represented by communication and activities is linked to geographic concepts of space and place through social media data to set a research platform for effective use of social media as an information system. Three case studies covering these conceptual linkages are presented to (1) identify communication patterns on social media; (2) identify spatial patterns of activities in urban areas and detect events; and (3) explore urban mobility patterns. The first case study examines the use of and communication dynamics on Twitter during Hurricane Sandy utilizing survey and data analytics techniques. Twitter was identified as a valuable source of disaster-related information. Additionally, the results shed lights on the most significant information that can be derived from Twitter during disasters and the need for establishing bi-directional communications during such events to achieve an effective communication. The second case study examines the potential of Twitter in identifying activities and events and exploring movements during Hurricane Sandy utilizing both time-geographic information and qualitative social media text data. The study provides insights for enhancing situational awareness during natural disasters. The third case study examines the potential of Twitter in modeling commuting trip distribution in New York City. By integrating both traditional and social media data and utilizing machine learning techniques, the study identified Twitter as a valuable source for transportation modeling. Despite the limitations of social media such as the accuracy issue, there is tremendous opportunity for geographers to enrich their understanding of human dynamics in the world. However, we will need new research frameworks, which integrate geographic concepts with information systems theories to theorize the process. Furthermore, integrating various data sources is the key to future research and will need new computational approaches. Addressing these computational challenges, therefore, will be a crucial step to extend the frontier of big data knowledge from a geographic perspective. KEYWORDS: Big data, social media, Twitter, human dynamics, VGI, natural disasters, Hurricane Sandy, transportation modeling, machine learning, situational awareness, NYC, GI

Giving meaning to tweets in emergency situations: a semantic approach for filtering and visualizing social data

In this paper, we propose a semantic approach for monitoring information publishedon social networks about a specific event. In the era of Big Data, when an emergencyoccurs information posted on social networks becomes more and more helpful foremergency operators. As direct witnesses of the situation, people share photos, videosor text messages about events that call their attention. In the emergency operationcenter, these data can be collected and integrated within the management processto improve the overall understanding of the situation and in particular of the citizenreactions. To support the tracking and analyzing of social network activities, there arealready monitoring tools that combine visualization techniques with geographicalmaps. However, tweets are written from the perspective of citizens and the informationthey provide might be inaccurate, irrelevant or false. Our approach tries to dealwith data relevance proposing an innovative ontology-based method for filteringtweets and extracting meaningful topics depending on their semantic content. In thisway data become relevant for the operators to make decisions. Two real cases used totest its applicability showed that different visualization techniques might be neededto support situation awareness. This ontology-based approach can be generalizedfor analyzing the information flow about other domains of application changing theunderlying knowledge base.This work is supported by the project emerCien grant funded by the Spanish Ministry of Economy and Competitivity (TIN2012-09687)

SOCIAL MEDIA FOOTPRINTS OF PUBLIC PERCEPTION ON ENERGY ISSUES IN THE CONTERMINOUS UNITED STATES

Energy has been at the top of the national and global political agenda along with other concomitant challenges, such as poverty, disaster and climate change. Social perception on various energy issues, such as its availability, development and consumption deeply affect our energy future. This type of information is traditionally collected through structured energy surveys. However, these surveys are often subject to formidable costs and intensive labor, as well as a lack of temporal dimensions. Social media can provide a more cost-effective solution to collect massive amount of data on public opinions in a timely manner that may complement the survey. The purpose of this study is to use machine learning algorithms and social media conversations to characterize the spatiotemporal topics and social perception on different energy in terms of spatial and temporal dimensions. Text analysis algorithms, such as sentiment analysis and topic analysis, were employed to offer insights into the public attitudes and those prominent issues related to energy. The results show that the energy related public perceptions exhibited spatiotemporal dynamics. The study is expected to help inform decision making, formulate national energy policies, and update entrepreneurial energy development decisions