Focusing post-disaster research methodology: reflecting on 50 years of post-disaster research

The 50th anniversary of cyclone Althea at Christmastime this year, 2021, prompts a reflection of a corresponding 50 years of post-disaster research by the Centre for Disaster Studies at James Cook University. Importantly, this reflection is on what is achieved through rapid-appraisal studies immediately following a disaster. This paper builds on earlier research into the methods and types of post-disaster surveys; taking into account new technology and the emergent issue of climate change. The paper identifies general findings and issues that have been uncovered through post-disaster surveys. What is seen is a continuity of the effects of disasters across decades and across events. Thus, it is important to interview people in affected communities for debriefing and also to enhance communication, education and awareness. Survey methods across a range of disasters during the last 2 decades are reviewed to identify research and survey approaches. The methods and approaches of post-disaster surveys should be driven by community needs and characteristics and surveys must propose focused research questions and purpose to be effective and contribute to better practice

Communication Patterns Government Prevents Spread of COVID-19 Hoax News on Social Media

he spread of fake news on social media with the theme of COVID-19 is on the rise and knows no bounds. Various title taglines, contents, and narrations about COVID-19, Masks, and Vaccine are rapidly influencing individuals to be frightened to participate in activities. The goal of this research is to expand on the evaluation of long-term intelligent communication patterns in order to predict the propagation of COVID-19 false news on social media. This study was conducted primarily to investigate the factors influencing public opinion regarding the spread of COVID-19, victim cases, immunizations, and origins, as well as the government's response in West Sumatra Province. Data were gathered through in-depth interviews and direct observation, as well as in-depth interviews with six different sources and the distribution of 120 questionnaires. 112 questionnaires (93.3%) were collected and analyzed using the Structural Equation Modeling Partial Least Squares (SEM) technique in SmartPLS 3.0. The findings revealed a significant relationship between public opinion and the government's response to COVID-19 pandemic issues on social media. The findings indicate that a robust response to socialization, education, and public monitoring is required for the correct and intelligent use of social media. Digital literacy and information filtering must be enhanced, and perpetrators of disseminating COVID-19 false information must face strong and concrete penalties. Strict penalties and structured appeals will gradually have an impact on the media, which is not in charge of conveying COVID-19 information to the publi

A deep multi-modal neural network for informative Twitter content classification during emergencies

YesPeople start posting tweets containing texts, images, and videos as soon as a disaster hits an area. The analysis of these disaster-related tweet texts, images, and videos can help humanitarian response organizations in better decision-making and prioritizing their tasks. Finding the informative contents which can help in decision making out of the massive volume of Twitter content is a difficult task and require a system to filter out the informative contents. In this paper, we present a multi-modal approach to identify disaster-related informative content from the Twitter streams using text and images together. Our approach is based on long-short-term-memory (LSTM) and VGG-16 networks that show significant improvement in the performance, as evident from the validation result on seven different disaster-related datasets. The range of F1-score varied from 0.74 to 0.93 when tweet texts and images used together, whereas, in the case of only tweet text, it varies from 0.61 to 0.92. From this result, it is evident that the proposed multi-modal system is performing significantly well in identifying disaster-related informative social media contents

How dramatic events can affect emotionality in social posting: The impact of covid-19 on reddit

The COVID-19 outbreak impacted almost all the aspects of ordinary life. In this context, social networks quickly started playing the role of a sounding board for the content produced by people. Studying how dramatic events affect the way people interact with each other and react to poorly known situations is recognized as a relevant research task. Since automatically identifying country-based COVID-19 social posts on generalized social networks, like Twitter and Facebook, is a difficult task, in this work we concentrate on Reddit megathreads, which provide a unique opportunity to study focused reactions of people by both topic and country. We analyze specific reactions and we compare them with a “normal” period, not affected by the pandemic; in particular, we consider structural variations in social posting behavior, emotional reactions under the Plutchik model of basic emotions, and emotional reactions under unconventional emotions, such as skepticism, particularly relevant in the COVID-19 context

Comparison of different machine learning techniques on location extraction by utilizing geo-tagged tweets: A case study

In emergencies, Twitter is an important platform to get situational awareness simultaneously. Therefore, information about Twitter users’ location is a fundamental aspect to understand the disaster effects. But location extraction is a challenging task. Most of the Twitter users do not share their locations in their tweets. In that respect, there are different methods proposed for location extraction which cover different fields such as statistics, machine learning, etc. This study is a sample study that utilizes geo-tagged tweets to demonstrate the importance of the location in disaster management by taking three cases into consideration. In our study, tweets are obtained by utilizing the “earthquake” keyword to determine the location of Twitter users. Tweets are evaluated by utilizing the Latent Dirichlet Allocation (LDA) topic model and sentiment analysis through machine learning classification algorithms including the Multinomial and Gaussian Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Random Forest, Extra Trees, Neural Network, k Nearest Neighbor (kNN), Stochastic Gradient Descent (SGD), and Adaptive Boosting (AdaBoost) classifications. Therefore, 10 different machine learning algorithms are applied in our study by utilizing sentiment analysis based on location-specific disaster-related tweets by aiming fast and correct response in a disaster situation. In addition, the effectiveness of each algorithm is evaluated in order to gather the right machine learning algorithm. Moreover, topic extraction via LDA is provided to comprehend the situation after a disaster. The gathered results from the application of three cases indicate that Multinomial Naïve Bayes and Extra Trees machine learning algorithms give the best results with an F-measure value over 80%. The study aims to provide a quick response to earthquakes by applying the aforementioned techniques. © 2020 Elsevier Lt

Temporal, spatial, and socioeconomic dynamics in social media thematic emphases during Typhoon Mangkhut

Disaster-related social media data often consist of several themes, and each theme allows people to understand and communicate from a certain perspective. It is necessary to take into consideration the dynamics of thematic emphases on social media in order to understand the nature of such data and to use them appropriately. This paper proposes a framework to analyze the temporal, spatial, and socioeconomic disparities in thematic emphases on social media during Typhoon Mangkhut. First, the themes were identified through a latent Dirichlet allocation model during Typhoon Mangkhut. Then, we adopted a quantitative method of indexing the themes to represent the dynamics of the thematic emphases. Spearman correlation analyses between the index and eight socioeconomic variables were conducted to identify the socioeconomic disparities in thematic emphases. The main research findings are revealing. From the perspective of time evolution, Theme 1 (general response) and Theme 2 (urban transportation) hold the principal position throughout the disaster. In the early hours of the disaster, Theme 3 (typhoon status and impact) was the most popular theme, but its popularity fell sharply soon after. From the perspective of spatial distribution, people in severely affected areas were more concerned about urban transportation (Theme 2), while people in moderately affected areas were more concerned about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4). The results of the correlation analyses show that there are differences in thematic emphases across disparate socioeconomic groups. Women preferred to post about typhoon status and impact (Theme 3) and animals and humorous news (Theme 4), while people with higher income paid less attention to these two themes during Typhoon Mangkhut. These findings can help government agencies and other stakeholders address public needs effectively and accurately in disaster responses

What roles do social media play in hurricane Ian, before, during and after the event

In recent years, natural disasters like wildfires, tsunamis, and floods have surged in both severity and frequency, causing widespread harm, including physical damage, loss of life, economic turmoil, and societal unrest. Among these disasters, hurricanes, defined by wind speeds surpassing 74 mph, pose a persistent threat, bringing hazards such as heavy rainfall and inland flooding. Hurricane Ian, one of the most significant in recent U.S. history, formed on September 23rd, hit Florida on September 28th, and dissipated on October 2nd, leaving widespread devastation. In the realm of disaster management, Location-Based Social Media (LBSM) has emerged as a crucial tool, aiding in early warnings, damage assessment, rescue coordination, and recovery evaluation. This thesis focuses on the analysis of English and Spanish tweets related to Hurricane Ian, covering the period from its formation to 50 days after its dissipation. The tweet datasets were divided into two categories: all tweets and the top 1% most shared tweets. Employing the Latent Dirichlet Allocation (LDA) model, the study unveiled prevalent themes within the tweets over different timeframes. Additionally, sentiment analysis was conducted on both English and Spanish tweet datasets, using the Valence Aware Dictionary and sEntimentReasoner (VADER) model for English tweets and Vader-multi for Spanish tweets. This aimed to capture the evolving sentiments of individuals and their emotional responses to various topics. The findings reveal Twitter's effectiveness as an early warning system and a valuable tool for risk assessment and recovery. Leading up to the hurricane's landfall, discussions mainly revolved around weather and disaster-related topics. During and after the hurricane, the focus shifted to disaster-related and situational topics. Sentiment analysis indicated a growing negativity as the storm approached, followed by a gradual return to less negative sentiments after the hurricane passed. This thesis emphasizes the significance of social media platforms as essential resources for rapid decision-making during crises, particularly when quick responses are imperative

Natural Disaster Preparation by Licensed Clinical Social Workers Providing Mental Health Interventions to Vulnerable Populations

Extreme weather events can devastate the social, physical, and emotional health of individuals and communities. Vulnerable populations are at risk for loss during natural disasters, and poverty exacerbates the impact of traumatic stress and has long-term impacts on physical and mental health. Disaster preparedness planning is critical for assisting individuals who rely on mental health services provided by clinical social workers. Limited evidence-based research exists on how licensed clinical social workers (LCSWs) prepare to provide mental health services before an extreme weather event. A qualitative action research study guided by Ajzen’s theory of planned behavior was conducted to explore the disaster preparedness experiences of eight LCSWs in human services agencies in southwest Florida. Data were collected through semistructured individual interviews. Coding and thematic analysis of the data were employed to answer the research questions. Implementing thematic analysis results from this study yielded four themes: (a) practice setting influenced the implementation of the disaster preparedness plan, (b) planned to address clients’ overall well-being, (c) anticipated barriers and considered how to ensure continuity of services, and (d) collaborated with colleagues. These themes capture how LCSWs prepared to provide services during a natural disaster and how the National Association of Social Workers Code of Ethics influenced their planning. The findings of this study could have implications for positive social change by fostering the development of social work interventions that reduce traumatic stress for vulnerable populations who are economically and geographically at risk during natural disasters