Big data analytics for disaster response and recovery through sentiment analysis

Evaluation of adjective and adverb types for effective Twitter sentiment classification

Twitter, the largest microblogging platform, has reported more than 330 million active users in recent years. Many users express their sentiments about politics, sports, products, personalities, etc. Sentiment analysis has emerged as a specialized branch of machine learning in which tweets are binary-classified to provide sentimental insights. A major step in sentiment classification is feature selection, which primarily revolves around parts of speech (POS). Few techniques merely focused on single features such as adjectives, adverbs, and verbs, while other techniques examined types of these features, such as comparative adjectives, superlative adjectives, or general adverbs. Furthermore, POS as linguistic entities have also been studied and extensively classified by researchers, such as CLAWS-C7. For sentiment analysis, none of the studies conceptualized all possible POS features under similar conditions to draw firm conclusion. This research is centered on the following objectives: 1) examining the impact of various types of adjectives and adverbs that have not been previously explored for sentiment classification; 2) analyzing potential combinations of adjectives and adverbs types 3) conducting a comparison with a benchmark dataset for better classification accuracy. To assess the concept, a renowned human annotated dataset of tweets is investigated. Results showed that classification accuracy for adjectives is improved up to 83% based on the general superlative adjective whereas for adverbs, comparative general adverb also depicted significant accuracy improvement. Their combination with general adjectives and general adverbs also played a substantial role. The unexplored potential of adjectives and adverb types proved better in accuracy against state-of-the-art probabilistic model. In comparison to lexicon-based model, proposed research model overruled the dependency of lexicon-based dictionary where each term first needs to be matched for semantic orientation. The evident outcomes also help in time reduction aspect where huge volume of data need to be processed swiftly. This noteworthy contribution brought up significant knowledge and direction for domain experts. In the future, the proposed technique will be explored for other types of textual data across different domains.

A spatial-temporal graph deep learning model for urban flood nowcasting leveraging heterogeneous community features

Flood nowcasting refers to near-future prediction of flood status as an extreme weather event unfolds to enhance situational awareness. The objective of this study was to adopt and test a novel structured deep-learning model for urban flood nowcasting by integrating physics-based and human-sensed features. We present a new computational modeling framework including an attention-based spatial-temporal graph convolution network (ASTGCN) model and different streams of data that are collected in real-time, preprocessed, and fed into the model to consider spatial and temporal information and dependencies that improve flood nowcasting. The novelty of the computational modeling framework is threefold: first, the model is capable of considering spatial and temporal dependencies in inundation propagation thanks to the spatial and temporal graph convolutional modules; second, it enables capturing the influence of heterogeneous temporal data streams that can signal flooding status, including physics-based features (e.g., rainfall intensity and water elevation) and human-sensed data (e.g., residents' flood reports and fluctuations of human activity) on flood nowcasting. Third, its attention mechanism enables the model to direct its focus to the most influential features that vary dynamically and influence the flood nowcasting. We show the application of the modeling framework in the context of Harris County, Texas, as the study area and 2017 Hurricane Harvey as the flood event. Three categories of features are used for nowcasting the extent of flood inundation in different census tracts: (i) static features that capture spatial characteristics of various locations and influence their flood status similarity, (ii) physics-based dynamic features that capture changes in hydrodynamic variables, and (iii) heterogeneous human-sensed dynamic features that capture various aspects of residents' activities that can provide information regarding flood status. Results indicate that the ASTGCN model provides superior performance for nowcasting of urban flood inundation at the census-tract level, with precision 0.808 and recall 0.891, which shows the model performs better compared with other state-of-the-art models. Moreover, ASTGCN model performance improves when heterogeneous dynamic features are added into the model that solely relies on physics-based features, which demonstrates the promise of using heterogenous human-sensed data for flood nowcasting. Given the results of the comparisons of the models, the proposed modeling framework has the potential to be more investigated when more data of historical events are available in order to develop a predictive tool to provide community responders with an enhanced prediction of the flood inundation during urban flood.

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions.

Beyond Sentiment Analysis: A Review of Recent Trends in Text Based Sentiment Analysis and Emotion Detection

Sentiment Analysis is probably one of the best-known area in text mining. However, in recent years, as big data rose in popularity more areas of text classification are being explored. Perhaps the next task to catch on is emotion detection, the task of identifying emotions. This is because emotions are the finer grained information which could be extracted from opinions. So besides writer sentiments, writer emotion is also a valuable data. Emotion detection can be done using text, facial expressions, verbal communications and brain waves; however, the focus of this review is on text-based sentiment analysis and emotion detection. The internet has provided an avenue for the public to express their opinions easily. These expressions not only contain positive or negative sentiments, it contains emotions as well. These emotions can help in social behaviour analysis, decision and policy makings for companies and the country. Emotion detection can further support other tasks such as opinion mining and early depression detection. This review provides a comprehensive analysis of the shift in recent trends from text sentiment analysis to emotion detection and the challenges in these tasks. We summarize some of the recent works in the last five years and look at the methods they used. We also look at the models of emotion classes that are generally referenced. The trend of text-based emotion detection has shifted from the early keyword-based comparisons to machine learning and deep learning algorithms that provide more flexibility to the task and better performance.

A systematic literature review on the use of big data analytics in humanitarian and disaster operations

At the start of this review, 168 million individuals required humanitarian assistance, at the conclusion of the research, the number had risen to 235 million. Humanitarian aid is critical not just for dealing with a pandemic that occurs once every century, but more for assisting amid civil conflicts, surging natural disasters, as well as other kinds of emergencies. Technology's dependability to support humanitarian and disaster operations has never been more pertinent and significant than it is right now. The ever-increasing volume of data, as well as innovations in the field of data analytics, present an incentive for the humanitarian sector. Given that the interaction between big data and humanitarian and disaster operations is crucial in the coming days, this systematic literature review offers a comprehensive overview of big data analytics in a humanitarian and disaster setting. In addition to presenting the descriptive aspects of the literature reviewed, the results explain review of existent reviews, the current state of research by disaster categories, disaster phases, disaster locations, and the big data sources used. A framework is also created to understand why researchers employ various big data sources in different crisis situations. The study, in particular, uncovered a considerable research disparity in the disaster group, disaster phase, and disaster regions, emphasising how the focus is on reactionary interventions rather than preventative approaches. These measures will merely compound the crisis, and so is the reality in many COVID-19-affected countries. Implications for practice and policy-making are also discussed.

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage

Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.

Public Opinions on COVID-19 Vaccines—A Spatiotemporal Perspective on Races and Topics Using a Bayesian-Based Method

The COVID-19 pandemic has been sweeping across the United States of America since early 2020. The whole world was waiting for vaccination to end this pandemic. Since the approval of the first vaccine by the U.S. CDC on 9 November 2020, nearly 67.5% of the US population have been fully vaccinated by 10 July 2022. While quite successful in controlling the spreading of COVID-19, there were voices against vaccines. Therefore, this research utilizes geo-tweets and Bayesian-based method to investigate public opinions towards vaccines based on (1) the spatiotemporal changes in public engagement and public sentiment; (2) how the public engagement and sentiment react to different vaccine-related topics; (3) how various races behave differently. We connected the phenomenon observed to real-time and historical events. We found that in general the public is positive towards COVID-19 vaccines. Public sentiment positivity went up as more people were vaccinated. Public sentiment on specific topics varied in different periods. African Americans’ sentiment toward vaccines was relatively lower than other races.

Tweets on a horror movie: An investigation into relationships between sentiment strength, cognitive language and tweet virality

This article studies how sentiment strength and cognitive language may influence the levels of tweet virality. A total of 11,381 tweets about a horror movie (‘Mother!’) were collected. Based on the definitions of two independent variables: sentiment strength and cognitive language use, and the dependent variable: tweet virality, the data descriptive statistics and analysis of variance (ANOVA) analysis were applied to reveal the relationships between tweet virality and sentiment and cognitive factors. The results indicate that a high tweet virality is associated with either a lower level of sentiment strength or/and a higher level of cognitive language use by a statistically significant margin. This finding is more evident for negative tweets. The study findings help improve the understanding about sentimental and cognitive factors impacting tweet virality and guide the movie industry to improve marketing movie content to achieve high virality on social media. The conclusions can also be applied to other industries, government agencies, organisations and individuals who intend to quickly disseminate specific information on social media platforms.

Using data mining technology to analyse the spatiotemporal public opinion of COVID-19 vaccine on social media

Purpose

The deployment of vaccines is the primary task in curbing the COVID-19 pandemic. The purpose of this paper is to understand the public’s opinions on vaccines and then design effective interventions to promote vaccination coverage.

Design/methodology/approach

This paper proposes a research framework based on the spatiotemporal perspective to analyse the public opinion evolution towards COVID-19 vaccine in China. The framework first obtains data through crawler tools. Then, with the help of data mining technologies, such as emotion computing and topic extraction, the evolution characteristics of discussion volume, emotions and topics are explored from spatiotemporal perspectives.

Findings

In the temporal perspective, the public emotion declines in the later stage, but overall emotion performance is positive and stabilizing. This decline in emotion is mainly associated with ambiguous information about the COVID-19 vaccine. The research progress of vaccines and the schedule of vaccination have driven the evolution of public discussion topics. In the spatial perspective, the public emotion tends to be positive in 31 regions, whereas local emotion increases and decreases in different stages. The dissemination of distinctive information and the local epidemic prevention and control status may be potential drivers of topic evolution in local regions.

Originality/value

The analysis results of media information can assist decision-makers to accurately grasp the subjective thoughts and emotional expressions of the public in terms of spatiotemporal perspective and provide decision support for macro-control response strategies and risk communication.

Data-Driven Community Flood Resilience Prediction

Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies.

Organizational Adoption of Sentiment Analytics in Social Media Networks

Enterprise adoption and application of sentiment analytics (SA) has recently attracted significant interest from both academia and industry, as it offers exciting opportunities to generate competitive intelligence on consumer attitudes and opinions. Yet, there is limited understanding of the factors underlying successful and widespread adoption of SA in enterprises. This study presents a systematic literature review (SLR) to analyze and summarize previous research on corporate adoption of SA in social media. The SLR examines the results of 83 studies and focuses on tasks, techniques, application domains, and factors that influence enterprise adoption of SA. The findings provide insights into (i) key factors influencing SA adoption, (ii) research trends and paradigms across disciplines, and (iii) potential areas for future research on enterprise adoption of SA. These findings recommend actionable future research agendas for scholars and inform practitioners' understanding of the decision-making processes involved in enterprise adoption of SA in social media.

Advanced Technologies for Offering Situational Intelligence in Flood Warning and Response Systems: A Literature Review

Deaths and property damage from floods have increased drastically in the past two decades due to various reasons such as increased populations, unplanned developments, and climate change. Such losses from floods can be reduced by issuing timely early warnings and through effective response mechanisms based on situational intelligence during emerging flood situations. This paper presents the outcome of a literature review that was conducted to identify the types and sources of the intelligence required for flood warning and response processes as well as the technology solutions that can be used for offering such intelligence. Twenty-seven different types of intelligence are presented together with the technologies that can be used to extract such intelligence. Furthermore, a conceptual architecture that illustrates how relevant technology solutions can be used to extract intelligence at various stages of a flood cycle for decision-making in issuing early warnings and planning responses is presented.

Concerned or Apathetic? Using Social Media Platform (Twitter) to Gauge the Public Awareness about Wildlife Conservation: A Case Study of the Illegal Rhino Trade

The illegal wildlife trade is resulting in worldwide biodiversity loss and species’ extinction. It should be exposed so that the problems of conservation caused by it can be highlighted and resolutions can be found. Social media is an effective method of information dissemination, providing a real-time, low-cost, and convenient platform for the public to release opinions on wildlife protection. This paper aims to explore the usage of social media in understanding public opinions toward conservation events, and illegal rhino trade is an example. This paper provides a framework for analyzing rhino protection issues by using Twitter. A total of 83,479 useful tweets and 33,336 pieces of users’ information were finally restored in our database after filtering out irrelevant tweets. With 2422 records of trade cases, this study builds up a rhino trade network based on social media data. The research shows important findings: (1) Tweeting behaviors are somewhat affected by the information of traditional mass media. (2) In general, countries and regions with strong negative sentiment tend to have high volume of rhino trade cases, but not all. (3) Social celebrities’ participation in activities arouses wide public concern, but the influence does not last for more than a month. NGOs, GOs, media, and individual enterprises are dominant in the dissemination of information about rhino trade. This study contributes in the following ways: First, this paper conducts research on public opinions toward wildlife conservation using natural language processing technique. Second, this paper offers advice to governments and conservationist organizations, helping them utilize social media for protecting wildlife.

How Advanced Technological Approaches Are Reshaping Sustainable Social Media Crisis Management and Communication: A Systematic Review

The end goal of technological advancement used in crisis response and recovery is to prevent, reduce or mitigate the impact of a crisis, thereby enhancing sustainable recovery. Advanced technological approaches such as social media, machine learning (ML), social network analysis (SNA), and big data are vital to a sustainable crisis management decisions and communication. This study selects 28 articles via a systematic process that focuses on ML, SNA, and related technological tools to understand how these tools are shaping crisis management and decision making. The analysis shows the significance of these tools in advancing sustainable crisis management to support decision making, information management, communication, collaboration and cooperation, location-based services, community resilience, situational awareness, and social position. Moreover, the findings noted that managing diverse outreach information and communication is increasingly essential. In addition, the study indicates why big data and language, cross-platform support, and dataset lacking are emerging concerns for sustainable crisis management. Finally, the study contributes to how advanced technological solutions effectively affect crisis response, communication, decision making, and overall crisis management.

A Multimodal Data Analysis Approach to Social Media during Natural Disasters

During natural disasters, social media can provide real time or rapid disaster, perception information to help government managers carry out disaster response efforts efficiently. Therefore, it is of great significance to mine social media information accurately. In contrast to previous studies, this study proposes a multimodal data classification model for mining social media information. Using the model, the study employs Late Dirichlet Allocation (LDA) to identify subject information from multimodal data, then, the multimodal data is analyzed by bidirectional encoder representation from transformers (Bert) and visual geometry group 16 (Vgg-16). Text and image data are classified separately, resulting in real mining of topic information during disasters. This study uses Weibo data during the 2021 Henan heavy storm as the research object. Comparing the data with previous experiment results, this study proposes a model that can classify natural disaster topics more accurately. The accuracy of this study is 0.93. Compared with a topic-based event classification model KGE-MMSLDA, the accuracy of this study is improved by 12%. This study results in a real-time understanding of different themed natural disasters to help make informed decisions.

Social Media User Behavior and Emotions during Crisis Events

The wide availability of smart mobile devices and Web 2.0 services has allowed people to easily access news, spread information, and express their opinions and emotions using various social media platforms. However, because of the ease of joining these sites, people also use them to spread rumors and vent their emotions, with the social platforms often playing a facilitation role. This paper collected more than 190,000 messages published on the Chinese Sina-Weibo platform to examine social media user behaviors and emotions during an emergency, with a particular research focus on the “Dr. Li Wenliang” reports associated with the COVID-19 epidemic in China. The verified accounts were found to have the strongest interactions with users, and the sentiment analysis revealed that the news from government agencies had a positive user effect and the national media and trusted experts were more favored by users in an emergency. This research provides a new perspective on trust and the use of social media platforms in crises, and therefore offers some guidance to government agencies.

An Emergency Event Detection Ensemble Model Based on Big Data

Emergency events arise when a serious, unexpected, and often dangerous threat affects normal life. Hence, knowing what is occurring during and after emergency events is critical to mitigate the effect of the incident on humans’ life, on the environment and our infrastructures, as well as the inherent financial consequences. Social network utilization in emergency event detection models can play an important role as information is shared and users’ status is updated once an emergency event occurs. Besides, big data proved its significance as a tool to assist and alleviate emergency events by processing an enormous amount of data over a short time interval. This paper shows that it is necessary to have an appropriate emergency event detection ensemble model (EEDEM) to respond quickly once such unfortunate events occur. Furthermore, it integrates Snapchat maps to propose a novel method to pinpoint the exact location of an emergency event. Moreover, merging social networks and big data can accelerate the emergency event detection system: social network data, such as those from Twitter and Snapchat, allow us to manage, monitor, analyze and detect emergency events. The main objective of this paper is to propose a novel and efficient big data-based EEDEM to pinpoint the exact location of emergency events by employing the collected data from social networks, such as “Twitter” and “Snapchat”, while integrating big data (BD) and machine learning (ML). Furthermore, this paper evaluates the performance of five ML base models and the proposed ensemble approach to detect emergency events. Results show that the proposed ensemble approach achieved a very high accuracy of 99.87% which outperform the other base models. Moreover, the proposed base models yields a high level of accuracy: 99.72%, 99.70% for LSTM and decision tree, respectively, with an acceptable training time.

#StrongTogether? Qualitative Sentiment Analysis of Social Media Reactions to Disaster Volunteering during a Forest Fire in Finland

The transformation of disaster volunteering has been highlighted in academic literature. This study examined that transformation via a big data approach. The context for the study was provided by a forest fire in Finland, which sparked a debate on volunteering. The data (806 social media messages) were analyzed using qualitative sentiment analysis to (1) identify the sentiments relating to a variety of volunteers and (2) understand the context of and tensions behind those sentiments. The data suggested that the prevailing view of disaster volunteering is a rather traditional one, while the observations on the transformation remain largely latent. The positive sentiments reflected a view of the co-production of extinguishing forest fires as an activity of formal governmental and nonprofit emergency management organizations and volunteers from expanding and extending organizations. Unaffiliated volunteers were seen as extra pairs of hands that could be invited to help in an organized way and with limited tasks, only if required. Sentiments with a more negative tone raised concerns about having sufficient numbers of affiliated volunteers in the future and the rhetorical level of appreciation of them. The data revealed a dichotomous relationship between “professionals” and “amateurs” and the politicization of the debate between different actor groups.

Accuracy of a pre-trained sentiment analysis (SA) classification model on tweets related to emergency response and early recovery assessment: the case of 2019 Albanian earthquake

Traditionally, earthquake impact assessments have been made via fieldwork by non-governmental organisations (NGO's) sponsored data collection; however, this approach is time-consuming, expensive and often limited. Recently, social media (SM) has become a valuable tool for quickly collecting large amounts of first-hand data after a disaster and shows great potential for decision-making. Nevertheless, extracting meaningful information from SM is an ongoing area of research. This paper tests the accuracy of the pre-trained sentiment analysis (SA) model developed by the no-code machine learning platform MonkeyLearn using the text data related to the emergency response and early recovery phase of the three major earthquakes that struck Albania on the 26th November 2019. These events caused 51 deaths, 3000 injuries and extensive damage. We obtained 695 tweets with the hashtags: #Albania #AlbanianEarthquake, and #albanianearthquake from the 26th November 2019 to the 3rd February 2020. We used these data to test the accuracy of the pre-trained SA classification model developed by MonkeyLearn to identify polarity in text data. This test explores the feasibility to automate the classification process to extract meaningful information from text data from SM in real-time in the future. We tested the no-code machine learning platform's performance using a confusion matrix. We obtained an overall accuracy (ACC) of 63% and a misclassification rate of 37%. We conclude that the ACC of the unsupervised classification is sufficient for a preliminary assessment, but further research is needed to determine if the accuracy is improved by customising the training model of the machine learning platform.

Outlier knowledge management for extreme public health events: Understanding public opinions about COVID-19 based on microblog data

Based on complex adaptive system theory and information theory for investigating heterogeneous situations, this paper develops an outlier knowledge management framework based on three aspects-dimension, object, and situation-for dealing with extreme public health events. In the context of the COVID-19 pandemic, we apply advanced natural language processing (NLP) technology to conduct data mining and feature extraction on the microblog data from the Wuhan area and the imported case province (Henan Province) during the high and median operating periods of the epidemic. Our experiment indicates that the semantic and sentiment vocabulary of words, the sentiment curve, and the portrait of patients seeking help were all heterogeneous in the context of COVID-19. We extract and acquire the outlier knowledge of COVID-19 and incorporate it into the outlier knowledge base of extreme public health events for knowledge sharing and transformation. The knowledge base serves as a think tank for public opinion guidance and platform suggestions for dealing with extreme public health events. This paper provides novel ideas and methods for outlier knowledge management in healthcare contexts.

User Privacy, Surveillance and Public Health during COVID-19 - An Examination of Twitterverse

Online users frequently rely on social networking platforms to transmit public concerns and raise awareness about societal issues. With many government organizations actively employing social media data in recent times, the need for processing public concerns on social media has become a critical topic of interest across academic scholars and practitioners. However, the growing volume of social media data makes it difficult to process all the issues under a single umbrella, causing to overlook the main topic of interest within communication technologies, such as privacy. For example, during the COVID-19 pandemic, arguments on privacy and health issues exploded on Twitter, with several threads centered on contact tracking, health data gathering, and its usage by government agencies. To address the challenges of rising data volumes and to understand the importance of privacy concerns, particularly among users seeking greater privacy protection during this pandemic, we conduct a focused empirical analysis of user tweets about privacy. In this two-part research, our first study reveals three macro privacy issues of discussion distilled from the Twitter corpus, subsequently subdivided into 12 user privacy categories. The second study builds on the findings of the first study, focusing on the primary difficulties highlighted in the macro privacy subjects-contact tracing and digital surveillance. Using a document clustering approach, we present implications for the focal privacy topics that policymakers, agencies, and governments should consider for offering better privacy protections and help the community rebuild.

Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: a case study of Nanchang, China

In the Internet age, emotions exist in cyberspace and geospatial space, and social media is the mapping from geospatial space to cyberspace. However, most previous studies pay less attention to the multidimensional and spatiotemporal characteristics of emotion. We obtained 211,526 Sina Weibo data with geographic locations and trained an emotion classification model by combining the Bidirectional Encoder Representation from Transformers (BERT) model and a convolutional neural network to calculate the emotional tendency of each Weibo. Then, the topic of the hot spots in Nanchang City was detected through a word shift graph, and the temporal and spatial change characteristics of the Weibo emotions were analyzed at the grid-scale. The results of our research show that Weibo's overall emotion tendencies are mainly positive. The spatial distribution of the urban emotions is extremely uneven, and the hot spots of a single emotion are mainly distributed around the city. In general, the intensity of the temporal and spatial changes in emotions in the cities is relatively high. Specifically, from day to night, the city exhibits a pattern of high in the east and low in the west. From working days to weekends, the model exhibits a low center and a four-week high. These results reveal the temporal and spatial distribution characteristics of the Weibo emotions in the city and provide auxiliary support for analyzing the happiness of residents in the city and guiding urban management and planning.

COVID-19 analytics: Towards the effect of vaccine brands through analyzing public sentiment of tweets

The COVID-19 outbreak has created effects on everyday life worldwide. Many research teams at major pharmaceutical companies and research institutes in various countries have been producing vaccines since the beginning of the outbreak. There is an impact of gender on vaccine responses, acceptance, and outcomes. Worldwide promotion of the COVID-19 vaccine additionally generates a huge amount of discussions on social media platforms about diverse factors of vaccines including protection and efficacy. Twitter is considered one of the most well-known social media platforms which have been widely used to share a public opinion on vaccine-related problems in the COVID-19 pandemic. However, there is a lack of research work to analyze the public perception of COVID-19 vaccines systematically from a gender perspective. In this paper, we perform an in-depth analysis of the coronavirus vaccine-related tweets to understand the people's sentiment towards various vaccine brands corresponding to the gender level. The proposed method focuses on the effect of COVID-19 vaccines on gender by taking into account descriptive, diagnostic, predictive, and prescriptive analytics on the Twitter dataset. We also conduct experiments with deep learning models to determine the sentiment polarities of tweets, which are positive, neutral, and negative. The results reveal that LSTM performs better compared to other models with an accuracy rate of 85.7%.

Social media analytics and reachability evaluation - #Diabetes

BACKGROUND AND AIMS

Diabetes as a lifestyle disorder could be effectively managed by creating awareness among people through social media. Understanding the content of Twitter messages will aid in strategizing health communication about diabetes to the community through Twitter. This study aimed to analyze the content, sentiment, and reachability of diabetes related tweets posted in India.

METHODS

Diabetes related messages from India were collected via Twitter's Application Programming Interface for April 2019. Themes and subthemes of tweet content were identified from randomly selected tweets. The tweets were coded as the source, themes, and subthemes manually. Sentiment analysis of the tweets was done by a lexicon-based approach. The reachability of tweets was assessed based on re-tweet and favorite counts.

RESULTS

Out of 1840 tweets, 57.28% were from organizations and 42.72% were from individuals. The largest proportion of tweet messages were informative (50.76%), followed by promotional tweets (21.52%). The largest proportion of tweets were positive (40.4%) followed by neutral (31.14%) tweets. Among the six major themes, the diabetes story had the highest reachability.

CONCLUSIONS

The outcome of this study would aid public health professionals in planning information dissemination and communication regarding diabetes on Twitter so that the right information reaches a wider population.

Use of Big Data in Disaster Recovery: An Integrative Literature Review

OBJECTIVE

Disasters of all varieties have been steadily increasing in frequency. Simultaneously, "big data" has seen explosive growth as a tool in business and private industries while opportunities for robust implementation in disaster management remain nascent. To more explicitly ascertain the current status of big data as applied to disaster recovery, we conducted an integrative literature review.

METHODS

Eleven databases were searched using iteratively developed keywords to target big data in a disaster recovery context. All studies were dual-screened by title and abstract followed by dual full-text review to determine if they met inclusion criteria. Articles were included if they focused on big data in a disaster recovery setting and were published in the English-language peer-reviewed literature.

RESULTS

After removing duplicates, 25,417 articles were originally identified. Following dual title/abstract review and full-text review, 18 studies were included in the final analysis. Among those, 44% were United States-based and 39% focused on hurricane recovery. Qualitative themes emerged surrounding geographic information systems (GIS), social media, and mental health.

CONCLUSIONS

Big data is an evolving tool for recovery from disasters. More research, particularly in real-time applied disaster recovery settings, is needed to further expand the knowledge base for future applications.

Information extraction using a mixed method analysis of social media data: A case study of the police shooting during the anti-Sterlite protests at Thoothukudi, India

During unexpected social events, information extracted from social media content posted by the people could play a crucial role in understanding the public opinion about the event. In this study, a mixed method procedure, which combines automated and human-based methods, is proposed to mine information from tweets to understand people's thoughts toward an unexpected turn of events. The proposed framework was applied on tweets posted regarding the police shooting to disperse protesters during the anti-Sterlite protests on May 22, 2018, at Thoothukudi in the Indian state of Tamil Nadu. The tweets were analyzed in two ways: (i) sentiment classification with automated computational methods and (ii) qualitatively examining the context of the expressed sentiments. In the case of anti-Sterlite protests, people expressed mixed emotions toward the protests for the closure of the Sterlite plant. A large negative sentiment toward the police shooting could be gleaned from the tweets. Analyzing tweets by the proposed method provides clear insights regarding the incident, which in turn will aid in planning an emergency response.

Rethinking the design of resilience and adaptation indicators supporting coastal communities

As resilience and adaptation considerations become mainstreamed into public policy, there is an overarching desire to measure and quantify metrics and indicators that seek to evaluate the efficiency, effectiveness, and justness associated with outcomes of such processes. While much research has sought to develop specific indicators that may serve as proxies for these considerations, less research has focused on those normative aspects of indicator design that support a variety of goals associated with the accuracy, reproducibility, proxy value and multi-stakeholder translation of indicators, among various other goals and values. This perspective article sets forth a range of potential considerations that may be useful for those who seek to design and develop novel resilience and adaptation indicators ("RAIs"). These considerations are explored through a range of hypothetical examples that may be applicable to coastal communities that seek to address the practical challenges facing the design, execution, management and modification of RAIs.

Social Media Behavior and Emotional Evolution during Emergency Events

Online social networks have recently become a vital source for emergency event news and the consequent venting of emotions. However, knowledge on what drives user emotion and behavioral responses to emergency event developments are still limited. Therefore, unlike previous studies that have only explored trending themes and public sentiment in social media, this study sought to develop a holistic framework to assess the impact of emergency developments on emotions and behavior by exploring the evolution of trending themes and public sentiments in social media posts as a focal event developed. By examining the event timelines and the associated hashtags on the popular Chinese social media site Sina-Weibo, the 2019 Wuxi viaduct collapse accident was taken as the research object and the event timeline and the Sina-Weibo tagging function focused on to analyze the behaviors and emotional changes in the social media users and elucidate the correlations. It can conclude that: (i) There were some social media rules being adhered to and that new focused news from the same event impacted user behavior and the popularity of previous thematic discussions. (ii) While the most critical function for users appeared to express their emotions, the user foci changed when recent focus news emerged. (iii) As the news of the collapse deepened, the change in user sentiment was found to be positively correlated with the information released by personal-authentication accounts. This research provides a new perspective on the extraction of information from social media platforms in emergencies and social-emotional transmission rules.

How to empower analytics capability to tackle emergency situations?

Purpose

Analytics thrives in navigating emergency situations. Emergency operations management needs to develop analytics empowerment capability (ANEC) to prepare for uncertainty, support continuity and tackle any disruptions. However, there is limited knowledge on ANEC and its effects on strategic emergency service agility (SESA) and emergency service adaptation (ESAD) in such contexts. Drawing on the dynamic capability (DC) theory, we address this research gap by developing an ANEC model. We also model the effects of ANEC on SESA and ESAD using SESA as a mediator. We also assess the moderating and quadratic effects of ANEC on two higher-order DCs (i.e. SESA and ESAD).

Design/methodology/approach

Drawing on the literature on big data, empowerment and DC, we develop and validate an ANEC model using data from 245 service systems managers in Australia. The study uses the partial least squares-based structural equation modelling (PLS-SEM) to prove the research model. The predictive power of the research model is validated through PLSpredict (k = 10) using a training sample (n = 220) and a holdout sample (n = 25).

Findings

The findings show that analytics climate, technological enablement, information access, knowledge and skills, training and development and decision-making ability are the significant components of ANEC. The findings confirm strategic emergency service agility as a significant partial mediator between ANEC and emergency service adaptation. The findings also discuss the moderating and quadratic effects of ANEC on outcome constructs. We discuss the implications of our findings for emergency situations with limitations and future research directions.

Originality/value

The findings show that building ANEC plays a fundamental role in developing strategic agility and service adaptation in emergency situations to prepare for disruptions, mitigate risks and continue operations.

Big Data and the United Nations Sustainable Development Goals (UN SDGs) at a Glance

The launch of the United Nations (UN) 17 Sustainable Development Goals (SDGs) in 2015 was a historic event, uniting countries around the world around the shared agenda of sustainable development with a more balanced relationship between human beings and the planet. The SDGs affect or impact almost all aspects of life, as indeed does the technological revolution, empowered by Big Data and their related technologies. It is inevitable that these two significant domains and their integration will play central roles in achieving the 2030 Agenda. This research aims to provide a comprehensive overview of how these domains are currently interacting, by illustrating the impact of Big Data on sustainable development in the context of each of the 17 UN SDGs.

Using community information for natural disaster alerts

Recently, the ceaseless rise in the global average temperature has led to extreme climates in which natural disasters, such as droughts, hurricanes, earthquakes and floods, are becoming increasingly serious. Recent research has found that social media typically reflects disasters earlier than official communication channels. In this study, the idea of collecting information on flood disasters caused during the periods of typhoons and heavy rains for a city from the plain text messages released by social media by means of a term frequency (TF) and sliding window approach is proposed. The dataset analysed here contains a total of 292 articles and 12,484 tweets. This research determines how to establish a warning mechanism, with an added notification time for flooding disasters, and it shows how to provide relevant disaster relief personnel with references. This article contributes by combining social media data with emergency management information cloud (EMIC) data, especially in the context of having a mechanism for warning about flooding disasters. According to the experimental results, a sliding window of 90 min and a sliding gap of 10 min obtained the best F-measure value ( F = 0.315). The event studied was Typhoon Megi (September 2016), which caused major flooding in Tainan. For the Typhoon Megi event, the flood disaster location database had 161 streets available for matching. Based on the experimental results, it is possible to obtain a high-precision (90% or higher) accuracy rate from real-time tweet data by exploiting a social media dataset.

Disaster management digitally transformed: Exploring the impact and key determinants from the UK national disaster management experience

With the increasing social and economic devastation caused by disasters around the world, the international community and country-level National Disaster Management (NDM) authorities have placed improving their ways to mitigate, prepare for, respond to, and recover from disasters as a top priority. Technological advancements and the 4th Industrial Revolution are critical tools to help achieve this. However, they also present many challenges to traditional NDM systems by altering the fundamental operational, organizational, and social dynamics of conventional disaster management. Currently, there is a lack of research that studies these aspects beyond technology and examines the impact of digital transformation on the full life cycle of disaster management on the national level. Therefore, this research fills this gap by integrating interdisciplinary concepts from different research fields including Disaster Management, Information Systems, and Business Management to understand the impact and determinants of digital transformation in NDM systems. To achieve this, the research uses the Technology-Organization-Environment (TOE) framework and conducts semi-structured interviews with UK NDM experts. The results show that the impact of digital transformation on NDM is profound, paradoxical, multi-directional, and driven by a multitude of driving forces. This research makes many significant contributions to research and practice. Theoretically, this research expands the TOE framework beyond its original underpinnings by uncovering a new set of disaster-context determinants. It also presents an innovative Layered Cake FAST (Foundations-Approach-Strategy-Technology) Model that offers a unique roadmap for NDM on how to handle its digital transformation journey. Practically, the research presents several sets of useful expert-recommended actions.

Using artificial intelligence to detect crisis related to events: Decision making in B2B by artificial intelligence

Artificial Intelligence (AI) could be an important foundation of competitive advantage in the market for firms. As such, firms use AI to achieve deep market engagement when the firm's data are employed to make informed decisions. This study examines the role of computer-mediated AI agents in detecting crises related to events in a firm. A crisis threatens organizational performance; therefore, a data-driven strategy will result in an efficient and timely reflection, which increases the success of crisis management. The study extends the situational crisis communication theory (SCCT) and Attribution theory frameworks built on big data and machine learning capabilities for early detection of crises in the market. This research proposes a structural model composed of a statistical and sentimental big data analytics approach. The findings of our empirical research suggest that knowledge extracted from day-to-day data communications such as email communications of a firm can lead to the sensing of critical events related to business activities. To test our model, we use a publicly available dataset containing 517,401 items belonging to 150 users, mostly senior managers of Enron during 1999 through the 2001 crisis. The findings suggest that the model is plausible in the early detection of Enron's critical events, which can support decision making in the market.

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This study examines the role of computer-mediated AI agents in detecting crises related to events in a firm.

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We developed a model called critical event detection analysis model (CEDA) for detecting critical events.

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This research proposes a structural model composed of a statistical and sentimental big data analytics approach.

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The findings discuss the opportunities of AI for the market.

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Artificial Intelligence could be an important foundation of competitive advantage in the market for B2B firms.

Social Media Use in Emergency Response to Natural Disasters: A Systematic Review With a Public Health Perspective

Social media research during natural disasters has been presented as a tool to guide response and relief efforts in the disciplines of geography and computer sciences. This systematic review highlights the public health implications of social media use in the response phase of the emergency, assessing (1) how social media can improve the dissemination of emergency warning and response information during and after a natural disaster, and (2) how social media can help identify physical, medical, functional, and emotional needs after a natural disaster. We surveyed the literature using 3 databases and included 44 research articles. We found that analyses of social media data were performed using a wide range of spatiotemporal scales. Social media platforms were identified as broadcasting tools presenting an opportunity for public health agencies to share emergency warnings. Social media was used as a tool to identify areas in need of relief operations or medical assistance by using self-reported location, with map development as a common method to visualize data. In retrospective analyses, social media analysis showed promise as an opportunity to reduce the time of response and to identify the individuals' location. Further research for misinformation and rumor control using social media is needed.

Quality management in humanitarian operations and disaster relief management: a review and future research directions

Quality management has been widely discussed in the literature, and recent special issues on humanitarian supply chains and relief operations have emphasized the increasing importance of quality management in this key emerging area. In this paper, we provide an extensive literature review in the field of quality management in humanitarian operations and disaster relief management. Our comprehensive review, comprising 61 articles published from 2009 to 2018, leads to the identification of enablers (e.g., transparency, policy framework), challenges (e.g., financial services, identity protection), and theory development approaches, as well as numerous research gaps that must be addressed.

Spatio-temporal distribution of negative emotions on Twitter during floods in Chennai, India, in 2015: a post hoc analysis

Background

Natural disasters are known to take their psychological toll immediately, and over the long term, on those living through them. Messages posted on Twitter provide an insight into the state of mind of citizens affected by such disasters and provide useful data on the emotional impact on groups of people. In 2015, Chennai, the capital city of Tamil Nadu state in southern India, experienced unprecedented flooding, which subsequently triggered economic losses and had considerable psychological impact on citizens. The objectives of this study are to (i) mine posts to Twitter to extract negative emotions of those posting tweets before, during and after the floods; (ii) examine the spatial and temporal variations of negative emotions across Chennai city via tweets; and (iii) analyse associations in the posts between the emotions observed before, during and after the disaster.

Methods

Using Twitter’s application programming interface, tweets posted at the time of floods were aggregated for detailed categorisation and analysis. The different emotions were extracted and classified by using the National Research Council emotion lexicon. Both an analysis of variance (ANOVA) and mixed-effect analysis were performed to assess the temporal variations in negative emotion rates. Global and local Moran’s I statistic were used to understand the spatial distribution and clusters of negative emotions across the Chennai region. Spatial regression was used to analyse over time the association in negative emotion rates from the tweets.

Results

In the 5696 tweets analysed around the time of the floods, negative emotions were in evidence 17.02% before, 29.45% during and 11.39% after the floods. The rates of negative emotions showed significant variation between tweets sent before, during and after the disaster. Negative emotions were highest at the time of disaster’s peak and reduced considerably post disaster in all wards of Chennai. Spatial clusters of wards with high negative emotion rates were identified.

Conclusions

Spatial analysis of emotions expressed on Twitter during disasters helps to identify geographic areas with high negative emotions and areas needing immediate emotional support. Analysing emotions temporally provides insight into early identification of mental health issues, and their consequences, for those affected by disasters.