Rapid assessment of disaster damage using social media activity

Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

Predicting economic resilience of territories in Italy during the COVID-19 first lockdown

This paper aims to predict the economic resilience to crises of territories based on local pre-existing socioeconomic characteristics. Specifically, we consider the case of Italian municipalities during the first wave of the COVID-19 pandemic, leveraging a large-scale dataset of cardholders performing transactions in Point-of-Sales. Based on a set of machine learning classifiers, we show that network-based measures and variables related to the social, economic, demographic and environmental dimensions are relevant predictors of the economic resilience of Italian municipalities to the crisis. In particular, we find accurate classification performance both in balanced and un-balanced scenarios, as well as in the case we restrict the analysis to specific geographical areas. Our analysis predicts that territories with larger income per capita, soil consumption, concentration of real estate activities and commuting network centrality in terms of closeness and Pagerank constitute the set of most affected areas, experiencing the strongest reduction of economic activities during the COVID-19 pandemic. Overall, we provide an application of an early-warning system able to provide timely evidence to policymakers about the detrimental effects generated by natural disasters and severe crisis episodes, thus contributing to optimize public decision support systems.

"Is a game really a reason for people to die?" Sentiment and thematic analysis of Twitter-based discourse on Indonesia soccer stampede

This study examined discourses related to an Indonesian soccer stadium stampede on 1st October 2022 using comments posted on Twitter. We conducted a lexicon-based sentiment analysis to identify the sentiments and emotions expressed in tweets and performed structural topic modeling to identify latent themes in the discourse. The majority of tweets (87.8%) expressed negative sentiments, while 8.2% and 4.0% of tweets expressed positive and neutral sentiments, respectively. The most common emotion expressed was fear (29.3%), followed by sadness and anger. Of the 19 themes identified, "Deaths and mortality" was the most prominent (15.1%), followed by "family impact". The negative stampede discourse was related to public concerns such as "vigil" and "calls for bans and suspension," while positive discourse focused more on the impact of the stampede. Public health institutions can leverage the volume and rapidity of social media to improve disaster prevention strategies.

Advancing the scientific study of prehospital mass casualty response through a Translational Science process: the T1 scoping literature review stage

PURPOSE

The European Union Horizon 2020 research and innovation funding program awarded the NIGHTINGALE grant to develop a toolkit to support first responders engaged in prehospital (PH) mass casualty incident (MCI) response. To reach the projects' objectives, the NIGHTINGALE consortium used a Translational Science (TS) process. The present work is the first TS stage (T1) aimed to extract data relevant for the subsequent modified Delphi study (T2) statements.

METHODS

The authors were divided into three work groups (WGs) MCI Triage, PH Life Support and Damage Control (PHLSDC), and PH Processes (PHP). Each WG conducted simultaneous literature searches following the PRISMA extension for scoping reviews. Relevant data were extracted from the included articles and indexed using pre-identified PH MCI response themes and subthemes.

RESULTS

The initial search yielded 925 total references to be considered for title and abstract review (MCI Triage 311, PHLSDC 329, PHP 285), then 483 articles for full reference review (MCI Triage 111, PHLSDC 216, PHP 156), and finally 152 articles for the database extraction process (MCI Triage 27, PHLSDC 37, PHP 88). Most frequent subthemes and novel concepts have been identified as a basis for the elaboration of draft statements for the T2 modified Delphi study.

CONCLUSION

The three simultaneous scoping reviews allowed the extraction of relevant PH MCI subthemes and novel concepts that will enable the NIGHTINGALE consortium to create scientifically anchored statements in the T2 modified Delphi study.

Young people's mental and social distress in times of international crisis: evidence from helpline calls, 2019-2022

We document mental and social distress of children, adolescents and adults, using data on 3 million calls to German helplines between January 2019 and May 2022. High-frequency data from crisis helpline logs offer rich information on the evolution of "revealed distress" among the most vulnerable, unaffected by researchers' study design and framing. Distress of adults, measured by the volume of calls, rose significantly after both the outbreak of the pandemic and the Russian invasion of Ukraine. In contrast, the overall revealed distress of children and adolescents did not increase during those crises. The nature of young people's concerns, however, changed more strongly than for adults after the COVID-19 outbreak. Consistent with the effects of social distancing, call topics of young people shifted from problems with school and peers to problems with family and mental health. We find the share of severe mental health problems among young people to have increased with a delay, in the second and third year of the pandemic.

Public opinion changing patterns under the double-hazard scenario of natural disaster and public health event

In the context of the COVID-19 epidemic, a "double-hazard scenario" consisting of a natural disaster and a public health event occurring simultaneously is likely to arise. Focusing on this double-hazard scenario, this study developed a new opinion dynamics model that verifies the effect of opinion dynamic in practical applications and extends the realistic meaning of the logic matrix. The new model can be used to quickly identify changing trends in public opinion about two co-occurring public safety events in China, helping the government to better anticipate and respond to these real double-hazard scenarios. The new model was tested with three real double-hazard scenarios involving natural disasters and public health events in China and the simulation results were analyzed. Using visualization and Pearson correlation coefficients to analyze more than a million items of network-wide public opinion data, the new model was found to show a good fit with reality. The study finally found that in China, public attention to both natural hazards and public health events was greater when these public safety events co-occurred (double-hazard scenario) than when they occurred separately (single-hazard scenarios). These results verify the coupling phenomenon of different disasters in a multi-hazard scenario at the information level for the first time, which is greatly meaningful for multi-hazard research.

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.

Social media data shed light on air-conditioning interest of heat-vulnerable regions and sociodemographic groups

Cooling homes with air conditioners is a vital adaptation approach, but the wider adoption of air conditioners can increase hydrofluorocarbon emissions that have high global warming potential and carbon emissions as a result of more fossil energy consumption. The scale and scope of future cooling demand worldwide are, however, uncertain because the extent and drivers of air-conditioning adoption remain unclear. Here, using 2021 and 2022 Facebook and Instagram data from 113 countries, we investigate the usability of social media advertising data to address these data gaps in relation to the drivers of air-conditioning adoption. We find that social media data might represent air-conditioning purchasing trends. Globally, parents of small children and middle-aged, highly educated married or cohabiting males tend to express greater interest in air-conditioning adoption. In regions with high heat vulnerability yet little empirical data on cooling demand (e.g., the Middle East and North Africa), these sociodemographic factors play a more prominent role. These findings can strengthen our understanding of future cooling demand for more sustainable cooling management.

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.

Exploring the Functioning of Online Self-Organizations during Public Health Emergencies: Patterns and Mechanism

With the increasing use of social media, online self-organized relief has become a crucial aspect of crisis management during public health emergencies, leading to the emergence of online self-organizations. This study employed the BERT model to classify the replies of Weibo users and used K-means clustering to summarize the patterns of self-organized groups and communities. We then combined the findings from pattern discovery and documents from online relief networks to analyze the core components and mechanisms of online self-organizations. Our findings indicate the following: (1) The composition of online self-organized groups follows Pareto's law. (2) Online self-organized communities are mainly composed of sparse and small groups with loose connections, and bot accounts can automatically identify those in need and provide them with helpful information and resources. (3) The core components of the mechanism of online self-organized rescue groups include the initial gathering of groups, the formation of key groups, the generation of collective action, and the establishment of organizational norms. This study suggests that social media can establish an authentication mechanism for online self-organizations, and that authorities should encourage online interactive live streams about public health issues. However, it is important to note that self-organizations are not a panacea for all issues during public health emergencies.

Exploring Social Media Network Connections to Assist During Public Health Emergency Response: A Retrospective Case-Study of Hurricane Matthew and Twitter Users in Georgia, USA

OBJECTIVE

To assist communities who suffered from hurricane-inflicted damages, emergency responders may monitor social media messages. We present a case-study using the event of Hurricane Matthew to analyze the results of an imputation method for the location of Twitter users who follow school and school districts in Georgia, USA.

METHODS

Tweets related to Hurricane Matthew were analyzed by content analysis with latent Dirichlet allocation models and sentiment analysis to identify needs and sentiment changes over time. A hurdle regression model was applied to study the association between retweet frequency and content analysis topics.

RESULTS

Users residing in counties affected by Hurricane Matthew posted tweets related to preparedness (n = 171; 16%), awareness (n = 407; 38%), call-for-action or help (n = 206; 19%), and evacuations (n = 93; 9%), with mostly a negative sentiment during the preparedness and response phase. Tweets posted in the hurricane path during the preparedness and response phase were less likely to be retweeted than those outside the path (adjusted odds ratio: 0.95; 95% confidence interval: 0.75, 1.19).

CONCLUSIONS

Social media data can be used to detect and evaluate damages of communities affected by natural disasters and identify users' needs in at-risk areas before the event takes place to aid during the preparedness phases.

The development of new remote technologies in disaster medicine education: A scoping review

Background

Remote teaching and online learning have significantly changed the responsiveness and accessibility after the COVID-19 pandemic. Disaster medicine (DM) has recently gained prominence as a critical issue due to the high frequency of worldwide disasters, especially in 2021. The new artificial intelligence (AI)-enhanced technologies and concepts have recently progressed in DM education.

Objectives

The aim of this article is to familiarize the reader with the remote technologies that have been developed and used in DM education over the past 20 years.

Literature scoping reviews

Mobile edge computing (MEC), unmanned aerial vehicles (UAVs)/drones, deep learning (DL), and visual reality stimulation, e.g., head-mounted display (HMD), are selected as promising and inspiring designs in DM education.

Methods

We performed a comprehensive review of the literature on the remote technologies applied in DM pedagogy for medical, nursing, and social work, as well as other health discipline students, e.g., paramedics. Databases including PubMed (MEDLINE), ISI Web of Science (WOS), EBSCO (EBSCO Essentials), Embase (EMB), and Scopus were used. The sourced results were recorded in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flowchart and followed in accordance with the PRISMA extension Scoping Review checklist. We included peer-reviewed articles, Epubs (electronic publications such as databases), and proceedings written in English. VOSviewer for related keywords extracted from review articles presented as a tabular summary to demonstrate their occurrence and connections among these DM education articles from 2000 to 2022.

Results

A total of 1,080 research articles on remote technologies in DM were initially reviewed. After exclusion, 64 articles were included in our review. Emergency remote teaching/learning education, remote learning, online learning/teaching, and blended learning are the most frequently used keywords. As new remote technologies used in emergencies become more advanced, DM pedagogy is facing more complex problems.

Discussions

Artificial intelligence-enhanced remote technologies promote learning incentives for medical undergraduate students or graduate professionals, but the efficacy of learning quality remains uncertain. More blended AI-modulating pedagogies in DM education could be increasingly important in the future. More sophisticated evaluation and assessment are needed to implement carefully considered designs for effective DM education.

Multi-scale governance and data for sustainable development

Future societal systems will be characterized by heterogeneous human behaviors and data-driven collective action. Complexity will arise as a consequence of the 5th Industrial Revolution and 2nd Data Revolution possible, thanks to a new generation of digital systems and the Metaverse. These technologies will enable new computational methods to tackle inequality while preserving individual rights and self-development. In this context, we do not only need data innovation and computational science, but also new forms of digital policy and governance. The emerging fragility or robustness of the system will depend on how complexity and governance are developed. Through data, humanity has been able to study a number of multi-scale systems from biological to migratory. Multi-scale governance is the new paradigm that feeds the Data Revolution in a world that would be highly digitalized. In the social dimension, we will encounter meta-populations sharing economy and human values. In the temporal dimension, we still need to make all real-time response, evaluation, and mitigation systems a standard integrated system into policy and governance to build up a resilient digital society. Top-down governance is not sufficient to manage all the complexities and exploit all the data available. Coordinating top-down agencies with bottom-up digital platforms will be the design principle. Digital platforms have to be built on top of data innovation and implement Artificial Intelligence (AI)-driven systems to connect, compute, collaborate, and curate data to implement data-driven policy for sustainable development based on Collective Intelligence.

Digital health for climate change mitigation and response: a scoping review

OBJECTIVE

Climate change poses a major threat to the operation of global health systems, triggering large scale health events, and disrupting normal system operation. Digital health may have a role in the management of such challenges and in greenhouse gas emission reduction. This scoping review explores recent work on digital health responses and mitigation approaches to climate change.

MATERIALS AND METHODS

We searched Medline up to February 11, 2022, using terms for digital health and climate change. Included articles were categorized into 3 application domains (mitigation, infectious disease, or environmental health risk management), and 6 technical tasks (data sensing, monitoring, electronic data capture, modeling, decision support, and communication). The review was PRISMA-ScR compliant.

RESULTS

The 142 included publications reported a wide variety of research designs. Publication numbers have grown substantially in recent years, but few come from low- and middle-income countries. Digital health has the potential to reduce health system greenhouse gas emissions, for example by shifting to virtual services. It can assist in managing changing patterns of infectious diseases as well as environmental health events by timely detection, reducing exposure to risk factors, and facilitating the delivery of care to under-resourced areas.

DISCUSSION

While digital health has real potential to help in managing climate change, research remains preliminary with little real-world evaluation.

CONCLUSION

Significant acceleration in the quality and quantity of digital health climate change research is urgently needed, given the enormity of the global challenge.

Specifying evacuation return and home-switch stability during short-term disaster recovery using location-based data

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner.

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.

Exploring climate change on Twitter using seven aspects: Stance, sentiment, aggressiveness, temperature, gender, topics, and disasters

How do climate change deniers differ from believers? Is there any correlation between human sentiment and deviations from historic temperature? We answer nine such questions using 13 years of Twitter data and 15 million tweets. Seven aspects are explored, namely, user gender, climate change stance and sentiment, aggressiveness, deviations from historic temperature, topics discussed, and environmental disaster events. We found that: a) climate change deniers use the term global warming much often than believers and use aggressive language, while believers tweet more about taking actions to fight the phenomenon, b) deniers are more present in the American Region, South Africa, Japan, and Eastern China and less present in Europe, India, and Central Africa, c) people connect much more the warm temperatures with man-made climate change than cold temperatures, d) the same regions that had more climate change deniers also tweet with negative sentiment, e) a positive correlation is observed between human sentiment and deviations from historic temperature; when the deviation is between -1.143°C and +2.401°C, people tweet the most positive, f) there exist 90% correlation between sentiment and stance, and -94% correlation between sentiment and aggressiveness, g) no clear patterns are observed to correlate sentiment and stance with disaster events based on total deaths, number of affected, and damage costs, h) topics discussed on Twitter indicate that climate change is a politicized issue and people are expressing their concerns especially when witnessing extreme weather; the global stance could be considered optimistic, as there are many discussions that point out the importance of human intervention to fight climate change and actions are being taken through events to raise the awareness of this phenomenon.

Analysis of Influencing Factors of Urban Community Function Loss in China under Flood Disaster Based on Social Network Analysis Model

The increasing frequency of floods is causing an increasing impact on urban communities. To identify the key influencing factors of functional loss in Chinese urban communities under floods, this paper explored the influencing factors and factor combinations through a social network analysis approach using the 265 cases of urban communities in China affected by floods collected from 2017-2021 as research data. The key influencing factors and factor combinations were identified comprehensively using multiple indicator analyses such as core-periphery structure, node centrality, and factor pairing. The analysis results showed that "road disruption", "housing inundation", and "power interruption" are the three most critical factors affecting the functional loss of urban communities in China under floods, followed by "residents trapped", "enterprises flooded", and "silt accumulation". In addition, "road disruption-housing inundation", "housing inundation-residents trapped", and "road disruption-residents trapped" are the most common combinations of influencing factors.

Enhancing Warning, Situational Awareness, Assessment and Education in Managing Emergency: Case Study of COVID-19

The volume of network and Internet traffic is increasing extraordinarily fast daily, creating huge data. With this volume, variety, speed, and precision of data, it is hard to collect crisis information in such a massive data environment. This paper proposes a hybrid of deep convolutional neural network (CNN)-long short-term memory (LSTM)-based model to efficiently retrieve crisis information. Deep CNN is used to extract significant characteristics from multiple sources. LSTM is used to maintain long-term dependencies in extracted characteristics while preventing overfitting on recurring connections. This method has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach allows integrating artificial intelligence technologies, deep learning and social media in managing crisis model. It is based on an extension of our previous approach namely long short-term memory-based disaster management and education: this experience forms a background for this model. It combines representation training with situational awareness and education, while retrieving template information by combining various search results from multiple sources. We have extended it to improve our managing disaster model and evaluate it in the case of the coronavirus disease 2019 (COVID-19) while achieving promising results.

Modelling and Analyzing the Semantic Evolution of Social Media User Behaviors during Disaster Events: A Case Study of COVID-19

Public behavior in cyberspace is extremely sensitive to emergency disaster events. Using appropriate methodologies to capture the semantic evolution of social media users’ behaviors and discover how it varies across geographic space and time still presents a significant challenge. This study proposes a novel framework based on complex network, topic model, and GIS to describe the topic change of social media users’ behaviors during disaster events. The framework employs topic modeling to extract topics from social media texts, builds a user semantic evolution model based on a complex network to describe topic dynamics, and analyzes the spatio-temporal characteristics of public semantics evolution. The proposed framework has demonstrated its effectiveness in analyzing the semantic spatial–temporal evolution of Chinese Weibo user behavior during COVID-19. The semantic change in response to COVID-19 was characterized by obvious expansion, frequent change, and gradual stabilization over time. In this case, there were obvious geographical differences in users’ semantic changes, which were mainly concentrated in the capital and economically developed areas. The semantics of users finally focused on specific topics related to positivity, epidemic prevention, and factual comments. Our work provides new insight into the behavioral response to disasters and provides the basis for data-driven public sector decisions. In emergency situations, this model could improve situational assessment, assist decision makers to better comprehend public opinion, and support analysts in allocating resources of disaster relief appropriately.

Perception versus reality of the COVID-19 pandemic in U.S. meat markets

Disruptions to meat markets during the COVID-19 pandemic spurred mass media attention. While media deeming the U.S. food system 'broken' garnered a great deal of attention, the actual production and meat availability data does not support this conclusion. The U.S. meat supply chain, while certainly strained and with measurable consequence during periods of adjustment, proved ultimately resilient and rebounded quickly. Increased attention on meat supply chains may drive continued efforts to improve resiliency, but analyses of online media and U.S. production and cold storage data do not support a narrative that the system 'broke', but was perhaps 'strained' and 'responded efficiently'. Findings indicate that public sentiment about U.S. meat supply overall was not as dominated by pandemic-era concerns as may be hypothesized.

Gamifying Community Education for Enhanced Disaster Resilience: An Effectiveness Testing Study from Australia

Providing convenient and effective online education is important for the public to be better prepared for disaster events. Nonetheless, the effectiveness of such education is questionable due to the limited use of online tools and platforms, which also results in narrow community outreach. Correspondingly, understanding public perceptions of disaster education methods and experiences for the adoption of novel methods is critical, but this is an understudied area of research. The aim of this study is to understand public perceptions towards online disaster education practices for disaster preparedness and evaluate the effectiveness of the gamification method in increasing public awareness. This study utilizes social media analytics and conducts a gamification exercise. The analysis involved Twitter posts (n = 13,683) related to the 2019–2020 Australian bushfires, and surveyed participants (n = 52) before and after experiencing a gamified application—i.e., STOP Disasters! The results revealed that: (a) The public satisfaction level is relatively low for traditional bushfire disaster education methods; (b) The study participants’ satisfaction level is relatively high for an online gamified application used for disaster education; and (c) The use of virtual and augmented reality was found to be promising for increasing the appeal of gamified applications, along with using a blended traditional and gamified approach.

Topic Modelling and Sentiment Analysis of Global Warming Tweets

With the increasing extreme weather events and various disasters, people are paying more attention to environmental issues than ever, particularly global warming. Public debate on it has grown on various platforms, including newspapers and social media. This paper examines the topics and sentiments of the discussion of global warming on Twitter over a span of 18 months using two big data analytics techniques—topic modelling and sentiment analysis. There are seven main topics concerning global warming frequently debated on Twitter: factors causing global warming, consequences of global warming, actions necessary to stop global warming, relations between global warming and Covid-19; global warming’s relation with politics, global warming as a hoax, and global warming as a reality. The sentiment analysis shows that most people express positive emotions about global warming, though the most evoked emotion found across the data is fear, followed by trust. The study provides a general and critical view of the public’s principal concerns and their feelings about global warming on Twitter.

Exploring Climate Change Adaptation, Mitigation and Marketing Connections

Adaptation and mitigation to the adverse impacts of rising weather and climate extremes require businesses to respond with adequate marketing strategies promoting sustained economic development. Unfortunately, the connections exploring such relationships have not been extensively investigated in the current body of literature. This study investigated the five marketing categories relating to sustainable practices (sustainable marketing, social marketing, green marketing, sustainable consumption and ecological marketing) within core research themes of climate change, global warming and sustainability from a bibliometric approach using the Scopus API. Additional topic modelling was conducted using the Latent Dirichlet Allocation (LDA) unsupervised approach on downloaded abstracts to distinguish ideas communicated in time through research and publications with co-occurrences of major Intergovernmental Panel on Climate Change (IPCC) Assessment Reports and Google search queries. The results confirmed marketing strategies aligned with the theme of sustainability with little work from small developing island nations. Additionally, findings demonstrated that research exploring business strategies through green marketing directed to green consumers with sustainable supply chain management had been dominantly increasing in the literature over recent years. Similarly, social marketing associated with green consumers was a common concern for the public and academics, rising over the years with strong influence from the published IPCC Assessment Reports. This study did not explore other published databases, including climate change-related meeting transcripts and published speeches from corporate and world leaders.

Global evidence of expressed sentiment alterations during the COVID-19 pandemic

The COVID-19 pandemic has created unprecedented burdens on people's physical health and subjective well-being. While countries worldwide have developed platforms to track the evolution of COVID-19 infections and deaths, frequent global measurements of affective states to gauge the emotional impacts of pandemic and related policy interventions remain scarce. Using 654 million geotagged social media posts in over 100 countries, covering 74% of world population, coupled with state-of-the-art natural language processing techniques, we develop a global dataset of expressed sentiment indices to track national- and subnational-level affective states on a daily basis. We present two motivating applications using data from the first wave of COVID-19 (from 1 January to 31 May 2020). First, using regression discontinuity design, we provide consistent evidence that COVID-19 outbreaks caused steep declines in expressed sentiment globally, followed by asymmetric, slower recoveries. Second, applying synthetic control methods, we find moderate to no effects of lockdown policies on expressed sentiment, with large heterogeneity across countries. This study shows how social media data, when coupled with machine learning techniques, can provide real-time measurements of affective states.

Bot-Based Emergency Software Applications for Natural Disaster Situations

Upon a serious emergency situation such as a natural disaster, people quickly try to call their friends and family with the software they use every day. On the other hand, people also tend to participate as a volunteer for rescue purposes. It is unlikely and impractical for these people to download and learn to use an application specially designed for aid processes. In this work, we investigate the feasibility of including bots, which provide a mechanism to get inside the software that people use daily, to develop emergency software applications designed to be used by victims and volunteers during stressful situations. In such situations, it is necessary to achieve efficiency, scalability, fault tolerance, elasticity, and mobility between data centers. We evaluate three bot-based applications. The first one, named Jayma, sends information about affected people during the natural disaster to a network of contacts. The second bot-based application, Ayni, manages and assigns tasks to volunteers. The third bot-based application named Rimay registers volunteers and manages campaigns and emergency tasks. The applications are built using common practice for distributed software architecture design. Most of the components forming the architecture are from existing public domain software, and some components are even consumed as an external service as in the case of Telegram. Moreover, the applications are executed on commodity hardware usually available from universities. We evaluate the applications to detect critical tasks, bottlenecks, and the most critical resource. Results show that Ayni and Rimay tend to saturate the CPU faster than other resources. Meanwhile, the RAM memory tends to reach the highest utilization level in the Jayma application.

Understanding Human Activities in Response to Typhoon Hato from Multi-Source Geospatial Big Data: A Case Study in Guangdong, China

Every year typhoons severely disrupt the normal rhythms of human activities and pose serious threats to China’s coast. Previous studies have shown that the impact extent and degree of a typhoon can be inferred from various geolocation datasets. However, it remains a challenge to unravel how dwellers respond to a typhoon disaster and what they concern most in the places with significant human activity changes. In this study, we integrated the geotagged microblogs with the Tencent’s location request data to advance our understanding of dweller’s collective response to typhoon Hato and the changes in their concerns over the typhoon process. Our results show that Hato induces both negative and positive anomalies in humans’ location request activities and such anomalies could be utilized to characterize the impacts of wind and rainfall brought by Hato to our study area, respectively. Topic analysis of Hato-related geotagged microblogs reveals that the negative location request anomalies are closely related to damage-related topics, whereas the positive anomalies to traffic-related topics. The negative anomalies are significantly correlated with economic loss and population affected at city level as suggested by an over 0.7 adjusted R2. The changes in the anomalies can be used to portray the response and recovery processes of the cities impacted.

Relevance of E-Participation in the state health campaign in Spain: #EstoNoEsUnJuego / #ThisIsNotAGame

Confronting the COVID-19 health emergency has forced public administrations in Spain to work with various networks as a means of promoting their campaigns to citizens. This paper aims to analyse digital citizens' e-participation by focusing on the state health campaign #EstoNoEsUnJuego - #ThisIsNotAGame. This campaign was launched by the Spanish Ministry of Health in September 2020 via Twitter with the objective of reinforcing protection measures against the virus. A sample consisting of 19,576 tweets, sent from September 2020 to February 2021, was investigated and the results have indicated that, of 9133 users, 64.8% of citizens collaborated in the dissemination of tweets. It was observed that most messages supported the campaign by disseminating information on measures, data and news. Only 0.1% of the messages were aggressive. The conclusion is that, despite not having created a true form of communication between public institutions and citizens, e-participation has generated a functional connection between them. Citizens have acquired a responsible and participatory digital role which, although failing to show personal involvement in their comments, has been the main driving force behind the success of this campaign.

Early detection of emergency events from social media: a new text clustering approach

Emergency events require early detection, quick response, and accurate recovery. In the era of big data, social media users can be seen as social sensors to monitor real-time emergency events. This paper proposed an integrated approach to detect all four kinds of emergency events early, including natural disasters, man-made accidents, public health events, and social security events. First, the BERT-Att-BiLSTM model is used to detect emergency-related posts from massive and irrelevant data. Then, the 3 W attribute information (what, where, and when) of the emergency event is extracted. With the 3 W attribute information, we create an unsupervised dynamical event clustering algorithm based on text similarity and combine it with the supervised logistical regression model to cluster posts into different events. Experiments on Sina Weibo data demonstrate the superiority of the proposed framework. Case studies on some real emergency events show that the proposed framework has good performance and high timeliness. Practical applications of the framework are also discussed, followed by future directions for improvement.

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

People's sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people's perceptions of greenhouse gas emissions and their preferences for renewable energy resources using a sample of Twitter data. We first identify themes of discussion using semantic text similarity and network analysis. Next, we measure people's interest in renewable energy resources based on the mentioned rate in Twitter and search interest in Google trends. Then, we measure people's sentiment toward these resources and compare the interest with sentiments to identify opportunities for policy improvement. The results indicate a minor influence of governmental assemblies on Twitter discourses compared to a very high influence of two renewable energy providers amounts to more than 40% of the tweeting activities related to renewable energy. The search interest analysis shows a slight shift in people's interest in favor of renewable energy. The interest in geothermal energy is decreasing while interest in biomass energy is increasing. The sentiment analysis shows that biomass energy has the highest positive sentiments while solar and wind energy have higher interest. Solar and wind energy are found to be the two most promising sources for the future energy transition. Our study implies that governments should practice a higher influence on promoting awareness of the environment and converging between people's interests and feasible energy solutions. We also advocate Twitter as a source for collecting real-time data about social preferences for environmental policy input.

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA

People's sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people's perceptions of greenhouse gas emissions and their preferences for renewable energy resources using a sample of Twitter data. We first identify themes of discussion using semantic text similarity and network analysis. Next, we measure people's interest in renewable energy resources based on the mentioned rate in Twitter and search interest in Google trends. Then, we measure people's sentiment toward these resources and compare the interest with sentiments to identify opportunities for policy improvement. The results indicate a minor influence of governmental assemblies on Twitter discourses compared to a very high influence of two renewable energy providers amounts to more than 40% of the tweeting activities related to renewable energy. The search interest analysis shows a slight shift in people's interest in favor of renewable energy. The interest in geothermal energy is decreasing while interest in biomass energy is increasing. The sentiment analysis shows that biomass energy has the highest positive sentiments while solar and wind energy have higher interest. Solar and wind energy are found to be the two most promising sources for the future energy transition. Our study implies that governments should practice a higher influence on promoting awareness of the environment and converging between people's interests and feasible energy solutions. We also advocate Twitter as a source for collecting real-time data about social preferences for environmental policy input.

Using localized Twitter activity to assess harmful algal bloom impacts of Karenia brevis in Florida, USA

Red tide blooms of the dinoflagellate Karenia brevis (K. brevis) produce toxic coastal conditions that can impact marine organisms and human health, while also affecting local economies. During the extreme Florida red tide event of 2017-2019, residents and visitors turned to social media platforms to both receive disaster-related information and communicate their own sentiments and experiences. This was the first major red tide event since the ubiquitous use of social media, thus providing unique crowd-sourced reporting of red tide impacts. We evaluated the spatial and temporal accuracy of red tide topic activity on Twitter, taking tweet sentiments and user types (e.g. media, citizens) into consideration, and compared tweet activity with reported red tide conditions, such as K. brevis cell counts, levels of dead fish and respiratory irritation on local beaches. The analysis was done on multiple levels with respect to both locality (e.g., entire Gulf coast, county-level, city-level, zip code tabulation areas) and temporal frequencies (e.g. daily, every three days, weekly), resulting in strong correlations between local per-capita Twitter activity and the actual red tide conditions observed in the area. Moreover, an association was observed between proximity to the affected coastal areas and per-capita counts for relevant tweets. Results show that Twitter presents a trustworthy reflection of the red tide's local impacts and development over time, and can potentially augment the already existing tools for efficient assessment and a more coordinated response to the disaster.

A process-driven and need-oriented framework for review of technological contributions to disaster management

An escalation in the frequency and intensity of natural disasters is observed over the last decade, forcing the community to develop innovative technological solutions to reduce disaster impact. The multidisciplinary nature of disaster management suggests the collaboration between different disciplines for an efficient outcome; however, any such collaborative framework is found lacking in the literature. A common taxonomy and interpretation of disaster management related constraints are critical to develop efficient technological solutions. This article proposes a process-driven and need-oriented framework to facilitate the review of technology based contributions in disaster management. The proposed framework aims to bring technological contributions and disaster management activities in a single frame to better classify and analyse the literature. A systematic review of benchmark disruptive technology based contributions to disaster management has been performed using the proposed framework. Furthermore, a set of basic requirements and constraints at each phase of a disaster management process have been proposed and cited literature has been analysed to highlight corresponding trends. Finally, the scope of computer vision in disaster management is explored and potential activities where computer vision can be used in the future are highlighted.

From Farm to Fork: Early Impacts of COVID-19 on Food Supply Chain

COVID-19 pandemic has resulted in widespread global disruptions. While much is being discussed about the health and economic impacts, there has been a limited focus on the immediate food sector shocks and their related social implications in developing countries, especially when the farmer surveys cannot be conducted due to mobility restrictions in many countries. To overcome these challenges, this study uses news mining and content analysis of media articles published from February to April 2020, to assess the early impacts of the COVID-19 pandemic on the food supply chain and farm distress in India. It also presents the media perception of the impact of the pandemic and resulting policy measures using sentiment analysis, in addition to the cross-tabulation of results that show differential impacts across food supply chain components among different commodity groups and regions. The results show wide-scale impacts across different components of the food supply chain ranging from crop harvesting and processing, distribution and logistics to disruptions across food markets, as represented by 22, 11 and 30% of total articles, respectively. The impacts are also differentiated by commodity groups, with animal products having more trade and demand-side issues, logistic bottlenecks in fruits and vegetables and crops showing problems in labor availability and harvesting. Sentiment analysis of news items shows a spike in the negative sentiment immediately post the national lockdown, with relatively less negativity in subsequent weeks due to large-scale policy and community action. Sentiment classification along different indicators shows the highest negative sentiment for animal products (85%) in commodity groups, western states of India (78%) among different regions, and food supply (85%) and markets (83%) among supply chain components. Further, extreme weather analysis (using excess rainfall events) shows that farmers faced compound risks from the COVID-19 pandemic and extreme weather events in many parts of the country. The results highlight the importance of building resilient food systems, especially when the biotic and abiotic shocks are projected to increase globally due to many drivers including biodiversity loss and climate change.

The Networked Context of COVID-19 Misinformation: Informational Homogeneity on YouTube at the Beginning of the Pandemic

During the coronavirus disease 2019 (COVID-19) pandemic, the video-sharing platform YouTube has been serving as an essential instrument to widely distribute news related to the global public health crisis and to allow users to discuss the news with each other in the comment sections. Along with these enhanced opportunities of technology-based communication, there is an overabundance of information and, in many cases, misinformation about current events. In times of a pandemic, the spread of misinformation can have direct detrimental effects, potentially influencing citizens' behavioral decisions (e.g., to not socially distance) and putting collective health at risk. Misinformation could be especially harmful if it is distributed in isolated news cocoons that homogeneously provide misinformation in the absence of corrections or mere accurate information. The present study analyzes data gathered at the beginning of the pandemic (January–March 2020) and focuses on the network structure of YouTube videos and their comments to understand the level of informational homogeneity associated with misinformation on COVID-19 and its evolution over time. This study combined machine learning and network analytic approaches. Results indicate that nodes (either individual users or channels) that spread misinformation were usually integrated in heterogeneous discussion networks, predominantly involving content other than misinformation. This pattern remained stable over time. Findings are discussed in light of the COVID-19 “infodemic” and the fragmentation of information networks.

Analysis of social media data for public emotion on the Wuhan lockdown event during the COVID-19 pandemic

BACKGROUND

With outbreaks of COVID-19 around the world, lockdown restrictions are routinely imposed to limit the spread of the virus. During periods of lockdown, social media has become the main channel for citizens to exchange information with others. Public emotions are being generated and shared rapidly online with citizens using internet platforms to reduce anxiety and stress, and stay connected while isolated.

OBJECTIVES

This study aims to explore the regularity of emotional evolution by examining public emotions expressed in online discussions about the Wuhan lockdown event in January 2020.

METHODS

Data related to the Wuhan lockdown was collected from Sina Weibo by web crawler. In this study, the Ortony, Clore, and Collins (OCC) model, Word2Vec, and Bi-directional Long Short-Term Memory model were employed to determine emotional types, train vectorization of words, and identify each text emotion for the training set. Latent Dirichlet Allocation models were also employed to mine the various topic categories, while topic emotional evolution was visualized.

RESULTS

Seven types of emotions and four phases were categorized to describe emotional evolution on the Wuhan lockdown event. The study found that negative emotions such as blame and fear dominated in the early days, and public attitudes towards the lockdown gradually alleviated and reached a balance as the situation improved. Emotional expression about Wuhan lockdown event were significantly related to users' gender, location, and whether or not their account was verified. There were statistically significant correlations between different emotions within the subtle emotional categories. In addition, the evolution of emotions presented a different path due to different topics.

CONCLUSIONS

Multiple emotional categories were determined in our study, providing a detailed and explainable emotion analysis to explored emotional appeal of citizen. The public emotions were gradually easing related to the Wuhan lockdown event, there yet exists regional discrimination and post-traumatic stress disorder in this process, which would lead us to pay continuous attention to citizens lives and psychological status post-pandemic. In addition, this study provided an appropriate method and reference case for the government's public opinion control and emotional appeasement.

Socioeconomic differences and persistent segregation of Italian territories during COVID-19 pandemic

Lockdowns implemented to address the COVID-19 pandemic have disrupted human mobility flows around the globe to an unprecedented extent and with economic consequences which are unevenly distributed across territories, firms and individuals. Here we study socioeconomic determinants of mobility disruption during both the lockdown and the recovery phases in Italy. For this purpose, we analyze a massive data set on Italian mobility from February to October 2020 and we combine it with detailed data on pre-existing local socioeconomic features of Italian administrative units. Using a set of unsupervised and supervised learning techniques, we reliably show that the least and the most affected areas persistently belong to two different clusters. Notably, the former cluster features significantly higher income per capita and lower income inequality than the latter. This distinction persists once the lockdown is lifted. The least affected areas display a swift (V-shaped) recovery in mobility patterns, while poorer, most affected areas experience a much slower (U-shaped) recovery: as of October 2020, their mobility was still significantly lower than pre-lockdown levels. These results are then detailed and confirmed with a quantile regression analysis. Our findings show that economic segregation has, thus, strengthened during the pandemic.

How has the COVID-19 pandemic affected the climate change debate on Twitter?

Climate change and the COVID-19 pandemic share many similarities. However, in the past months, concerns have increased about the fact the health emergency has put on hold during the pandemic many climate adaptation and mitigation policies. We focus our attention on understanding the role of the recent health emergency on the transmission of information related to climate change, jointly with other socio-economic variables, social norms, and cultural dimensions. In doing so, we create a unique dataset containing the number of tweets written with specific climate related keywords per country worldwide, as well as country specific socio-economic characteristics, relevant social norms, and cultural variables. We find that socio-economic variables, such as income, education, and other risk-related variables matter in the transmission of information about climate change and Twitter activity. We also find that the COVID-19 pandemic has significantly decreased the overall number of messages written about climate change, postponing the climate debate worldwide; but particularly in some vulnerable countries. This shows that in spite of the existing climate emergency, the current pandemic has had a detrimental effect over the short-term planning of climate policies in countries where climate action is urgent.

Decoding the global trend of “vaccine tourism” through public sentiments and emotions: does it get a nod on Twitter?

Purpose

This paper aims to fill the major research gap prevalent in the tourism literature on the new form of tourism branching out from the COVID-19. While there are newspaper reports mentioning about the government’s reaction to vaccine tourism, there is no such study or report that tries to understand what the global masses feel about it; thus, a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter is carried out.

Design/methodology/approach

This exploratory study serves as a preliminary investigation of the social sentiment and emotion accruing around vaccine tourism on Twitter and tries to categorise them into eight basic emotions from Plutchik (1994) “wheel of emotions” as joy, disgust, fear, anger, anticipation, sadness, trust and surprise. The results are presented through data visualisation technique for analysis. The study makes use of R programming languages and the extensive packages offered on RStudio.

Findings

A total of 12,258 emotions were captured. It is evident that Vaccine Tourism has got maximum of positive sentiments (28.14%) which is almost double of the negative sentiment (14.05%). It is visible that the highest sentiment is “trust” (12.74%) and is followed by “fear” (8.97%). The least visible sentiment is “surprise” (4.32%). Polarity has been found for maximum tweets as positive (55.52%) which yet again surpasses negative polarity (33.7%), and neutral polarity is the least (10.67%).

Research limitations/implications

It can be said that people bear a positive emotion regarding vaccine tourism such as “trust” and “joy” which also denotes a positive sentiment score for testing polarity. But there are still concerns of high prices of the packages, fear-prevalent people to step out, and the uncertainty of right precautionary measures being taken still puts vaccine tourism under the radar of doubt with a fourth population having negative and neutral sentiments each. This is indicative with “fear” being the second highest emotion to the users. There are mixed emotions for vaccine tourism, but positive dominates the results.

Practical implications

The study attempts to see the global reaction on social media on vaccine tourism trend for giving food for thought to marketers. It can be said that Asians can be the target group.

Originality/value

To the best of the authors’ knowledge, there is no study that addresses the new trend of “Vaccine Tourism” or attempts to understand the emotions and sentiments of people globally.

The anatomy of natural disasters on online media: hurricanes and wildfires

Increasing prevalence and scale of natural disasters fuel the need for new approaches to evaluating, and eventually mitigating, their impact. This analysis quantifies and compares online and social media attention to hurricanes and wildfires over time and geographic space. Hurricanes studied included: Michael, Maria, Irma, Harvey, and Florence. Fires studied included: Woolsey, Mendocino, Carr, and Camp. It was hypothesized that total volume of online media content, measured in posts and mentions, varied measurably over the phases of the disasters. Furthermore, it was hypothesized that the anatomy of the disaster, specifically the number and timing/dates, of posts and mentions varied inside versus outside impacted zones/geographies. Social media content, in sheer volume, related to hurricanes was larger than that devoted to fires. A mismatch between the time periods that people post about natural disasters on social media and the times when aid is needed to rebuild was found. Mentions fell rapidly after landfall for hurricanes, and long before fires were officially contained or extinguished. This rapid fall in media attention may leave directly impacted populations without help and support during the rebuilding process. Greater understanding of volume of posts over time, or the anatomy of disasters in online media space, may help government agencies, private industry, and relief organizations understand public attentiveness before, during, and after various types of natural disasters.

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.

Local information sources received the most attention from Puerto Ricans during the aftermath of Hurricane Maria

In September 2017, Hurricane Maria made landfall across the Caribbean region as a category 4 storm. In the aftermath, many residents of Puerto Rico were without power or clean running water for nearly a year. Using both English and Spanish tweets from September 16 to October 15 2017, we investigate discussion of Maria both on and off the island, constructing a proxy for the temporal network of communication between victims of the hurricane and others. We use information theoretic tools to compare the lexical divergence of different subgroups within the network. Lastly, we quantify temporal changes in user prominence throughout the event. We find at the global level that Spanish tweets more often contained messages of hope and a focus on those helping. At the local level, we find that information propagating among Puerto Ricans most often originated from sources local to the island, such as journalists and politicians. Critically, content from these accounts overshadows content from celebrities, global news networks, and the like for the large majority of the time period studied. Our findings reveal insight into ways social media campaigns could be deployed to disseminate relief information during similar events in the future.

Effects of PM2.5 on People's Emotion: A Case Study of Weibo (Chinese Twitter) in Beijing

PM_2.5 not only harms physical health but also has negative impacts on the public’s wellbeing and cognitive and behavioral patterns. However, traditional air quality assessments may fail to provide comprehensive, real-time monitoring of air quality because of the sparse distribution of air quality monitoring stations. Overcoming some key limitations of traditional surface monitoring data, Web-based social media platforms, such as Twitter, Weibo, and Facebook, provide a promising tool and novel perspective for environmental monitoring, prediction, and evaluation. This study aims to investigate the relationship between PM_2.5 levels and people’s emotional intensity by observing social media postings. This study defines the “emotional intensity” indicator, which is measured by the number of negative posts on Weibo, based on Weibo data related to haze from 2016 and 2017. This study estimates sentiment polarity using a recurrent neural networks model based on LSTM (Long Short-Term Memory) and verifies the correlation between high PM_2.5 levels and negative posts on Weibo using a Pearson correlation coefficient and multiple linear regression model. This study makes the following observations: (1) Taking the two-year data as an example, this study recorded the significant influence of PM_2.5 levels on netizens’ posting behavior. (2) Air quality, meteorological factors, the seasons, and other factors have a strong influence on netizens’ emotional intensity. (3) From a quantitative viewpoint, the level of PM_2.5 varies by 1 unit, and the number of negative Weibo posts fluctuates by 1.0168 units. Thus, it can be concluded that netizens’ emotional intensity is significantly positively affected by levels of PM_2.5. The high correlation between PM_2.5 levels and emotional intensity and the sensitivity of social media data shows that social media data can be used to provide a new perspective on the assessment of air quality.

Space-Time Dependence of Emotions on Twitter after a Natural Disaster

Natural disasters can have significant consequences for population mental health. Using a digital spatial epidemiologic approach, this study documents emotional changes over space and time in the context of a large-scale disaster. Our aims were to (a) explore the spatial distribution of negative emotional expressions of Twitter users before, during, and after Superstorm Sandy in New York City (NYC) in 2012 and (b) examine potential correlations between socioeconomic status and infrastructural damage with negative emotional expressions across NYC census tracts over time. A total of 984,311 geo-referenced tweets with negative basic emotions (anger, disgust, fear, sadness, shame) were collected and assigned to the census tracts within NYC boroughs between 8 October and 18 November 2012. Global and local univariate and bivariate Moran's I statistics were used to analyze the data. We found local spatial clusters of all negative emotions over all disaster periods. Socioeconomic status and infrastructural damage were predominantly correlated with disgust, fear, and shame post-disaster. We identified spatial clusters of emotional reactions during and in the aftermath of a large-scale disaster that could help provide guidance about where immediate and long-term relief measures are needed the most, if transferred to similar events and on comparable data worldwide.

A social media analytics platform visualising the spread of COVID-19 in Italy via exploitation of automatically geotagged tweets

Social media play an important role in the daily life of people around the globe and users have emerged as an active part of news distribution as well as production. The threatening pandemic of COVID-19 has been the lead subject in online discussions and posts, resulting to large amounts of related social media data, which can be utilised to reinforce the crisis management in several ways. Towards this direction, we propose a novel framework to collect, analyse, and visualise Twitter posts, which has been tailored to specifically monitor the virus spread in severely affected Italy. We present and evaluate a deep learning localisation technique that geotags posts based on the locations mentioned in their text, a face detection algorithm to estimate the number of people appearing in posted images, and a community detection approach to identify communities of Twitter users. Moreover, we propose further analysis of the collected posts to predict their reliability and to detect trending topics and events. Finally, we demonstrate an online platform that comprises an interactive map to display and filter analysed posts, utilising the outcome of the localisation technique, and a visual analytics dashboard that visualises the results of the topic, community, and event detection methodologies.

Fog-assisted Energy Efficient Cyber Physical System for Panic-based Evacuation during Disasters

Disasters around the world have adversely affected every aspect of life and panic-health of stranded persons is one such category. An effective and on-time evacuation from disaster-affected areas can avoid any panic-related health problems of the stranded persons. Although the nature of disasters differ in terms of how they occur, the evacuation of stranded persons faces approximately same set of issues related to the communication, time-sensitive computation and energy efficiency of the devices operated in the disaster-affected areas. In this paper, a cyber physical system (CPS) is proposed that takes into account various challenges of the disaster evacuation, so an efficient on-time and orderly evacuation of stranded panicked persons could be realized. The system employs fog-assisted mobile and UAV devices for time-sensitive computation services, data relaying and energy-aware computation. The system uses a fog-assisted two-factor energy-aware computation approach using data reduction, which enables the energy-efficient data reception and transmission (DRecTrans) operations at the fog nodes and compensates to extend the period for other functionalities. The data reduction at fog devices employs Novel Events Identification (NEI) and Principal Component Analysis (PCA) for detecting consecutive duplicate traffic and data summarization of high dimensional data, respectively. The proposed system operates in two spaces: physical and cyber. Physical space facilitates real-world data acquisition and information sharing with the concerned stakeholders (stranded persons, evacuation teams and medical professionals). The cyber space houses various data-analytics layers and comprises of two subspaces: fog and cloud. The fog space helps in providing real-time panic-health diagnostic and alert services and enables the optimized energy consumption of devices operate in disaster-affected areas, whereas the cloud space facilitates the monitoring and prediction of panic severity of the stranded persons, using a conditional probabilistic model and seasonal auto regression integrated moving average (SARIMA), respectively. Cloud space also facilitates the disaster mapping for converging the evacuation map to the actual situation of the disaster-affected area, and geographical population analysis (GPA) for the identification of the panic severity-based critical regions. The performance evaluation of the proposed CPS acknowledges its Logistic Regression-based panic-well being determination and real-time alert generation efficiency. The simulated implementation of NEI and PCA depicts the fog-assisted energy efficiency of the DRecTrans operations of the fog nodes. The performance evaluation of the proposed CPS also acknowledges the prediction efficiency of the SARIMA and disaster mapping accuracy through GPA. The proposed system also discusses a case study related to the pandemic disaster of coronavirus disease 2019 (COVID-19), where the system can help in panic-based selective testing of the persons, and preventing panic due to distressing period of COVID-19 outbreak.

The growing amplification of social media: measuring temporal and social contagion dynamics for over 150 languages on Twitter for 2009-2020

Working from a dataset of 118 billion messages running from the start of 2009 to the end of 2019, we identify and explore the relative daily use of over 150 languages on Twitter. We find that eight languages comprise 80% of all tweets, with English, Japanese, Spanish, Arabic, and Portuguese being the most dominant. To quantify social spreading in each language over time, we compute the 'contagion ratio': The balance of retweets to organic messages. We find that for the most common languages on Twitter there is a growing tendency, though not universal, to retweet rather than share new content. By the end of 2019, the contagion ratios for half of the top 30 languages, including English and Spanish, had reached above 1-the naive contagion threshold. In 2019, the top 5 languages with the highest average daily ratios were, in order, Thai (7.3), Hindi, Tamil, Urdu, and Catalan, while the bottom 5 were Russian, Swedish, Esperanto, Cebuano, and Finnish (0.26). Further, we show that over time, the contagion ratios for most common languages are growing more strongly than those of rare languages.