Twitter as a tool for the management and analysis of emergency situations: A systematic literature review

Evaluation of the Opinions of Volunteers Involved in Disaster on the Use of Social Media

OBJECTIVE

The aim was to deeply examine the opinions of volunteers who took part in the Kahramanmaraş-centered earthquakes that occurred in Türkiye on February 6, 2023, regarding the use of social media during the disaster period.

METHODS

The study was designed as qualitative research. Because it was planned to examine the participant experiences in depth, the phenomenological design was employed in the study. Study data were collected from individuals who had earthquake experience through a semi-structured interview form between May 2023 and July 2023.

RESULTS

In line with the data obtained, 2 themes were created: "social media content and communication analysis" and "social media impact analysis and results." It was found that for information seeking, information sharing, or interaction during disasters, Twitter (X), Instagram, and WhatsApp were the most preferred social media platforms, respectively. Participants mentioned that posts related to disaster during times of disasters have an impact on their emotions. It was determined that the proper use and correct management of social media tools in times of earthquakes affect coordination and relief efforts.

CONCLUSIONS

It was concluded that the type of content shared during earthquake times affects both disaster victims and other individuals of the society positively or negatively.

Time in hand: Temporal focus in risk discourse and audience emotions on knowledge-sharing platforms

Online knowledge-sharing platforms construct risk knowledge and provide the audience with risk-related scientific facts. We study how speakers organize narratives in past, present, and future foci to influence the audience's emotions through the audience's appraisal of motive congruency and coping potential. Empirical evidence from 210 Technology, Entertainment, Design talks about disasters from 2002 to 2018 demonstrates that emphasizing the past, present, and future in risk narrative leads to the audience's comments with more negative, less positive, and more positive emotions, respectively. Concrete (vs. abstract) portrayal of the risk narrative improves the audience's situational awareness, enhances their risk appraisal, and intensifies the impact of temporal focus on emotions, providing evidence of how temporal focus impacts. These findings demonstrate that temporal focus can effectively reduce risk overreaction or ignorance and facilitate emotion regulation in risk communication.

Towards quantifying the communication aspect of resilience in disaster-prone communities

In this study, we investigate the communication networks of urban, suburban, and rural communities from three US Midwest counties through a stochastic model that simulates the diffusion of information over time in disaster and in normal situations. To understand information diffusion in communities, we investigate the interplay of information that individuals get from online social networks, local news, government sources, mainstream media, and print media. We utilize survey data collected from target communities and create graphs of each community to quantify node-to-node and source-to-node interactions, as well as trust patterns. Monte Carlo simulation results show the average time it takes for information to propagate to 90% of the population for each community. We conclude that rural, suburban, and urban communities have different inherent properties promoting the varied flow of information. Also, information sources affect information spread differently, causing degradation of information speed if any source becomes unavailable. Finally, we provide insights on the optimal investments to improve disaster communication based on community features and contexts.

Using mobile phone data to evaluate access to essential services following natural hazards

Natural hazards bring about changes in the access to essential services such as grocery stores, healthcare, schools, and day care because of facility closures, transportation system disruption, evacuation orders, power outages, and other barriers to access. Understanding changes in access to essential services following a disruption is critical to ensure equitable recovery and more resilient communities. However, past approaches to understanding facility closures and inaccessibility such as surveys and interviews are labor-intensive and of limited geographic scope. In this article, we develop an approach to understanding facility-level inaccessibility across a broad geographic area based on location-based services data collected from cell phones. This approach supplements current approaches and helps both researchers and emergency response planners better understand which communities lose access to essential services and for how long. We demonstrate our approach by analyzing loss of access to supermarkets, schools, healthcare facilities, and home improvement stores in Southwest Florida leading up to and following the landfall of Hurricane Irma in 2017.

Multi-generational labour markets: Data-driven discovery of multi-perspective system parameters using machine learning

The impact of aggressive capitalist approaches on social, economic and planet sustainability is significant. Economic issues such as inflation, energy costs, taxes and interest rates persist and are further exacerbated by global events such as wars, pandemics and environmental disasters. A sustained history of financial crises exposes weaknesses in modern economies. The Great Attrition, with many quitting jobs, adds to concerns. The diversity of the workforce poses new challenges. Transformative approaches are essential to safeguard societies, economies and the planet. In this work, we use big data and machine learning methods to discover multi-perspective parameters for multi-generational labour markets. The parameters for the academic perspective are discovered using 35,000 article abstracts from the Web of Science for the period 1958-2022 and for the professionals' perspective using 57,000 LinkedIn posts from 2022. We discover a total of 28 parameters and categorized them into five macro-parameters, Learning & Skills, Employment Sectors, Consumer Industries, Learning & Employment Issues and Generations-specific Issues. A complete machine learning software tool is developed for data-driven parameter discovery. A variety of quantitative and visualization methods are applied and multiple taxonomies are extracted to explore multi-generational labour markets. A knowledge structure and literature review of multi-generational labour markets using over 100 research articles is provided. It is expected that this work will enhance the theory and practice of artificial intelligence-based methods for knowledge discovery and system parameter discovery to develop autonomous capabilities and systems and promote novel approaches to labour economics and markets, leading to the development of sustainable societies and economies.

Predictive modeling for suspicious content identification on Twitter

The wide popularity of Twitter as a medium of exchanging activities, entertainment, and information is attracted spammers to discover it as a stage to spam clients and spread misinformation. It poses the challenge to the researchers to identify malicious content and user profiles over Twitter such that timely action can be taken. Many previous works have used different strategies to overcome this challenge and combat spammer activities on Twitter. In this work, we develop various models that utilize different features such as profile-based features, content-based features, and hybrid features to identify malicious content and classify it as spam or not-spam. In the first step, we collect and label a large dataset from Twitter to create a spam detection corpus. Then, we create a set of rich features by extracting various features from the collected dataset. Further, we apply different machine learning, ensemble, and deep learning techniques to build the prediction models. We performed a comprehensive evaluation of different techniques over the collected dataset and assessed the performance for accuracy, precision, recall, and f1-score measures. The results showed that the used different sets of learning techniques have achieved a higher performance for the tweet spam classification. In most cases, the values are above 90% for different performance measures. These results show that using profile, content, user, and hybrid features for suspicious tweets detection helps build better prediction models.

Influence of information attributes on information dissemination in public health emergencies

When public health emergencies occur, relevant information containing different topics, sentiments, and emotions spread rapidly on social media. From the cognitive and emotional dimensions, this paper explores the relationship between information attributes and information dissemination behavior. At the same time, the moderating role of the media factor (user influence) and the time factor (life cycle) in information attributes and information transmission is also discussed. The results confirm differences in the spread of posts under different topic types, sentiment types, and emotion types on social media. At the same time, the study also found that posts published by users with a high number of followers and users of a media type are more likely to spread on social media. In addition, the study also found that posts with different information attributes are easier to spread on social media during the outbreak and recurrence periods. The driving effect of life cycles is more obvious, especially for topics of prayer and fact, negative sentiment, emotions of fear, and anger. Relevant findings have specific contributions to the information governance of public opinion, the development of social media theory, and the maintenance of network order, which can further weaken the negative impact of information epidemic in the occurrence of public health emergencies, maintain normal social order, and thus create favorable conditions for the further promotion of global recovery.

Reaching and engaging people: Analyzing tweeting practices of large U.S. police departments pre- and post- the killing of George Floyd

Finding ways to improve police legitimacy and police-community relations has for long been an important social issue in the United States. It becomes particularly urgent following the murder of George Floyd on May 25th, 2020. An emerging area that holds potential in remediating police-community relations pertains to the use of social media by police. Yet, this body of research stays highly exploratory (e.g., case studies based on a small sample of agencies) and different viewpoints exist regarding the objectives of police social media usage. The current study identified 115 large police departments in the U.S. and collected their tweets over a 4-month period between 4/1/2020 and 7/31/2020. We investigated how police agencies (both individually and as an aggregate) leveraged social media to respond to the nationwide protests directed at the police and community reactions to such responses. We found that police agencies tweeted more frequently in the immediate aftermath of the murder and posted an increased number of civil-unrest related tweets. The public showed a greater interest in engaging with law enforcement agencies (i.e., average favorite and retweet counts) following the murder. A great variability emerged across agencies in their responses on social media, suggesting that examining only a handful of agencies or a particular dimension of social media usage would limit our understanding of police behaviors and citizen interactions on social media. In conclusion, we suggested a few avenues for future research (and practices) on responsible and effective use of social media by police, while pointing out the challenges associated with such inquiries.

Towards the Use of Big Data in Healthcare: A Literature Review

The interest in new and more advanced technological solutions is paving the way for the diffusion of innovative and revolutionary applications in healthcare organizations. The application of an artificial intelligence system to medical research has the potential to move toward highly advanced e-Health. This analysis aims to explore the main areas of application of big data in healthcare, as well as the restructuring of the technological infrastructure and the integration of traditional data analytical tools and techniques with an elaborate computational technology that is able to enhance and extract useful information for decision-making. We conducted a literature review using the Scopus database over the period 2010–2020. The article selection process involved five steps: the planning and identification of studies, the evaluation of articles, the extraction of results, the summary, and the dissemination of the audit results. We included 93 documents. Our results suggest that effective and patient-centered care cannot disregard the acquisition, management, and analysis of a huge volume and variety of health data. In this way, an immediate and more effective diagnosis could be possible while maximizing healthcare resources. Deriving the benefits associated with digitization and technological innovation, however, requires the restructuring of traditional operational and strategic processes, and the acquisition of new skills.

COVID-19 vaccine hesitancy: a social media analysis using deep learning

Hesitant attitudes have been a significant issue since the development of the first vaccines-the WHO sees them as one of the most critical global health threats. The increasing use of social media to spread questionable information about vaccination strongly impacts the population's decision to get vaccinated. Developing text classification methods that can identify hesitant messages on social media could be useful for health campaigns in their efforts to address negative influences from social media platforms and provide reliable information to support their strategies against hesitant-vaccination sentiments. This study aims to evaluate the performance of different machine learning models and deep learning methods in identifying vaccine-hesitant tweets that are being published during the COVID-19 pandemic. Our concluding remarks are that Long Short-Term Memory and Recurrent Neural Network models have outperformed traditional machine learning models on detecting vaccine-hesitant messages in social media, with an accuracy rate of 86% against 83%.

Public Social Media Discussions on Agricultural Product Safety Incidents: Chinese African Swine Fever Debate on Weibo

Public concern over major agricultural product safety incidents, such as swine flu and avian flu, can intensify financial losses in the livestock and poultry industries. Crawler technology were applied to reviewed the Weibo social media discussions on the African Swine Fever (ASF) incident in China that was reported on 3 August 2018, and used content analysis and network analysis to specifically examine the online public opinion network dissemination characteristics of verified individual users, institutional users and ordinary users. It was found that: (1) attention paid to topics related to "epidemic," "treatment," "effect" and "prevent" decrease in turn, with the interest in "prevent" increasing significantly when human infections were possible; (2) verified individual users were most concerned about epidemic prevention and control and play a supervisory role, the greatest concern of institutional users and ordinary users were issues related to agricultural industry and agricultural products price fluctuations respectively; (3) among institutional users, media was the main opinion leader, and among non-institutional users, elites from all walks of life, especially the food safety personnel acted as opinion leaders. Based on these findings, some policy suggestions are given: determine the nature of the risk to human health of the safety incident, stabilizing prices of relevant agricultural products, and giving play to the role of information dissemination of relevant institutions.

Colombian political leaders on Twitter during the Covid-19 pandemic

This study analyzes the use of Twitter by Colombian political elites during the Covid-19 pandemic, employing qualitative and quantitative methods and techniques. We collected Twitter data on the Colombian president, the mayor of Bogota, and all the members of the Congress of Colombia. We then analyzed qualitatively the content of the most popular tweets sent by President Iván Duque, Mayor Claudia López, and Gustavo Petro, the leader of the opposition. We also analyzed the growth in the number of their followers during the pandemic. We found that the most popular tweets from Colombian opposition politicians were often related to criticism of the government. López also informed her constituency about the state of the capital. President Duque's most popular tweets were primarily informative. During the pandemic, all three politicians gained a significant number of Twitter followers.

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.

Big data analytics meets social media: A systematic review of techniques, open issues, and future directions

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A comprehensive systematic review on social big data analytic approaches is provided.

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The main methods, pros, cons, evaluation methods, and parameters are discussed.

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A scientific taxonomy of social big data analytic approaches is presented.

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A detailed list of challenges and future research directions is outlined.

Social Networking Services (SNSs) connect people worldwide, where they communicate through sharing contents, photos, videos, posting their first-hand opinions, comments, and following their friends. Social networks are characterized by velocity, volume, value, variety, and veracity, the 5 V’s of big data. Hence, big data analytic techniques and frameworks are commonly exploited in Social Network Analysis (SNA). By the ever-increasing growth of social networks, the analysis of social data, to describe and find communication patterns among users and understand their behaviors, has attracted much attention. In this paper, we demonstrate how big data analytics meets social media, and a comprehensive review is provided on big data analytic approaches in social networks to search published studies between 2013 and August 2020, with 74 identified papers. The findings of this paper are presented in terms of main journals/conferences, yearly distributions, and the distribution of studies among publishers. Furthermore, the big data analytic approaches are classified into two main categories: Content-oriented approaches and network-oriented approaches. The main ideas, evaluation parameters, tools, evaluation methods, advantages, and disadvantages are also discussed in detail. Finally, the open challenges and future directions that are worth further investigating are discussed.

International mobile marketing: a satisfactory concept for companies and users in times of pandemic

Purpose

This study’s purpose is to analyze the international mobile marketing (IMK) in order to stage the importance of this tool in the internationalization of companies. Our understanding of mobile marketing is constantly evolving, due to its high business penetration in a world globalized by technologies.

Design/methodology/approach

A review of the relevant literature on IMK, companies and customers is undertaken to understand the link between them. The paper begins by explaining the coronavirus disease 2019 is accelerating the change of the rules of the game in traditional and online commerce around the world. Furthermore, this study uses secondary data from organisation for economic co-operation and development (OECD), Sensor Tower, mobile marketing association (MMA), App Annie, among others, to support research results.

Findings

The results have shown that IMK has opened a melting pot of opportunities for companies and consumers in this period of pandemic; the potential of this tool is being redefined, in order to identify, anticipate and satisfy customers requirement profitably and efficiently. This study aims to provide an assessment of new concept of IMK and how this tool has to be integrated into the firm’s digital marketing strategies.

Originality/value

The study contributes to make better future decisions in the international digital expansion of companies by company executives and marketing experts. This paper provides a comprehensive framework intended to guide research efforts focusing on digital marketing as well as to aid practitioners in their quest to achieve IMK success.

Clusters of science and health related Twitter users become more isolated during the COVID-19 pandemic

COVID-19 represents the most severe global crisis to date whose public conversation can be studied in real time. To do so, we use a data set of over 350 million tweets and retweets posted by over 26 million English speaking Twitter users from January 13 to June 7, 2020. We characterize the retweet network to identify spontaneous clustering of users and the evolution of their interaction over time in relation to the pandemic's emergence. We identify several stable clusters (super-communities), and are able to link them to international groups mainly involved in science and health topics, national elites, and political actors. The science- and health-related super-community received disproportionate attention early on during the pandemic, and was leading the discussion at the time. However, as the pandemic unfolded, the attention shifted towards both national elites and political actors, paralleled by the introduction of country-specific containment measures and the growing politicization of the debate. Scientific super-community remained present in the discussion, but experienced less reach and became more isolated within the network. Overall, the emerging network communities are characterized by an increased self-amplification and polarization. This makes it generally harder for information from international health organizations or scientific authorities to directly reach a broad audience through Twitter for prolonged time. These results may have implications for information dissemination along the unfolding of long-term events like epidemic diseases on a world-wide scale.

Understanding government discourses on social media: Lessons from the use of YouTube at local level1

Local Governments around the world have taken advantage of social media during the past ten years to improve transparency and to provide public services. Challenges related to information management and citizen participation have emerged, namely at the local level where the diffusion of social media has been slower compared to initiatives launched at the national level. This paper analyzes how the use of social media can reflect a change in the discursive exchanges established between local governments in Canada and Mexico and citizens. To achieve this goal, the use of YouTube by the municipalities of Quebec and Morelia was examined by using digital methods and content analysis. The author proposes the emergence of new conditions between government and users, which are changing the discourse, identity, and communication purposes of the municipalities. However, the development of more dialogic communication processes supported by social media is still a promise, at least on YouTube.

Twitter data analysis to assess the interest of citizens on the impact of marine plastic pollution

Few studies have mined social media platforms to assess environmental concerns. In this study, Twitter was scraped to obtain a ~140,000 tweet dataset related specifically to marine plastic pollution. The goal is to understand what kind of users profiles are tweeting and how and when they do it. In addition, topic modelling and graph theory techniques have allowed us to identify main concerns on this topic: i) impact on wildlife, ii) microplastics/water pollution, iii) estimates/reports, iv) legislation/protection, and v) recycling/cleaning initiatives. Results reveal a scarce influence of organizations involved in research and marine environmental awareness, so some guidelines are depicted that could help to adjust their communication plans. This is relevant to engage society through reliable information, change habits and reinforce sustainable behaviour. A visualization tool has been created to analyze the results over time.

Identifying and Characterizing the Propagation Scale of COVID-19 Situational Information on Twitter: A Hybrid Text Analytic Approach

During the recent pandemic of COVID-19, an increasing amount of information has been propagated on social media. This situational information is valuable for public authorities. Therefore, this study characterized the propagation scale of situational information types by harnessing the power of natural language processing techniques and machine learning algorithms. We observed that the length of the post has a positive correlation with type 1 information (announcements), and negative words were mostly used in type 5 information (criticizing the government), whereas anxiety-related words have a negative effect on the amount of retweeted type 0 (precautions) and type 2 (donations) information. This type of research study not only contributes to the situational information literature by comprehensively defining categories but also provides data-oriented practical insights into information so that management authorities can formulate response strategies after the pandemic. Our approach is one of its kind and combines Twitter content features, user features and LIWC linguistic features with machine learning algorithms to analyze the propagation scale of situational information, and it achieved 77% accuracy with SVM while classifying the information categories.

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.

Topic-Emotion Propagation Mechanism of Public Emergencies in Social Networks

The information propagation of emergencies in social networks is often accompanied by the dissemination of the topic and emotion. As a virtual sensor of public emergencies, social networks have been widely used in data mining, knowledge discovery, and machine learning. From the perspective of network, this study aims to explore the topic and emotion propagation mechanism, as well as the interaction and communication relations of the public in social networks under four types of emergencies, including public health events, accidents and disasters, social security events, and natural disasters. Event topics were identified by Word2vec and K-means clustering. The biLSTM model was used to identify emotion in posts. The propagation maps of topic and emotion were presented visually on the network, and the synergistic relationship between topic and emotion propagation as well as the communication characteristics of multiple subjects were analyzed. The results show that there were similarities and differences in the propagation mechanism of topic and emotion in different types of emergencies. There was a positive correlation between topic and emotion of different types of users in social networks in emergencies. Users with a high level of topic influence were often accompanied by a high level of emotion appeal.

A guided latent Dirichlet allocation approach to investigate real-time latent topics of Twitter data during Hurricane Laura

Natural disasters cause significant damage, casualties and economical losses. Twitter has been used to support prompt disaster response and management because people tend to communicate and spread information on public social media platforms during disaster events. To retrieve real-time situational awareness (SA) information from tweets, the most effective way to mine text is using natural language processing (NLP). Among the advanced NLP models, the supervised approach can classify tweets into different categories to gain insight and leverage useful SA information from social media data. However, high-performing supervised models require domain knowledge to specify categories and involve costly labelling tasks. This research proposes a guided latent Dirichlet allocation (LDA) workflow to investigate temporal latent topics from tweets during a recent disaster event, the 2020 Hurricane Laura. With integration of prior knowledge, a coherence model, LDA topics visualisation and validation from official reports, our guided approach reveals that most tweets contain several latent topics during the 10-day period of Hurricane Laura. This result indicates that state-of-the-art supervised models have not fully utilised tweet information because they only assign each tweet a single label. In contrast, our model can not only identify emerging topics during different disaster events but also provides multilabel references to the classification schema. In addition, our results can help to quickly identify and extract SA information to responders, stakeholders and the general public so that they can adopt timely responsive strategies and wisely allocate resource during Hurricane events.

A comprehensive social media data processing and analytics architecture by using big data platforms: a case study of twitter flood-risk messages

The main objective of the article is to propose an advanced architecture and workflow based on Apache Hadoop and Apache Spark big data platforms. The primary purpose of the presented architecture is collecting, storing, processing, and analysing intensive data from social media streams. This paper presents how the proposed architecture and data workflow can be applied to analyse Tweets with a specific flood topic. The secondary objective, trying to describe the flood alert situation by using only Tweet messages and exploring the informative potential of such data is demonstrated as well. The predictive machine learning approach based on Bayes Theorem was utilized to classify flood and no flood messages. For this study, approximately 100,000 Twitter messages were processed and analysed. Messages were related to the flooding domain and collected over a period of 5 days (14 May - 18 May 2018). Spark application was developed to run data processing commands automatically and to generate the appropriate output data. Results confirmed the advantages of many well-known features of Spark and Hadoop in social media data processing. It was noted that such technologies are prepared to deal with social media data streams, but there are still challenges that one has to take into account. Based on the flood tweet analysis, it was observed that Twitter messages with some considerations are informative enough to be used to estimate general flood alert situations in particular regions. Text analysis techniques proved that Twitter messages contain valuable flood-spatial information.

Collective Response to Media Coverage of the COVID-19 Pandemic on Reddit and Wikipedia: Mixed-Methods Analysis

BACKGROUND

The exposure and consumption of information during epidemic outbreaks may alter people's risk perception and trigger behavioral changes, which can ultimately affect the evolution of the disease. It is thus of utmost importance to map the dissemination of information by mainstream media outlets and the public response to this information. However, our understanding of this exposure-response dynamic during the COVID-19 pandemic is still limited.

OBJECTIVE

The goal of this study is to characterize the media coverage and collective internet response to the COVID-19 pandemic in four countries: Italy, the United Kingdom, the United States, and Canada.

METHODS

We collected a heterogeneous data set including 227,768 web-based news articles and 13,448 YouTube videos published by mainstream media outlets, 107,898 user posts and 3,829,309 comments on the social media platform Reddit, and 278,456,892 views of COVID-19-related Wikipedia pages. To analyze the relationship between media coverage, epidemic progression, and users' collective web-based response, we considered a linear regression model that predicts the public response for each country given the amount of news exposure. We also applied topic modelling to the data set using nonnegative matrix factorization.

RESULTS

Our results show that public attention, quantified as user activity on Reddit and active searches on Wikipedia pages, is mainly driven by media coverage; meanwhile, this activity declines rapidly while news exposure and COVID-19 incidence remain high. Furthermore, using an unsupervised, dynamic topic modeling approach, we show that while the levels of attention dedicated to different topics by media outlets and internet users are in good accordance, interesting deviations emerge in their temporal patterns.

CONCLUSIONS

Overall, our findings offer an additional key to interpret public perception and response to the current global health emergency and raise questions about the effects of attention saturation on people's collective awareness and risk perception and thus on their tendencies toward behavioral change.

What drives and inhibits researchers to share and use open research data? A systematic literature review to analyze factors influencing open research data adoption

Both sharing and using open research data have the revolutionary potentials for forwarding scientific advancement. Although previous research gives insight into researchers' drivers and inhibitors for sharing and using open research data, both these drivers and inhibitors have not yet been integrated via a thematic analysis and a theoretical argument is lacking. This study's purpose is to systematically review the literature on individual researchers' drivers and inhibitors for sharing and using open research data. This study systematically analyzed 32 open data studies (published between 2004 and 2019 inclusively) and elicited drivers plus inhibitors for both open research data sharing and use in eleven categories total that are: 'the researcher's background', 'requirements and formal obligations', 'personal drivers and intrinsic motivations', 'facilitating conditions', 'trust', 'expected performance', 'social influence and affiliation', 'effort', 'the researcher's experience and skills', 'legislation and regulation', and 'data characteristics.' This study extensively discusses these categories, along with argues how such categories and factors are connected using a thematic analysis. Also, this study discusses several opportunities for altogether applying, extending, using, and testing theories in open research data studies. With such discussions, an overview of identified categories and factors can be further applied to examine both researchers' drivers and inhibitors in different research disciplines, such as those with low rates of data sharing and use versus disciplines with high rates of data sharing plus use. What's more, this study serves as a first vital step towards developing effective incentives for both open data sharing and use behavior.

Big Data Analysis of Media Reports Related to COVID-19

COVID-19 is lasting longer than expected, which has a huge impact on the economy and on personal life. Each country has a different response method, and the damage scale is also distinct. This study aims to find out how COVID-19-related news was handled in the domestic media to seek ways to minimize the pandemic. The paper focuses on the number of news features by period and by disaster and analyzes related words based on big data. The results of the analysis are as follows. First, in the initial response phase, keywords to identify accurate sources of actual broadcast contents, fake news, social networking service (SNS), etc. were also ranked in the top 20. Second, in the active response phase, when the number of confirmed persons and the government’s countermeasures were announced, more than 100 COVID-19-related articles were issued, and the related words increased rapidly from the initial response stage. Therefore, the fact that COVID-19 has been expressed as a keyword indicates that our society is watching with great interest in the government’s response to the disease.

Media Exposure and Anxiety during COVID-19: The Mediation Effect of Media Vicarious Traumatization

The rapid spread and high death rates of the COVID-19 pandemic resulted in massive panic and anxiety all over the world. People rely heavily on media for information-seeking during the period of social isolation. This study aimed to explore the relationship between media exposure and anxiety, and highlighted the underlying mechanisms mediated by the media vicarious traumatization effect. A total of 1118 Chinese citizens participated in the online survey, who were from 30 provinces in mainland China. Results showed that all four types of media (official media, commercial media, social media, and overseas media) cause vicarious traumatization to their audiences to different degrees. It was also found that the impact of media exposure on anxiety was mediated by media vicarious traumatization: there were full mediation effects for commercial media exposure and overseas media exposure, while there were indirect-only mediation effects for official media exposure and social media exposure. Audiences staying in cities with a relatively severe pandemic were more susceptible to the vicarious traumatization caused by commercial media compared to those staying in Hubei. This study expanded the concept and application of vicarious traumatization to the mediated context, and the findings provided insightful advice to media practitioners in the face of major crisis.

TwiFly: A Data Analysis Framework for Twitter

Over the last decade, there have been many changes in the field of political analysis at a global level. Through social networking platforms, millions of people have the opportunity to express their opinion and capture their thoughts at any time, leaving their digital footprint. As such, massive datasets are now available, which can be used by analysts to gain useful insights on the current political climate and identify political tendencies. In this paper, we present TwiFly, a framework built for analyzing Twitter data. TwiFly accepts a number of accounts to be monitored for a specific time-frame and visualizes in real time useful extracted information. As a proof of concept, we present the application of our platform to the most recent elections of Greece, gaining useful insights on the election results.

VARTTA: A Visual Analytics System for Making Sense of Real-Time Twitter Data

Through social media platforms, massive amounts of data are being produced. As a microblogging social media platform, Twitter enables its users to post short updates as “tweets” on an unprecedented scale. Once analyzed using machine learning (ML) techniques and in aggregate, Twitter data can be an invaluable resource for gaining insight into different domains of discussion and public opinion. However, when applied to real-time data streams, due to covariate shifts in the data (i.e., changes in the distributions of the inputs of ML algorithms), existing ML approaches result in different types of biases and provide uncertain outputs. In this paper, we describe VARTTA (Visual Analytics for Real-Time Twitter datA), a visual analytics system that combines data visualizations, human-data interaction, and ML algorithms to help users monitor, analyze, and make sense of the streams of tweets in a real-time manner. As a case study, we demonstrate the use of VARTTA in political discussions. VARTTA not only provides users with powerful analytical tools, but also enables them to diagnose and to heuristically suggest fixes for the errors in the outcome, resulting in a more detailed understanding of the tweets. Finally, we outline several issues to be considered while designing other similar visual analytics systems.

Twitter-Based Safety Confirmation System for Disaster Situations

In the aftermath of disastrous events in Japan, safety information and rescue requests, as well as emergency alerts and damage situations, have been shared on Twitter. However, even victims who are familiar with smartphones or similar devices and social media cannot easily share detailed information, such as the coordinates or address of their current location, which are essential components of safety information and rescue requests. Moreover, local governments and rescue experts have difficulty in gathering such tweets from Twitter. In this paper, we propose a novel system to enable the victims to share their safety information, make rescue requests, and enable quick information gathering for decision making by local government staff or rescue experts. The proposed system is a Twitter-based safety confirmation system named T-@npi. Using the proposed application, the users can easily submit their safety information and send rescue requests on Twitter. The users who want to confirm the safety information can check it quickly on Twitter or via this system. Furthermore, the registered safety information is displayed on an online map to support rescue and assistance activities by local governments and rescue experts.

Using Mobile Phone Data for Emergency Management: a Systematic Literature Review

Emergency management (EM) has always been a concern of people from all walks of life due to the devastating impacts emergencies can have. The global outbreak of COVID-19 in 2020 has pushed EM to the top topic. As mobile phones have become ubiquitous, many scholars have shown interest in using mobile phone data for EM. This paper presents a systematic literature review about the use of mobile phone data for EM that includes 65 related articles written between 2014 and 2019 from six electronic databases. Five themes in using mobile phone data for EM emerged from the reviewed articles, and a systematic framework is proposed to illustrate the current state of the research. This paper also discusses EM under COVID-19 pandemic and five future implications of the proposed framework to guide future work.

Interactive Learning for Identifying Relevant Tweets to Support Real-time Situational Awareness

Various domain users are increasingly leveraging real-time social media data to gain rapid situational awareness. However, due to the high noise in the deluge of data, effectively determining semantically relevant information can be difficult, further complicated by the changing definition of relevancy by each end user for different events. The majority of existing methods for short text relevance classification fail to incorporate users' knowledge into the classification process. Existing methods that incorporate interactive user feedback focus on historical datasets. Therefore, classifiers cannot be interactively retrained for specific events or user-dependent needs in real-time. This limits real-time situational awareness, as streaming data that is incorrectly classified cannot be corrected immediately, permitting the possibility for important incoming data to be incorrectly classified as well. We present a novel interactive learning framework to improve the classification process in which the user iteratively corrects the relevancy of tweets in real-time to train the classification model on-the-fly for immediate predictive improvements. We computationally evaluate our classification model adapted to learn at interactive rates. Our results show that our approach outperforms state-of-the-art machine learning models. In addition, we integrate our framework with the extended Social Media Analytics and Reporting Toolkit (SMART) 2.0 system, allowing the use of our interactive learning framework within a visual analytics system tailored for real-time situational awareness. To demonstrate our framework's effectiveness, we provide domain expert feedback from first responders who used the extended SMART 2.0 system.