[Sentiments in the COVID-19 crisis communication of German authorities and independent experts on Twitter : A sentiment analysis for the first year of the pandemic]. / Emotionalität in der COVID-19-Krisenkommunikation von Behörden und unabhängigen Expert*innen auf Twitter : Eine Sentiment-Analyse für das erste Pandemiejahr.

BACKGROUND: At the beginning of the COVID­19 pandemic in Germany, there was great uncertainty among the population and among those responsible for crisis communication. A substantial part of the communication from experts and the responsible authorities took place on social media, especially on Twitter. The positive, negative, and neutral sentiments (emotions) conveyed there during crisis communication have not yet been comparatively studied for Germany. STUDY AIM: Sentiments in Twitter messages from various (health) authorities and independent experts on COVID­19 will be evaluated for the first pandemic year (1 January 2020 to 15 January 2021) to provide a knowledge base for improving future crisis communication. MATERIAL AND METHODS: From n = 39 Twitter actors (21 authorities and 18 experts), n = 8251 tweets were included in the analysis. The sentiment analysis was done using the so-called lexicon approach, a method within the social media analytics framework to detect sentiments. Descriptive statistics were calculated to determine, among other things, the average polarity of sentiments and the frequencies of positive and negative words in the three phases of the pandemic. RESULTS AND DISCUSSION: The development of emotionality in COVID­19 tweets and the number of new infections in Germany run roughly parallel. The analysis shows that the polarity of sentiments is negative on average for both groups of actors. Experts tweet significantly more negatively about COVID­19 than authorities during the study period. Authorities communicate close to the neutrality line in the second phase, that is, neither distinctly positive nor negative.

Public Opinion Mining on Construction Health and Safety: Latent Dirichlet Allocation Approach

The construction industry has been experiencing many occupational accidents as working on construction sites is dangerous. To reduce the likelihood of accidents, construction companies share the latest construction health and safety news and information on social media. While research studies in recent years have explored the perceptions towards these companies' social media pages, there are no big data analytic studies conducted on Instagram about construction health and safety. This study aims to consolidate public perceptions of construction health and safety by analyzing Instagram posts. The study adopted a big data analytics approach involving visual, content, user, and sentiment analyses of Instagram posts (n = 17,835). The study adopted the Latent Dirichlet Allocation, a kind of machine learning approach for generative probabilistic topic extraction, and the five most mentioned topics were: (a) training service, (b) team management, (c) training organization, (d) workers' work and family, and (e) users' action. Besides, the Jaccard coefficient co-occurrence cluster analysis revealed: (a) the most mentioned collocations were ‘construction safety week', ‘safety first', and ‘construction team', (b) the largest clusters were ‘safety training', ‘occupational health and safety administration', and ‘health and safety environment', (c) the most active users were ‘Parallel Consultancy Ltd.', ‘Pike Consulting Group', and ‘Global Training Canada', and (d) positive sentiment accounted for an overwhelming figure of 85%. The findings inform the industry on public perceptions that help create awareness and develop preventative measures for increased health and safety and decreased incidents.

Understanding Residents' Behavior for Smart City Management by Sequential and Periodic Pattern Mining

Understanding the residents’routine and repetitive behavior patterns is important for city planners and strategic partners to enact appropriate city management policies. However, the existing approaches reported in smart city management areas often rely on clustering or machine learning, which are ineffective in capturing such behavioral patterns. Aiming to address this research gap, this article proposes an analytical framework, adopting sequential and periodic pattern mining techniques, to effectively discover residents’routine behavior patterns. The effectiveness of the proposed framework is demonstrated in a case study of American public behavior based on a large-scale venue check-in dataset. The dataset was collected in 2020 (during the global pandemic due to COVID-19) and contains 257 561 check-in data of 3995 residents. The findings uncovered interesting behavioral patterns and venue visit information of residents in the United States during the pandemic, which could help the public and crisis management in cities. IEEE

Demagoguery, populism, and foreign policy rhetoric: evidence from Jair Bolsonaro's tweets

This article investigates whether Brazil's President Jair Bolsonaro extended demagoguery and populism into his foreign policy discourse. An analysis of 673 tweets indicates that demagoguery was more common (observed in 94 tweets) than populism (observed in 14 tweets). Bolsonaro adopted a Red Scare tactic, distorted information about the 2019 Amazon wildfires, spread rumours about COVID-19, and portrayed relations with the US during Trump's administration and Israel during Netanyahu's as panaceas. Findings suggest that demagoguery can ramify into foreign policy discourse, with a leader fitting distorted interpretations of foreign topics and actors into stories made for domestic consumption. Bolsonaro was cautious concerning relations with China though, indicating that international power politics and expected gains or losses from trade and investment may condition the scope of demagogical discourses. This article shows a conceptual gap in literature on foreign policy discourse, which a framework using the concept of demagoguery can, in part, fill. [ FROM AUTHOR] Copyright of Contemporary Politics is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A user-oriented multi-input Sentiment Analysis and Feedback System for mental state awareness necessitated due to COVID-19

Development in technology has led to a spike in sharing of opinions about different subjects on social media, for instance, movie or product reviews. Unprecedented COVID-19 led to forced isolation and affected mental health negatively. This paper introduces a system to detect users' emotions and mental states based on provided input. Among the different data sources available on social media, real-time Twitter data is used in this analysis. Sentiment analysis can be used as a tool at various levels, right from individual to organizational development. Deep learning algorithms like LSTM and CNN lay the foundation of this system. Python libraries and Google APIs are used to add functionalities. Earlier studies only focused on detecting emotions, whereas the proposed system provides the user with a graphical analysis of detected emotions and apt suggestions like motivational quotes or videos. The system accepts multilingual text input, speech, or video input. The scope of this system is not restricted to COVID-19 related texts. This research will assist individuals and businesses and aid future development. © 2022 IEEE.

Public Awareness and Sentiment Analysis of COVID-Related Discussions Using BERT-Based Infoveillance

Understanding different aspects of public concerns and sentiments during large health emergencies, such as the COVID-19 pandemic, is essential for public health agencies to develop effective communication strategies, deliver up-to-date and accurate health information, and mitigate potential impacts of emerging misinformation. Current infoveillance systems generally focus on discussion intensity (i.e., number of relevant posts) as an approximation of public awareness, while largely ignoring the rich and diverse information in texts with granular information of varying public concerns and sentiments. In this study, we address this grand challenge by developing a novel natural language processing (NLP) infoveillance workflow based on bidirectional encoder representation from transformers (BERT). We first used a smaller COVID-19 tweet sample to develop a content classification and sentiment analysis model using COVID-Twitter-BERT. The classification accuracy was between 0.77 and 0.88 across the five identified topics. In the sentiment analysis with a three-class classification task (positive/negative/neutral), BERT achieved decent accuracy, 0.7. We then applied the content topic and sentiment classifiers to a much larger dataset with more than 4 million tweets in a 15-month period. We specifically analyzed non-pharmaceutical intervention (NPI) and social issue content topics. There were significant differences in terms of public awareness and sentiment towards the overall COVID-19, NPI, and social issue content topics across time and space. In addition, key events were also identified to associate with abrupt sentiment changes towards NPIs and social issues. This novel NLP-based AI workflow can be readily adopted for real-time granular content topic and sentiment infoveillance beyond the health context. [ABSTRACT FROM AUTHOR] Copyright of AI is the property of MDPI and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Sigmoidal Particle Swarm Optimization for Twitter Sentiment Analysis

Social media, like Twitter, is a data repository, and people exchange views on global issues like the COVID-19 pandemic. Social media has been shown to influence the low acceptance of vaccines. This work aims to identify public sentiments concerning the COVID-19 vaccines and better understand the individual's sensitivities and feelings that lead to achievement. This work proposes a method to analyze the opinion of an individual's tweet about the COVID-19 vaccines. This paper introduces a sigmoidal particle swarm optimization (SPSO) algorithm. First, the performance of SPSO is measured on a set of 12 benchmark problems, and later it is deployed for selecting optimal text features and categorizing sentiment. The proposed method uses TextBlob and VADER for sentiment analysis, CountVectorizer, and term frequency-inverse document frequency (TF-IDF) vectorizer for feature extraction, followed by SPSO-based feature selection. The Covid-19 vaccination tweets dataset was created and used for training, validating, and testing. The proposed approach outperformed considered algorithms in terms of accuracy. Additionally, we augmented the newly created dataset to make it balanced to increase performance. A classical support vector machine (SVM) gives better accuracy for the augmented dataset without a feature selection algorithm. It shows that augmentation improves the overall accuracy of tweet analysis. After the augmentation performance of PSO and SPSO is improved by almost 7% and 5%, respectively, it is observed that simple SVM with 10-fold cross-validation significantly improved compared to the primary dataset. © 2023 Tech Science Press. All rights reserved.

Social Bots' Role in the COVID-19 Pandemic Discussion on Twitter.

Social bots have already infiltrated social media platforms, such as Twitter, Facebook, and so on. Exploring the role of social bots in discussions of the COVID-19 pandemic, as well as comparing the behavioral differences between social bots and humans, is an important foundation for studying public health opinion dissemination. We collected data on Twitter and used Botometer to classify users into social bots and humans. Machine learning methods were used to analyze the characteristics of topic semantics, sentiment attributes, dissemination intentions, and interaction patterns of humans and social bots. The results show that 22% of these accounts were social bots, while 78% were humans, and there are significant differences in the behavioral characteristics between them. Social bots are more concerned with the topics of public health news than humans are with individual health and daily lives. More than 85% of bots' tweets are liked, and they have a large number of followers and friends, which means they have influence on internet users' perceptions about disease transmission and public health. In addition, social bots, located mainly in Europe and America countries, create an "authoritative" image by posting a lot of news, which in turn gains more attention and has a significant effect on humans. The findings contribute to understanding the behavioral patterns of new technologies such as social bots and their role in the dissemination of public health information.

YouTube's Social Media Analytics as an Evaluation of Educational Teaching Videos

During the COVID-19 lockdowns in South Africa undergraduate laboratory sessions were forbidden, in turn, video-based tutorials were proposed as a tentative solution to address the lack of in-person practical demonstration sessions. Five videos were filmed on electrical engineering topics, uploaded, and then publicly shared on YouTube. An investigation was then conducted as to whether videos may be useful for the teaching of practical engineering content in the university context. This article is a report back on the findings of using YouTube as a platform for sharing and evaluating engineering educational practical tutorial videos. The gaol of this article is to introduce YouTube's social media analytics as a tool for educators to evaluate their educational videos. The findings suggest that educators may consider evaluating their videos using social media analytics, but these analytics should be reviewed critically and should comprise of several metrics measured temporally. Understanding YouTube's recommender system and its influence on the platform is also an important factor in evaluating one's video content. © 2022 IEEE.

Developing an evidence-based TISM: an application for the success of COVID-19 Vaccination Drive.

The study illustrates an application of evidence data for performing Total Interpretive Structural Modeling (TISM). TISM is widely used to analyze the critical success factors or inhibitors and their interlinkages. This study uses learning from evidence data, specifically social media analytics, to identify the relationship between the elements. Thus, it leads to the advancement of the TISM-P methodology with evidence-based TISM (TISM-E). This study uses Twitter as a source of evidence data. Further, 2,60,297 tweets were used to illustrate the process of TISM-E. The paper demonstrates the application of TISM-E for the success of the COVID-19 vaccination drive. The pandemic effects are long-term; therefore, the hierarchical model developed shows a sustainable approach for vaccinating maximum population. Further, the framework developed will ensure the distribution efficacy of vaccines. In addition, it will aid practitioners in developing future vaccination policies. The enhanced model provides evidence for polarity (positive/negative) of relationships and can help to build propositions for theory development. The study contributes to healthcare, modeling research, and graph-theoretic literature.

Exploring public opinion about telehealth during COVID-19 by social media analytics.

While COVID-19 catalyzed the acceptance and use of telehealth, our understanding of how it is perceived by multi-stakeholders such as patients, clinicians, and health authorities is limited. Drawing on social media analytics, this research examines social media discourses and users' opinions about telehealth during the COVID-19 pandemic. It applies natural language processing and deep learning to explore word of mouth on telehealth with a contextualized focus on the COVID-19 pandemic. We conducted topic modeling, sentiment analysis, and emotion analysis (fearful, happy, sad, surprised, and angry emotions). The topic modeling analysis led to the identification of 18 topics, representing 6 themes of digital health service delivery, pandemic response, communication and promotion, government action, health service domains (e.g. mental health, cancer, aged care), as well as pharma and drug. The sentiment analysis revealed that while most opinions expressed in tweets were positive, the public expressed mostly negative opinions about certain aspects of COVID-19 such as lockdowns and cyberattacks. Emotion analysis of tweets showed a dominant pattern of fearful and sad emotions in particular topics. The results of this study that inductively emerged from our social media analysis can aid public health authorities and health professionals to address the concerns of telehealth users and improve their experiences.

COVID-19 staycations and the implications for leisure travel.

The COVID-19 pandemic has prompted the re-emergence of staycations to the fore, as many people were forced to spend their vacations at or close to home due to travel restrictions. This phenomenon first went mainstream during the 2008 financial crisis, and has now been further accelerated by the COVID-19 pandemic. This study investigated the growth and practice of staycations during the first two years of the pandemic by analyzing social media and internet search data using Latent Dirichlet Allocation (LDA) topic modeling and Google Trends analytics. Key findings suggest that, while spatially close to home, people tried to achieve a psychological distance away from home. This was demonstrated by a strong global search interest in spending staycations at hotels close to home. The optimal LDA topic model produced 38 topics which were classified under four aggregate dimensions of antecedents, attributes, activities, and consequences of staycations. The findings provide useful insights to managers and policymakers on boosting revenue through this practice, and the role of staycations in promoting leisure activities close to home and sustainable tourism.

Demagoguery, populism, and foreign policy rhetoric: evidence from Jair Bolsonaro’s tweets

This article investigates whether Brazil’s President Jair Bolsonaro extended demagoguery and populism into his foreign policy discourse. An analysis of 673 tweets indicates that demagoguery was more common (observed in 94 tweets) than populism (observed in 14 tweets). Bolsonaro adopted a Red Scare tactic, distorted information about the 2019 Amazon wildfires, spread rumours about COVID-19, and portrayed relations with the US during Trump’s administration and Israel during Netanyahu’s as panaceas. Findings suggest that demagoguery can ramify into foreign policy discourse, with a leader fitting distorted interpretations of foreign topics and actors into stories made for domestic consumption. Bolsonaro was cautious concerning relations with China though, indicating that international power politics and expected gains or losses from trade and investment may condition the scope of demagogical discourses. This article shows a conceptual gap in literature on foreign policy discourse, which a framework using the concept of demagoguery can, in part, fill. [ FROM AUTHOR] Copyright of Contemporary Politics is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

COVID-19 fake news analytics from social media using topic modeling and clustering

Nowadays, Health misinformation and myths regarding various types of disease has spread on social media which terrified the public. During COVID-19 pandemic, misinformation and fake news outbreak increased as social media platforms play important role to enable people to view, search, and share the news as well as their point of view globally. Social media users might find difficulties in checking the validity of the news as they could not differentiate which one are the authorized news. Thus, it is too risky if people could easily be swayed by believing the news without validation. Therefore, the goal of this research is to classify the news related to COVID-19 using topic modeling and clustering. Latent Dirichlet Allocation is used for topic modeling of the fake and real news. This study can increase the awareness among social media users to reduce the risk of believing and sharing the misinformation especially during COVID-19 pandemic. © 2022 Elsevier Inc. All rights reserved.

Twitter conversations predict the daily confirmed COVID-19 cases.

As of writing this paper, COVID-19 (Coronavirus disease 2019) has spread to more than 220 countries and territories. Following the outbreak, the pandemic's seriousness has made people more active on social media, especially on the microblogging platforms such as Twitter and Weibo. The pandemic-specific discourse has remained on-trend on these platforms for months now. Previous studies have confirmed the contributions of such socially generated conversations towards situational awareness of crisis events. The early forecasts of cases are essential to authorities to estimate the requirements of resources needed to cope with the outgrowths of the virus. Therefore, this study attempts to incorporate the public discourse in the design of forecasting models particularly targeted for the steep-hill region of an ongoing wave. We propose a sentiment-involved topic-based latent variables search methodology for designing forecasting models from publicly available Twitter conversations. As a use case, we implement the proposed methodology on Australian COVID-19 daily cases and Twitter conversations generated within the country. Experimental results: (i) show the presence of latent social media variables that Granger-cause the daily COVID-19 confirmed cases, and (ii) confirm that those variables offer additional prediction capability to forecasting models. Further, the results show that the inclusion of social media variables introduces 48.83%-51.38% improvements on RMSE over the baseline models. We also release the large-scale COVID-19 specific geotagged global tweets dataset, MegaGeoCOV, to the public anticipating that the geotagged data of this scale would aid in understanding the conversational dynamics of the pandemic through other spatial and temporal contexts.

Fostering consumer engagement during an omnipresent victim crisis: understanding the role of values in pandemic-themed advertising

Many brands have launched pandemic-themed advertising campaigns, aiming to build rapport with their customers in this unprecedented moment. Yet it is challenging for brands to know how to communicate efficiently. To fill this gap, the current research aims to provide a systematic framework that could guide advertisers in designing pandemic-themed advertisements to stimulate consumer engagement on social media by examining the role of values in context-specific brand communications. In particular, we analyze a large corpus of 286 brand YouTube videos posted between the onset of the COVID-19 and the fall of 2020 through a combination of qualitative induction, coding, and big data analytics. The results demonstrate that brands can incorporate various values in their brand communications when the world is combating a victim crisis like the current pandemic. Our findings reveal that hedonism, universalism, conformity, security, and tradition values positively predict consumer engagement (i.e., commenting), whereas stimulation value negatively predicts consumer commenting. We develop a new type of victim crisis - omnipresent victim crisis - and offer a theorization of this sub-type of victim crisis to delineate the pandemic or crises alike (e.g., environmental issues) for future research. We further highlight the role of value embodiment in crisis communication and advertising literature and offer rich theoretical and practical implications.

Detecting Depression in Tweets Using Natural Language Processing and Deep Learning

COVID-19 has caused physical, emotional, and psychological distress for people. Due to COVID-19 norms, people were restricted to their homes and could not interact with other people, due to which they turned to social media to express their state of mind. In this paper, we implemented a system using TensorFlow, which consists of multilayer perceptron (MLP), convolutional neural networks (CNN), and long short-term memory (LSTM), which works on preprocessing, semantic information on our manually extracted dataset using Twint scraper. The models were used for classifying tweets, based upon whether they indicate depressive behavior or not. We experimented for different optimizer algorithms and their related hyperparameters for all the models. The highest accuracy was achieved by MLP using sentence embeddings, which gave an accuracy of 94% over 50 epochs, closely followed by the other two. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Dealing with the COVID-19 crisis: Theoretical application of social media analytics in government crisis management.

Little theory-grounded research addresses how to use social media strategically in government public relations through machine learning. To fill this gap, we propose a way to optimize social media analytics to manage issues and crises by using the framework of attribution theory to analyze 360,861 tweets. In particular, we examined the attribution of crisis responsibility related to the spread of COVID-19 and its relations to the negative emotions of U.S. citizens on Twitter for six months (from January 20 to June 30, 2020). The results of this study showed that social media analytics is a valid tool to monitor how the spread of COVID-19 evolved from an issue to a crisis for the Trump administration. In addition, the federal government's lack of response and inability to handle the outbreak led to citizens' engagement and amplification of negative tweets that blamed the Trump White House. Theoretical and practical implications of the results are discussed.

Social Versus Physical Distancing: Analysis of Public Health Messages at the Start of COVID-19 Outbreak in Malaysia Using Natural Language Processing

The study presents an attempt to analyse how social media netizens in Malaysia responded to the calls for “Social Distancing” and “Physical Distancing” as the newly recommended social norm was introduced to the world as a response to the COVID-19 global pandemic. The pandemic drove a sharp increase in social media platforms’ use as a public health communication platform since the first wave of the COVID-19 outbreak in Malaysia in April 2020. We analysed thousands of tweets posted by Malaysians daily between January 2020 and August 2021 to determine public perceptions and interactions patterns. The analysis focused on positive and negative reactions and the interchanges of uses of the recommended terminologies “social distancing” and “physical distancing”. Using linguistic analysis and natural language processing, findings dominantly indicate influences from the multilingual and multicultural values held by Malaysian netizens, as they embrace the concept of distancing as a measure of global public health safety. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Artificial Intelligent Technologies for the Construction Industry: How Are They Perceived and Utilized in Australia?

Artificial intelligence (AI) is a powerful technology that can be utilized throughout a construction project lifecycle. Transition to incorporate AI technologies in the construction industry has been delayed due to the lack of know-how and research. There is also a knowledge gap regarding how the public perceives AI technologies, their areas of application, prospects, and constraints in the construction industry. This study aims to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. This study adopted social media analytics, along with sentiment and content analyses of Twitter messages (n = 7906), as the methodological approach. The results revealed that: (a) robotics, internet-of-things, and machine learning are the most popular AI technologies in Australia;(b) Australian public sentiments toward AI are mostly positive, whilst some negative perceptions exist;(c) there are distinctive views on the opportunities and constraints of AI among the Australian states/territories;(d) timesaving, innovation, and digitalization are the most common AI prospects;and (e) project risk, security of data, and lack of capabilities are the most common AI constraints. This study is the first to explore AI technology adoption prospects and constraints in the Australian construction industry by analyzing social media data. The findings inform the construction industry on public perceptions and prospects and constraints of AI adoption. In addition, it advocates the search for finding the most efficient means to utilize AI technologies. The study helps public perceptions and prospects and constraints of AI adoption to be factored in construction industry technology adoption.