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Data visualizations can empower an audience to make informed decisions. At the same time, deceptive representations of data can lead to inaccurate interpretations while still providing an illusion of data-driven insights. Existing research on misleading visualizations primarily focuses on examples of charts and techniques previously reported to be deceptive. These approaches do not necessarily describe how charts mislead the general population in practice. We instead present an analysis of data visualizations found in a real-world discourse of a significant global event - Twitter posts with visualizations related to the COVID-19 pandemic. Our work shows that, contrary to conventional wisdom, violations of visualization design guidelines are not the dominant way people mislead with charts. Specifically, they do not disproportionately lead to reasoning errors in posters' arguments. Through a series of examples, we present common reasoning errors and discuss how even faithfully plotted data visualizations can be used to support misinformation. © 2023 Owner/Author.
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BACKGROUND: In 2019, coronavirus disease 2019 (COVID-19) broke out worldwide, leading to a pandemic. Studies have shown that COVID-19 patients in intensive care units (ICUs) require more nursing care than other patients. ICU nurses who care for patients with COVID-19 have shown signs of psychological and physical strain. AIM: The aim of this study was to illuminate ICU nurses' experiences of caring for patients with COVID-19 in ICUs during the first wave of the pandemic. DESIGN: A qualitative, descriptive and inductive approach was used. METHOD: A total of 70 blog posts from 13 bloggers in the United States, Great Britain, Finland and Sweden were analysed using qualitative inductive manifest content analysis. RESULTS: The results reveal an overall theme: 'An overturned existence under extreme conditions'. Furthermore, three categories-'the virus caused changes in work and private lives', 'unreasonable demands', and to hold on to caring ideals thanks to the support of others'-and seven subcategories were identified. CONCLUSION: Caring for patients with COVID-19 during the first wave of the pandemic was demanding because of a lack of knowledge about the disease and the severity of the illness. This led to ICU nurses experiencing extreme conditions that affected various aspects of their lives. Support from colleagues and teamwork were revealed to be particularly important for how nurses dealt with the demands of working during a pandemic, as was sufficient recovery time between work shifts. RELEVANCE TO CLINICAL PRACTICE: Work in ICUs was challenging and demanding, even before the pandemic. This study contributes to an understanding of the complex work environment that existed in hospitals during the first wave of the COVID-19 pandemic. The knowledge obtained from this study can be used to revise working conditions and identify health interventions for ICU nurses.
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Social Media are an important communication tool in today's society. In recent years, many events have been held online due to COVID-19, making Social Media an even more important communication tool. However, it is difficult to explicitly imagine the recipients of messages when posting on Social Media and there is a tendency to provide information easily, leading to the existence of inappropriate postings that the user does not intend. Furthermore, it is difficult to disclose information for anonymous posting on Twitter. This cause the link problem between the posts. In our proposal, we realize a way to solve these problems by realizing a Social Media that allows both unlinkable posting and disclose posting. Specifically, unlinkable posts can be changed to named posts, and when the name is changed, it is guaranteed that the person who posted the anonymous post was really the anonymous writer and that the anonymous writer cannot be identified from the anonymous post. We introduced randomized pseudonyms to prevent the viewer from checking a post text based only on the posting name without checking the contents of the posting. We also show how to prevent the attack on our proposed scheme by using hiding property and binding property of the commitment scheme. In addition, we implement the proposed scheme and describe the changes between our proposed scheme and regular post in posting time, publication time, and verification time.
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Purpose: The authors explore how the sentiment expressed by emojis in comments on stocks is associated with the stocks' subsequent returns. Design/methodology/approach: By applying our own analyzer, the authors find a sentiment effect of emojis on stocks returns separately to the plain text-expressed sentiment in Reddit posts about meme stocks such as Gamestop during the Covid-19 pandemic. Findings: The authors document that a one-standard deviation change in emoji sentiment magnitude measured as the quantity of positive emoji sentiment posts over the previous hour is associated with an 0.06% (annualized: 109.2%) one-hour abnormal stock return compared to a mean of 0.03% (annualized: 54.6%). If the stock exhibits a higher intra-hour volatility, a proxy for uninformed noise trading, this relation is more pronounced and even stronger compared to stock return's relation to plain text sentiment. Research limitations/implications: The authors are not able to show causation that is open to future research. It also remains an open question how emojis impact market price efficiency. Practical implications: Emojis are positively related to stock returns in addition to plain text-expressed content if they are discussed heavily by retail investors in Internet boards such as Reddit. Social implications: Shared emotions expressed by emojis might have an influence on how disconnected individuals make homogeneous decisions. This argument might explain our found relation of emojis and stock returns. Originality/value: So, the study findings provide empirical evidence that emojis in Reddit posts convey information on future short-term stocks returns distinct from information expressed in plain text, in the case of volatile stocks, with a higher magnitude. © 2023, Emerald Publishing Limited.
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As the possibility of sharing inaccurate information on social media increases markedly during the health crisis, there is a need to develop an understanding of social media users' motivations for online sharing of information related to major public health challenges such as COVID-19. This study utilised an online survey based on Theory of Planned Behaviour and Diffusion of Innovation Theory to examine how the behavioural intention to share COVID-19-related content on social media is impacted and to develop a model of health information sharing. Results indicate that opinion leadership, beliefs held towards the source of the information, and peers' influence serve as determinants of the intention to share COVID-19-related information on social media, while the opinion-seeking attitude does not, which could be explained by opinion seekers' inherent tendency to seek more sources to verify new information obtained. The study contributes to the Information Science field by addressing the previously under-researched area and proposing a new model that explains the impact of the factors on behavioural intention to share health-related information during the health crisis in the online network environment.
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Real-time access to information during a pandemic is crucial for mobilizing a response. A sentiment analysis of Twitter posts from the first 90 days of the COVID-19 pandemic was conducted. In particular, 2 million English tweets were collected from users in the United States that contained the word 'covid' between January 1, 2020 and March 31, 2020. Sentiments were used to model the new case and death counts using data from this time. The results of linear regression and k-nearest neighbors indicate that sentiments expressed on social media accurately predict both same-day and near future counts of both COVID-19 cases and deaths. Public health officials can use this knowledge to assist in responding to adverse public health events. Additionally, implications for future research and theorizing of social media's impact on health behaviors are discussed. © 2022 IEEE Computer Society. All rights reserved.
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To prevent and curb viral outbreaks, such as COVID-19, it is important to increase vaccination coverage while resolving vaccine hesitancy and refusal. To understand why COVID-19 vaccination coverage had rapidly increased in Japan, we analyzed Twitter posts (tweets) to track the evolution of people's stance on vaccination and clarify the factors of why people decide to vaccinate. We collected all Japanese tweets related to vaccines over a five-month period and classified the vaccination stances of users who posted those tweets by using a deep neural network we designed. Examining diachronic changes in the users' stances on this large-scale vaccine dataset, we found that a certain number of neutral users changed to a pro-vaccine stance while very few changed to an anti-vaccine stance in Japan. Investigation of their information-sharing behaviors revealed what types of users and external sites were referred to when they changed their stances. These findings will help increase coverage of booster doses and future vaccinations. © 2022 IEEE.
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With the spread of the SARS-CoV-2, enormous amounts of information about the pandemic are disseminated through social media platforms such as Twitter. Social media posts often leverage the trust readers have in prestigious news agencies and cite news articles as a way of gaining credibility. Nevertheless, it is not always the case that the cited article supports the claim made in the social media post. We present a cross-genre ad hoc pipeline to identify whether the information in a Twitter post (i.e., a "Tweet") is indeed supported by the cited news article. Our approach is empirically based on a corpus of over 46.86 million Tweets and is divided into two tasks: (i) development of models to detect Tweets containing claim and worth to be fact-checked and (ii) verifying whether the claims made in a Tweet are supported by the newswire article it cites. Unlike previous studies that detect unsubstantiated information by post hoc analysis of the patterns of propagation, we seek to identify reliable support (or the lack of it) before the misinformation begins to spread. We discover that nearly half of the Tweets (43.4%) are not factual and hence not worth checking - a significant filter, given the sheer volume of social media posts on a platform such as Twitter. Moreover, we find that among the Tweets that contain a seemingly factual claim while citing a news article as supporting evidence, at least 1% are not actually supported by the cited news and are hence misleading. © 2022 Association for Computing Machinery.
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Since the onset of the COVID-19 pandemic, social media users have shared their personal experiences related to the viral infection. Their posts contain rich information of symptoms that may provide useful hints to advancing the knowledge body of medical research and supplement the discoveries from clinical settings. Identification of symptom expressions in social media text is challenging, partially due to lack of annotated data. In this study, we investigate utilizing few-shot learning with generative pre-trained transformer language models to identify COVID-19 symptoms in Twitter posts. The results of our approach show that large language models are promising in more accurately identifying symptom expressions in Twitter posts with small amount of annotation effort, and our method can be applied to other medical and health applications where abundant of unlabeled data is available. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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Many social media users express concerns about vaccines and their side effects on Twitter. These concerns lead to a compromise of confidence which brings about vaccine hesitancy. In Africa, vaccine hesitancy is a major challenge faced by health policymakers in the fight against COVID-19. Given that most tweets are geotagged, clustering them according to their sentiments could help identify locations that may likely experience vaccine hesitancy for health policy and planning. In this study, we collected 70 000 geotagged vaccine-related tweets in nine African countries, from December 2020 to February 2022. The tweets were classified into three sentiment classes—positive, negative, and neutral. The quality of the classification outputs was achieved using Naíve Bayes (NB), logistic regression (LR), support vector machines (SVMs), decision tree (DT), and K-nearest neighbor (KNN) machine learning classifiers. The LR achieved the highest accuracy of 71% with an average area under the curve of 85%. The point-based location technique was used to calculate the hotspots based on the locations of the classified tweets. Locations with green, red, and gray backgrounds on the map signify a hotspot for positive, negative, and neutral sentiments. The outcome of this research shows that discussions on social media can be analyzed to identify hotspots during a disease outbreak, which could inform health policy in planning and management of vaccine hesitancy in Africa. Author
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As the possibility of sharing inaccurate information on social media increases markedly during the health crisis, there is a need to develop an understanding of social media users' motivations for online sharing of information related to major public health challenges such as COVID-19. This study utilised an online survey based on Theory of Planned Behaviour and Diffusion of Innovation Theory to examine how the behavioural intention to share COVID-19-related content on social media is impacted and to develop a model of health information sharing. Results indicate that opinion leadership, beliefs held towards the source of the information, and peers' influence serve as determinants of the intention to share COVID-19-related information on social media, while the opinion-seeking attitude does not, which could be explained by opinion seekers' inherent tendency to seek more sources to verify new information obtained. The study contributes to the Information Science field by addressing the previously under-researched area and proposing a new model that explains the impact of the factors on behavioural intention to share health-related information during the health crisis in the online network environment. © 2022 World Scientific Publishing Co.
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This paper reports a method for automatically identifying, analyzing and explaining anomalies in different indexes of COVID-19 crisis using Artificial Intelligence (AI) based techniques. With systematic application of News sensor, language detection & translation, Keyword-based extraction of COVID-19 indexes, Convolutional Neural Network (CNN) based anomaly detection, and Natural Language Processing (NLP) based explanation methods, this paper demonstrates a methodological solution for strategic decision makers to make evidence-based policy decisions on COVID-19 (in multiple dimensions like Travel, Vaccine, Medical etc.). Firstly, COVID-19 related News is fetched from multiple sources in multiple languages. Then, AI-based language detection and translation process automatically translates these News and posts in real-time. Next, COVID-19 related News and posts are segregated in multiple groups using pre-defined keywords for creation of multiple indexes. Lastly, CNN based anomaly detection identifies all the anomalies on multiple COVID-19 indexes with NLP-based explanations. A standalone decision support system was developed that implemented the presented method. This decision support system allows a strategic decision-maker to comprehend "when, where, and why there are fluctuations in COVID-19 related sentiments on a particular dimension". Method was validated with Tweets from 15/072021 to 24/05/2022 resulting in automated generation of 5 COVID-19 indexes and 69 anomalies with explanations. In summary, this method of anomaly detection on COVID-19 indexes presents:â¢An explicit, transferable and reproducible procedure for detecting anomalies on multiple indexes of COVID-19 in multiple languagesâ¢It helps a strategic decision maker to comprehend the root-causes of anomalies in COVID-19 related travel, vaccine, medical indexesâ¢The solution developed using the presented method allows evidence-based strategic decision-making COVID-19 crisis using AI, Deep Learning and NLP.
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Objectives: Live-streaming fitness is perceived by the Chinese government as an invaluable means to reduce the prevalence of physical inactivity amid the COVID-19 pandemic. This study aims to investigate the public altitudes of the Chinese people toward live-streaming fitness and provide future health communication strategies on the public promotion of live-streaming fitness accordingly. Methods: This study collected live-streaming fitness-related microblog posts from July 2021 to June 2022 in Weibo, the Chinese equivalent to Twitter. We used the BiLSTM-CNN model to carry out the sentiment analysis, and the structured topic modeling (STM) method to conduct content analysis. Results: This study extracted 114,397 live-streaming fitness-related Weibo posts. Over 80% of the Weibo posts were positive during the period of the study, and over 85% were positive in half of the period. This study finds 8 topics through content analysis, which are fitness during quarantine; cost reduction; online community; celebrity effect; Industry; fitness injuries; live commerce and Zero Covid strategy. Conclusions: It is discovered that the public attitudes toward live-streaming fitness were largely positive. Topics related to celebrity effect (5-11%), fitness injuries (8-16%), live commerce (5-9%) and Zero Covid strategy (16-26%) showed upward trends in negative views of the Chinese people. Specific health communication strategy suggestions are given to target each of the negative topics.
Subject(s)
COVID-19 , Social Media , Humans , Pandemics , COVID-19/epidemiology , China , AttitudeABSTRACT
Newspapers and other mass media outlets are critical in shaping public opinion on a variety of contemporary issues, including the COVID-19 pandemic. This study examines how the pandemic is portrayed in the news and how the public reacted differently in the West and East using archival data from Facebook posts about COVID-19 news by English-language mass media between January 2020 and April 2022 (N = 711,646). Specifically, we employed the Valence Aware Dictionary and sEntiment Reasoner (Vader) to measure the news tone on each COVID-19 news item shared on Facebook by mass media outlets. In addition, we calculated a polarity score based on Facebook special reactions (i.e., love, angry, sad, wow, haha, and care) received by each post to measure public reactions toward it. We discovered that people in Western countries reacted significantly more negatively to COVID-19 news than their East counterparts, despite the fact that the news itself, in aggregate, generally contained a relatively similar level of neutral tone in both West and East media. The implications of these distinctions are discussed in greater detail.
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People receive a wide variety of news from social media. They especially look for information on social media in times of crisis with the desire to assess the risk they face. This risk assessment, and other aspects of user reactions, may be affected by characteristics of the social media post relaying certain information. Thus, it is critical to understand these characteristics to deliver information with the reactions in mind. This study investigated various types of imagery used as thumbnails in social media posts regarding news about the COVID-19 pandemic. In an experimental design, 300 participants viewed social media posts containing each of the three types of imagery: data visualization (directly about risk information), advisory (not containing direct risk information, but instead help on how to lower risk), or clickbait (containing no risk-related information, just generic visuals). After observing the social media posts, they answered questionnaires measuring their emotions (valence, arousal, and dominance), risk perception, perceived credibility of the post, and engagement. The participants also indicated their emotions towards and interest in COVID-19 news coverage, age, gender, and how often and actively they use social media. These variables acted as controls. The data were analysed using mixed linear models. Results indicated that advisory imagery positively influenced valence, arousal, dominance, credibility, and (lower) risk perception. Alternatively, imagery showing data visualizations yielded low valence, arousal, dominance, credibility, and high risk perception. Clickbait-styled thumbnails which carry no information are usually measured between the other two types. The type of imagery did not affect the motivation to engage with a post. Aside from visual imagery, most variables were affected by COVID sentiment and the usual activity on social media. These study results indicate that one should use advisory imagery for more comfortable news delivery and data visualization when the poster wishes to warn users of existing risks.
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Companies are increasingly using social media to communicate with stakeholders. During the last decade, social media started to become part and parcel of contemporary lifestyles. Thus, the main purpose of this research was the investigation of the impacts of social media on accounting and auditing by using companies’ social media posts. We performed quantitative research on an initial population of 183 companies being traded on the Athens Stock Exchange (ATHEX) for one fiscal year. We gathered data from corporate social media accounts and social media posts for the 2018 fiscal year (Twitter, Facebook and LinkedIn). We analyzed social media posts’ strategies, and we used the Kruskal–Wallis model and OLS regression model in order to analyze the relationships between social media accounts and posts and accounting and auditing. The findings from our research show that firms with active social media accounts and active impression management techniques on Twitter, Facebook and LinkedIn tend to achieve higher profits compared to companies that have inactive social media accounts. Additionally, the firm’s total liabilities are mainly positively related to its posts on specific social media accounts. In addition, cash, total assets and earnings before taxes affect social media posts to different degrees, depending on the post’s content and the category of social media as well. Taking into account the auditing variables, it is suggested that there is no relation among the given auditor’s opinion, the going concern assumption and the reviewed posts.
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This research shows that function words can be useful as features for machine learning models tasked with detecting conspiratorial content in COVID-19 related Twitter posts. A significance test exposes that the distribution of function words between fake and legitimate content varies greatly. Further, a support vector machine classifier is demonstrated to perform above chance when using function word-only features, achieving a Matthews correlation coefficient of 0.139 on unseen test data. Copyright 2021 for this paper by its authors.
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The wide spread pandemic COVID-19 has propelled the entire world to rely on social media interaction digitally. Social media is thus a platform to express numerous kinds of direct and indirect sentiments by human beings. Psychologically, a person tends to share his/her feelings in terms of sentiments more openly over the social media. These sentiments, when intense may polarize oneself to commit severe mis-deeds. Here arises the role of the researchers to perform a real time identification of sentiments and classify them so that a prospective mishap can be averted. In this work, an integrated framework is proposed that does an early recognition of sentiments over social media in the digital domain. Along with sentiment categorization, another module has been integrated to the framework to perform a post-predictive analysis of the same. The proposed integrated framework involves combination of two distinct mechanisms. First, the proposed work channelizes the input data in line with its characteristics text, image, and voice. The text input is directly fed to our proposed ‘Lexicon based LSTM with sentiment word mapping’ mechanism. From the input image, both text and semantics are extracted through two different blocks. One block converts image-to-text and redirects the output to the above proposed model. We proposed a new generative model (GM) to extract the semantics of the image and the second block utilizes our generative model and redirects the outcome straight to the final output buffer of the framework. The voice-to-text module has been used for transforming voice input data to text data which is redirected to our proposed Lexicon based LSTM for further processing. A comparison of the proposed work has been made with state-of-the-art techniques. Our results indicate that the overall rate of accuracy of this framework is superior to the existing methods. © 2022 Lavoisier. All rights reserved.
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SARS-CoV-2, or more popularly known COVID-19 has claimed more than 5.5 million lives since it has been declared as a global pandemic. Similar to other viruses, COVID-19 is also undergoing several mutations and has many variants like Alpha, Beta, Gamma, Delta, Omicron and others. With so many variants, social media users are confused and posting their frustrations and angers with Tweets or Posts in public social media platforms. These publicly accessible social media posts provide a wealth of information for a social scientist or political leader or a strategic decision maker. This study demonstrates a feasible approach to extract meaningful critical information from social media posts. By programmatically accessing Twitter database from 11th January 2022 till 20th January 2022, we retrieved almost 9 K Tweet messages on 6 different keywords like “COVID Variants”, “Omicron”, “Alpha Variant”, “Beta Variant”, “Gamma Variant” and “Delta Variant”. Results were compared against metrics like users, posts, engagement, and influence. Omicron was found to be the most popular topic compared to other variants with an influence score of 70.2 million and 2.1 K posts during the monitored period. The most popular sources for influences on COVID-19 Variant related posts were found to be @reuters with 24.2M, @forbes with 17.4M, @timesofindia with 14.2M and @inquirerdotnet with 3.4 followers. This study also found out that the most popular Tweet languages were English followed by French and Dutch. Lastly, this study ranked user mentions, word frequency (with word cloud) and hashtags for COVID-19 Variant related twitter posts during the monitored timeframe.
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This study used opinion mining theory and the potentials of artificial intelligence to explore the opinions, sentiments, and attitudes of customers expressed on Twitter regarding the services provided by the Saudi telecommunications companies during the COVID-19 crisis. A corpus of 12,458 Twitter posts was constructed covering the period 2020–2021. For data analysis, the study adopted a discourse-based mining approach, combining vector space classification (VSC) and collocation analysis. The results indicate that most users had negative attitudes and sentiments regarding the performance of the telecommunications companies during the pandemic, as reflected in both the lexical semantic properties and discoursal and thematic features of their Twitter posts. The study of collocates and the discoursal properties of the data was useful in attaining a deeper understanding of the users’ responses and attitudes to the performance of the telecommunications companies during the COVID-19 pandemic. It was not possible for text clustering based on the “bag of words” model alone to address the discoursal features in the corpus. Opinion mining applications, especially in Arabic, thus need to integrate discourse approaches to gain a better understanding of people’s opinions and attitudes regarding given issues © 2022. International Journal of Advanced Computer Science and Applications.All Rights Reserved.