The impact of coronavirus on business: Developing service research agenda for a post-coronavirus world

Purpose: The coronavirus (COVID-19) has had a tremendous impact on companies worldwide. However, researchers have no clear idea of the key issues requiring their attention. This paper aims to close this gap by analysing all business-related posts on a coronavirus subreddit ("r/coronavirus") and identifying the main research streams that are guiding the research agenda for a post-coronavirus world. Design/methodology/approach: We use data from reddit, particularly the subreddit "r/coronavirus" to identify posts that reveal the impact of coronavirus on business. Our dataset has more than 200,000 posts. We used an artificial intelligence-based algorithm to scrape the data with business-related search terms, clean it and analyse the discussion topics. Findings: We show the key topics that address the impact of coronavirus on business, combining them into four themes: essential service provision, bricolage service innovation, responsible shopping practices and market shaping amid crisis. We discuss these themes and use them to develop a service research agenda. The results are reported against the backdrop of service research priorities. Originality/value: The study identifies four key themes that have emerged from the impact of coronavirus on business and that require scholarly attention. Our findings can guide service research with unique insights provided immediately after the coronavirus outbreak to conduct research that matters to business and helps people in vulnerable positions in a post-coronavirus world. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Measuring Network Polarization and Political Sectarianism During the 2020 Pandemic

Online social networks are at the limelight of the public debate, where antagonistic groups compete to impose conflicting narratives and polarize the discussions. This article proposes an approach for measuring network polarization and political sectarianism in Twitter based on user interaction networks. Centrality metrics identify a small group of influential users (polarizers and unpolarizers) who influence a larger group of users (polarizees and unpolarizees) according to their ideological stance (left, right, and undefined). This network polarization is computed by the Bayesian probability using typical actions such as following, tweeting, retweeting, and replying. The measurement of political sectarianism also uses Bayesian probability and words extracted from the tweets to quantify the intensity of othering, aversion, and moralization in the debate. We collected Twitter data from 33 conflicted political events in Brazil during 2020, strongly influenced by the COVID-19 pandemic. Based on our methodology and polarization score, our results reveal that the approach based on user interaction networks leads to an increasing understanding of polarized conflicts in Twitter. Also, a small number of polarizers is enough to represent the polarization and sectarianism of Twitter events. © 2014 IEEE.

On the online self as an extension of personhood

Social workers must adapt along with the technology that both they and their clients are using and utilize it as a tool for exploration of identity formation, recognizing unique experiences in the online realm shape their perceptions of themselves and the world around them. The speed at which global populations turned to the online world due to the COVID-19 pandemic, when they were perhaps underprepared to do so, has complicated the feelings about being digital and skewed the discussions of online life to focus on the struggles of lacking "normal" human interactions. Adolescents who have already for years been forming their identities through an online world are participating in similar experiential activities that the generations before them have, but the means and mode of doing so have changed. While for years, many people have tried to limit the amount of time and energy that they put into their online lives, the changing landscape has forced many who had little interest in living lives online to grapple with their identity in a virtual world. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Characterizing the nature of trust & misinformation on Twitter

We analyze a dataset from Twitter of misinformation related to the COVID-19 pandemic. We consider this dataset from the intersection of two important but, heretofore, largely separate perspectives: misinformation and trust. We apply existing direct trust measures to the dataset to understand their topology, and to better understand if and how trust relates to spread of misinformation online. We find evidence for small worldness in the misinformation trust network;outsized influence from broker nodes;a digital fingerprint that may indicate when a misinformation trust network is forming;and, a positive relationship between greater trust and spread of misinformation. © 2022 IEEE.

A Study on Information Disorders on Social Networks during the Chilean Social Outbreak and COVID-19 Pandemic

Information disorders on social media can have a significant impact on citizens' participation in democratic processes. To better understand the spread of false and inaccurate information online, this research analyzed data from Twitter, Facebook, and Instagram. The data were collected and verified by professional fact-checkers in Chile between October 2019 and October 2021, a period marked by political and health crises. The study found that false information spreads faster and reaches more users than true information on Twitter and Facebook. Instagram, on the other hand, seemed to be less affected by this phenomenon. False information was also more likely to be shared by users with lower reading comprehension skills. True information, on the other hand, tended to be less verbose and generate less interest among audiences. This research provides valuable insights into the characteristics of misinformation and how it spreads online. By recognizing the patterns of how false information diffuses and how users interact with it, we can identify the circumstances in which false and inaccurate messages are prone to becoming widespread. This knowledge can help us to develop strategies to counter the spread of misinformation and protect the integrity of democratic processes.

A motivation-based intervention to study loneliness: The gerontological user experience in learning on Facebook (GULF)

It is predicted that soon, as baby boomers continue their shift into retirement, the population age will soon hit its peak in the United States and across the globe (Mather & Kilduff, 2020). As seen with COVID-19, the United States is grossly underprepared for emergencies involving older adults, with excessive physical and mental health resources (Ranney et al., 2020). This is a concern compounded by the declaration by U.S. Surgeon General Vivik Murthy in 2017 that older adults were experiencing a loneliness epidemic that was impacting their mental and physical health. With the shortage in resources, one option to consider is examining existing resources to ensure they are fully utilized. One of those resources is technology-specifically, Facebook. Very few have examined what is motivating a person to use Facebook and the connection that may have to their loneliness. This dissertation fills that gap.The purpose of this study is to examine an older adult's motivation to use Facebook and how that may impact their experience concerning loneliness. This study's quasi-experimental design introduced an intervention to older adults (N = 19), compared to a control group (N = 22). The intervention was grounded in Ryan and Deci's (2000) motivation-oriented self-determination theory and guided by andragogical principles to guide the intervention using different Facebook features. Mean comparisons from pre- to posttest for the intervention showed significant growth in motivation, with nonsignificant decreased levels of loneliness. The control group had unremarkable decreases in motivation over time. Interaction effects, however, suggest that competence was significant between groups from pre- to posttest. These findings provide additional information into the relationship between older adult Facebook users and how the use of Facebook, and technology, may impact the lives of older adults, in consideration of future resource use. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Controlling of Fake Information Dissemination in Online Social Networks: An Epidemiological Approach

Due to the fast advancement of Internet technology, the popularity of Online Social Networks (OSN) over the Internet is increasing day by day. In the modern world, people are using OSN to communicate with others around the world who may or may not know each other. OSN has become the most convenient means to transmit media (news/content) and gather or spread information in the world. The posts (contents) on OSN affect and impact people, and minds at least for some time. These contents are important because they play a crucial role in taking the decision. The posts which are available on the OSN may be information or just misinformation. The misinformation may be a type of fake news or rumour. This is very difficult for people to differentiate whether the posts are information or rumour. Therefore, the development of techniques that can prevent the transmission of false information or rumours that might harm society in any way is critical. In this paper, a model is developed based on the epidemic approach, for examining and controlling fake information dissemination in OSN. The proposed model illustrates how different misinformation debunking measures impact and how misinformation spreads among different groups. In this article, we explain that the proposed model will be able to recognize and eradicate fake news from OSN. The model is written as a system of differential equations. Its equilibrium and stability are also carefully examined. The basic reproduction number $(R_{0})$ is calculated, which is an important parameter in the study of message propagation in OSN. If $R_{0} < 1$ , the propagation of rumor in the OSN will be minimal;nevertheless, if $R_{0} > 1$ , the fake information/rumor will continue in OSN. The effects of disinformation of rumours in OSN in the real world are explored. In addition, the model covers the fake information/rumour dissemination control mechanism. The comparative study shows that the proposed model provides a better mechanism to prevent the dissemination of fake information in OSN in comparison to other previous models Extensive theoretical study and computation analysis have also been used to validate the proposed model © 2013 IEEE.

A framework to evaluate trustworthiness in online video-based information environments

Online misinformation has been shown to be a significant threat, with measurable real world impact. This has become especially evident during the COVID-19 crisis, where online spaces saw the propagation of false or inaccurate information on healthcare, protective equipment, vaccines, and more;causing major public health repercussions. Although many video-based online platforms are used as vehicles of such misinformation and are counted as some of the most popular and influential social media platforms, current research faces a lack of systemic methodology to analyze such content and detect potential misinformation efforts. This work proposes a solution by introducing an adaptable framework. This framework provides indicators of potential misinformation campaigns, addresses issues of large data volumes, and takes into consideration multiple classes of features such as media content, engagement, and user networks. The goal of this research is to integrate into larger, user-facing systems and help members of the information community, data scientists, journalists, or policy makers to make sense of impossibly large information environments and take action based on reliable data. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Insta-muscle: Examining online and offline IPED trade and masculine body culture

Empirical evidence suggests that we are witnessing a rise in the use of image and performance enhancing drugs both nationally and internationally (Sagoe et al., 2014;Mullen et al., 2020) which, despite the COVID-19 pandemic, shows little sign of halting. Set against this context, this thesis interrogates the consumption and supply of IPEDs within the post-industrial city of Stoke-on-Trent, as well as the digitised spaces of the social media sites (SNS) Facebook and Instagram. Underpinned by a twelve-month 'connective' ethnography, the work employs cutting-edge criminological theory to identify Stoke's health and fitness industry as a site of deviant leisure (Smith and Raymen, 2018). Through data precured from enactive fieldwork in two gyms, semi-structured interviews, and digital ethnographic observations, it presents a multi-faceted account of IPED consumption, taking in a psychoanalytic exploration of bodily desire, elements of instrumental and hyper-conformist use, the pleasures of lifestyle enhancement, and the role of SNS as 'dopogenic environments' (Backhouse et al., 2018).Building upon this, the thesis then offers a comprehensive account of IPED supply in the city. First identifying underground laboratories (UGLs) as the most common producers of IPEDs in the UK, the work paints a picture of the local 'partial' market (Fincoeur et al., 2015). Within this, the sanctity of bodily and cultural capital is discussed alongside the barriers that preclude external actors from accessing the supply chain. However, the research also identifies a concerted move towards commercialisation and digitisation, wherein the market now caters for less culturally embedded users and has in some respects moved online (Hall and Antonopoulos, 2016). The impact of these shifts is made clear in a discussion of the IPED market on both Facebook and Instagram.Ultimately, the research offers an original empirical and theoretical account of the image and performance enhancing drugs market. The findings bring us closer to a more theoretically nuanced account of IPED consumption, as well as building on the burgeoning body of work on the marketplace for these substances. This will be of use to academics, practitioners and policymakers. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

An investigation into the use of a Facebook group by secondary English teachers during a pandemic

This study focuses on a Facebook group utilized by secondary English teachers during the initial crisis period of COVID 19, defined as March 2020-June 2020. During this period, teacher participants used this Facebook group as a community of practice to re-envision pedagogy, using social media as a third space in which to have discussions with other teachers, either to seek help or to share resources. After a qualitative content analysis of 630 initial posts, 14,500 comments, and 13,539 reactions, three themes were determined. Teachers used the Facebook group to re-envision pedagogy by discussing texts and related activities, teachers sought strategies for lessons to implement during a pandemic;by offering a forum for discussion about ethical considerations of social justice and school responsibility, the teachers sought a space to talk openly about how to respond to current events;and by serving as a space for solidarity and support among fellow English teachers, the teachers supported each other through change. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Disentangling the Information Flood on OSNs: Finding Notable Posts and Topics

Online Social Networks (OSN s) are an integral part of modern life for sharing thoughts, stories, and news. An ecosystem of influencers generates a flood of content in the form of posts, some of which have an unusually high level of engagement with the influencer's fan base. These posts relate to blossoming topics of discussion that generate particular interest among users: The COVID-19 pandemic is a prominent example. Studying these phenomena provides an understanding of the OSN landscape and requires appropriate methods. This paper presents a methodology to discover notable posts and group them according to their related topic. By combining anomaly detection, graph modelling and community detection techniques, we pinpoint salient events automatically, with the ability to tune the amount of them. We showcase our approach using a large Instagram dataset and extract some notable weekly topics that gained momentum from 1.4 million posts. We then illustrate some use cases ranging from the COVID-19 outbreak to sporting events. © 2022 IEEE.

ICT in the university environment: Twitch and Instagram as learning tools

In recent days, Information and Communication Technologies (ICT) have been incorporated into the teaching-learning process. The study aims to achieve greater student engagement, and satisfaction using Instagram and Twitch platforms as methodological tools in teaching. The sample collects 50 students from the degree of a primary school teacher at the University of Valencia. The evaluation instrument has been based on direct observation and the use of the survey Barrado et al. (1999), evaluating the practical program, and the degree of satisfaction of the students. The results show the benefits of accessing learning at any time. Active participation in the platforms has made possible the involvement of students, receiving immediate feedback, and allowing them to build continuous learning. IEEE

Research on the propagation and governance of public opinion information under the joint action of internal and external factors

PurposeUnder the new media environment, while enjoying the convenience brought by the propagation of public opinion information (referred to as public opinion), learning the evolution process of public opinion and strengthening the governance of the spreading of public opinion are of great significance to promoting economic development and maintaining social stability as well as effectively resisting the negative impact of its propagation.Design/methodology/approachThinking about the results of empirical research and bibliometric analysis, this paper focused on introducing key factors such as information content, social strengthening effects, etc., from both internal and external levels, dynamically designed public opinion spreading rules and netizens' state transition probability. Subsequently, simulation experiments were conducted to discuss the spreading law of public opinion in two types of online social networks and to identify the key factors which influencing its evolution process. Based on the experimental results, the governance strategies for the propagation of negative public opinion were proposed finally.FindingsThe results show that compared with other factors, the propagation of public opinion depends more on the attributes of the information content itself. For the propagation of negative public opinion, on the one hand, the regulators should adopt flexible guidance strategy to establish a public opinion supervision mechanism and autonomous system with universal participation. On the other hand, they still need to adopt rigid governance strategy, focusing on the governance timing and netizens with higher network status to forestall the wide-diffusion of public opinion.Practical implicationsThe research conclusions put forward the enlightenment for the governance of public opinion in management practice, and also provided decision-making reference for the regulators to reasonably respond to the propagation of public opinion.Originality/valueOur research proposed a research framework for the discussion of public opinion propagation process and had important practical guiding significance for the governance of public opinion propagation.

Predictors of depression during the COVID-19 global pandemic when using social media platforms

The overall impact and consequences of the coronavirus (COVID-19) pandemic has had an unimaginable and lasting influence on everyone worldwide. Since the start of the pandemic, people around the world have been forced into isolation and lockdowns for long periods of time, which has resulted in adverse psychological consequences for many people. The purpose of this study was to explore and identify how different predictors influence depression during the COVID-19 pandemic among adult social media users. The researcher used a combination of the uses and gratifications theory and the social comparison theory as a theoretical framework for this study. An online survey was conducted with a sample of 215 valid responses from participants around the world. The results demonstrated that increased COVID-19 anxiety was associated with higher levels of depression. The results also demonstrated that positive social comparison to other people was associated with lower levels of depression. The most significant result was that increased time spent on Facebook resulted in a reduction of depression for people who had favorable views of themselves. (PsycInfo Database Record (c) 2022 APA, all rights reserved)

Explaining news spreading phenomena in social networks: From data acquisition and processing to network analysis and modelling

When a high-ranking British politician was falsely accused of child abuse by the BBC in November 2012, a wave of short messages followed on the online social network Twitter leading to considerable damage to his reputation. However, not only did the politician's image suffer considerable damage, moreover, he was also able to sue the BBC for 185,000 in damages. On the relatively new media of the internet and specifically in online social networks, digital wildfires, i.e., fast spreading, counterfactual or even intentionally misleading information occur on a regular basis and lead to severe repercussions. Although the example of the British politician is a simple digital wildfire that only damaged the reputation of a single person, there are more complex digital wildfires whose consequences are more far-reaching. This thesis deals with the capture, automatic processing, and investigation of a complex digital wildfire, namely, the Corona and 5G misinformations event - the idea that the COVID-19 outbreak is somehow connected to the introduction of the 5G wireless technology. In this context, we present a system whose application allows us to acquire large amounts of data from the online social network Twitter and thus create the database from which we extract the digital wildfire in its entirety. Furthermore, we present a framework that provides the playing field for investigating the spread of digital wildfires. The main findings that emerge from the study of the 5G and corona misinformation event can be summarised as follows. Although published work suggests that a purely structure-based analysis of the information spread allows for early detection, there is no way of predictively labelling spreading information as probably leading to a digital wildfire. Digital wildfires do not emerge out of nowhere but find their origin in a multitude of already existing ideas and narratives that are reinterpreted and recomposed in the light of a new situation. It does not matter if ideas and explanations contradict each other. On the contrary, it seems that it is the existence of contradictory explanations that unites supporters from different camps to support a new idea. Finally, it has been shown that the spread of a digital wildfire is not the result of an information cascade in the sense of single, particularly influential short messages within a single medium. Rather, a multitude of small cascades with partly contradictory statements are responsible for the rapid spread. The dissemination media are diverse, and even more so, it is precisely the mix of different media that makes a digital wildfire possible. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Contact Tracing Platform in OSN for Prevention of Infectious Disease Outbreaks

To limit the spread of COVID-19, social distancing measurements and contact tracing have become popular strategies implemented worldwide. In addition to manual contact tracing, smartphone-based applications based on proximity detection have emerged to speed up the discovery of potential infectious individuals. However, so far, their effectiveness has been limited, mainly due to privacy issues. A new tracing mechanism is represented by Online Social Networks (OSNs), which provide a successful way to track, share and exchange information in real-time. Being extremely popular and largely used by citizens, OSNs are less exposed to privacy concerns. In this paper, we present an OSN-based contact tracing platform called TraceMe to reduce the spread of the epidemic. The proposal currently targets COVID-19, but it can be used in presence of other infectious diseases, like Ebola, swine flue, etc. TraceMe implements conventional contact tracing based on physical proximity and, in addition, it leverages OSNs to identify other contacts potentially exposed to the virus. To efficiently find the targeted social community, while saving the time complexity, a clique-based method is applied. Performance evaluation based on a realistic dataset shows that TraceMe is able to analyse large-scale social networks in order to find, and then alert, the tight communities of contacts that are at high risk of infection. © 2023 IEEE.

Exploring the Relationship Between Student Engagement and Authentic Learning During COVID-19

Social networking sites (SNSs) such as WeChat or Facebook can facilitate university students in learning, especially during a deadly epidemic period such as COVID-19. Student engagement is a challenging task for educators in internet-enabled technology-enhanced learning platforms. This research attempts to identify the relationship between student engagement and authentic learning during COVID-19 through the theory of planned behavior (TPB) as a theoretical base. Quantitative data were collected (n = 285) using an online survey technique with the students from a recognized university in China. All six proposed hypotheses, including a moderating and two mediating variables, were found to be supported. The findings indicated that constructs such as affective engagement (AE) and social engagement (SE) are significant predictors of social interaction (SI) that may lead to accomplish authentic learning task (ALTask). Further, lack of attention (LAN) was found to significantly moderate social interaction and authentic learning tasks during COVID-19. (PsycInfo Database Record (c) 2023 APA, all rights reserved)

Hybrid Approach for Detecting Propagandistic Community and Core Node on Social Networks

People share their views and daily life experiences on social networks and form a network structure. The information shared on social networks can be unreliable, and detecting such kinds of information may reduce mass panic. Propaganda is a kind of biased or unreliable information that can mislead or intend to promote a political cause. The disseminators involved in spreading such information create a sophisticated network structure. Detecting such communities can lead to a safe and reliable network for the users. In this paper, a Boundary-based Community Detection Approach (BCDA) has been proposed to identify the core nodes in a propagandistic community that detects propagandistic communities from social networks with the help of interior and boundary nodes. The approach consists of two phases, one is to detect the community, and the other is to detect the core member. The approach mines nodes from the boundary as well as from the interior of the community structure. The leader Ranker algorithm is used for mining candidate nodes within the boundary, and the Constraint coefficient is used for mining nodes within the boundary. A novel dataset is generated from Twitter. About six propagandistic communities are detected. The core members of the propagandistic community are a combination of a few nodes. The experiments are conducted on a newly collected Twitter dataset consisting of 16 attributes. From the experimental results, it is clear that the proposed model outperformed other related approaches, including Greedy Approach, Improved Community-based 316 Robust Influence Maximization (ICRIM), Community Based Influence Maximization Approach (CBIMA), etc. It was also observed from the experiments that most of the propagandistic information is being shared during trending events around the globe, for example, at times of the COVID-19 pandemic.

Understanding conflict origin and dynamics on Twitter: A real-time detection system

Social Network Sites provide a venue for people worldwide to share their point of view and interact with each other, offering a virtual space with freedom for expressing ideas and opinions. The interaction dynamics often creates clusters of users sharing similar interest and opinions, thus creating an information bubble or echo chamber. In certain topics, such as politics, different groups tend to collide and start arguments characterized by conflicts of opinion. This fact has been increasingly observed during the COVID-19 pandemic, fed by misinformation and anti-science movements. One approach to address these issues is to use statistical measures of the posts revolving around the topic of interest, such as the number of shares, likes , and replies. In this paper we propose a methodology to extract a feature set from trending topics of the Twitter social network and apply two white-box models, a Symbolic Regression, named ITEA, and a Decision Tree, for the automated detection and understanding of conflicts. Our experiments show that both models obtain close extrapolation accuracy to the baseline black-box model (Random Forest). As a highlight of this paper, both white-box models are fully described to be used by any practitioner. Additionally, the model created by ITEA allows us to extract some insights from the generated models. Although these models do not allow for a complete comprehension of the dynamics of a conflict, it certainly points towards a direction for a more thorough investigation. • Classifiers to detect conflicts of opinion on Twitter using statistical metrics. • Use of transparent models capable of helping to understand the dynamics of conflicts. • Analysis of the conflict dynamics using the generated transparent models. [ FROM AUTHOR]

Following Negationists on Twitter and Telegram: Application of NCD to the Analysis of Multiplatform Misinformation Dynamics

In this paper we present an analysis of misinformation cross-platform dynamics by focusing on communications published by COVID19 negationists on Twitter and Telegram. Previous research shows the need for better explanations of the way misinformation travels across platforms. Here we pay attention to communities of users vulnerable to negationism, which refers to the tendency to revise history in order to omit something that actually happened. We start from searching specific key words previously identified by experts and used by negationists in Telegram channels. We retain only those public Telegram channels where those keywords are used. Then, we search on Twitter for those users who reference those Telegram channels. This way we obtain a list of potential Twitter negationist accounts and correct for false positives. We use the normalised compression distance (NCD) technique to reduce this error, while performing authorship attribution. We extract images and news domains shared by Twitter negationist accounts identified with NCD and by Telegram accounts initially identified;then we perform a reverse image search to identify other Twitter accounts that have used those images. We search in Telegram where those news domains appear and extract those Telegram channels and compare them with the original channels identified. The procedure is semi-automatic to ensure human supervision as required by The Assessment List on Trustworthy Artificial Intelligence (ALTAI). As discussed in the end, results are promising and motivate further research about the use of NCD to automate the identification of accounts spreading misinformation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.