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The proliferation of enterprise social media generates an ever-growing record of digital traces that provides ample opportunities to study the social making of organisations. Subsequently, we present the social fabric framework, which comprises a structured five-step approach for eliciting, interpreting, and representing the situated social idiosyncrasies and underlying patterns of the social making of organisations. The paper focuses on the application of the social fabric framework as a research method. However, the framework also lends itself to practice as a diagnostic tool that can detect emergent changes in the social fabric of an organisation as well as support organisational development and change. Moreover, by providing a vocabulary for articulating the social making of organisations, the framework can help organisation members reify their dispositions, make sense of the social dynamics, and enable a constructive discussion at the grassroots level about any controversy or aspiration.

With the rapid development of the Internet, readers tend to share their views and emotions about news events. Predicting these emotions provides a vital role in social media applications (e.g., sentiment retrieval, opinion summary, and election prediction). However, news articles usually consist of objective texts that lack emotion words, making emotion prediction challenging. From prior studies, we know that comments that come directly from readers are full of emotions. Therefore, in this article, we propose a deep learning framework that first merges article and comment information to predict readers' emotions. At the same time, in the prediction process, we design a pseudo comment representation for unpublished news articles by the comments of published news. In addition, a better model is required to encode articles that contain implicit emotions. To solve this problem, we propose a block emotion attention network (BEAN) to encode news articles better. It includes an emotion attention mechanism and a hierarchical structure to capture emotion words and generate structural information during encoding. Experiments performed on three public datasets show that BEAN achieves the state-of-the-art average Pearson (AP) and accuracy (Acc@1). Moreover, results on four self-collected datasets show that both the introduction of emotional comments and BEAN in our framework improve the ability to predict readers' emotions.

Recently, luxury fashion brands can webcast fashion shows and analyze market feedback from social media exposure (SME). See now buy now (SNBN), a new product introduction system that allows consumers to purchase products after fashion shows, has been deemed a revolutionary movement in the fashion industry. Whether SNBN can replace see now buy later (SNBL), the traditional system in which consumers purchase products several months after fashion shows, is unclear. In this article we evaluate the value of demand learning through SME for SNBL and SNBN. We develop a stylish two-period model to characterize SNBL and SNBN with SME. We find that when the feedback accuracy and holding cost are sufficiently low, SNBN performs better than SNBL with SME. Furthermore, for SNBL, when the consumer valuation is relatively low, learning market responses from SME is effective and profit improvement with SME decreases. For SNBN, SME is not always effective. Our results imply that brands, such as Gucci, should sell trendy and fashionable items via SNBL without SME and classic products (products with high consumer preference) via SME. Brands, such as Burberry, should sell low holding cost products (e.g., shirts) through SNBN and high holding cost products (e.g., leather bags) through SNBL.

OBJECTIVE: To assess participant attendance and engagement for an in-person Canadian Ophthalmological Society (COS) annual meeting (2019) compared with a virtual COS annual meeting (2020). DESIGN: Retrospective case-control study of key event metrics of the 2019 and 2020 COS meetings as well as Twitter activity. METHODS: Key annual meeting metrics were collected retrospectively for 2020 virtual meeting and compared with the most recent in-person annual meeting cohort from 2019. Metrics collected included attendance by ophthalmology specialist, geographic distribution of attendees, postevent survey rate, and social media engagement (Twitter). RESULTS: Overall, there was a 7% (n = 60) increase in the number of registrants between 2019 and 2020. The largest change noted was the increase in registrants from British Columbia (n = 78). More ophthalmologists registered for the 2020 meeting than for 2019 meeting (627 versus 592). Of those who registered for the meeting, meeting participation (defined as checking in for the 2019 and logging in for the 2020 meetings) increased from 70% in 2019 to 79% in 2020. There was a 158% (n = 15 000) increase in tweet impressions in 2020 compared with 2019. CONCLUSION: The first COS virtual meeting attracted more participants and was available to a geographically wider audience. Indeed, more professionals from provinces that are geographically further from the traditional COS meeting locations were able to participate in the event. Meeting engagement on a social media platform increased in the virtual meeting in 2020 relative to the in-person meeting in 2019, and possible enablers for increased engagement should be sought and incorporated into future meetings.

Background Traditional measures of evaluating scholarly output do not capture the impact social media can provide in disseminating and promoting research. We sought to better understand the level of online attention that high-quality hand research received. Methods Scientific manuscripts published from 2017 in Journal of Hand Surgery (American Volume) ( JHS-A), Journal of Hand Surgery ( European Volume) ( JHS-E), and HAND were recorded. Manuscript characteristics were determined, including the number of citations. Altmetric Attention Score (AAS), a measure of a manuscript’s online attention and impact, was determined, as well as Twitter mentions, Facebook mentions, and news outlet mentions. Spearman rho (ρ) correlation coefficient was used to evaluate the relationship between the number of citations and AAS. Multivariable linear regression analysis was performed to determine which manuscript factors were associated with AAS. Results A total of 323 manuscripts were identified. There was a weak positive correlation between the number of citations and AAS; however, this relationship did not exist for each individual journal. Publication in HAND and JHS-E were associated with lower average manuscript AAS when using JHS-A as the reference group. Two additional factors were also associated with increased manuscript AAS: (1) being a clinical study focused on a specific upper extremity anatomical location; and (2) increasing number of institutions on a study. Conclusions Publication in HAND and JHS-E were associated with lower manuscript AAS when using JHS-A as the reference group, suggesting that HAND and JHS-E have room for improvement in using social media to share their high-quality hand surgery scientific articles.

We sought to understand the mechanism underlying the growth trajectory in the United States Agricultural Safety and Health Centers YouTube channel. We also explored the benefits and limitations of using YouTube analytics to evaluate the impacts of public health interventions involving YouTube. Time series analysis of total views, total watch hours, average duration of watch time, and number of subscribers were assessed to determine the monthly patterns of non-seasonal and seasonal components in the data from 2013 to 2020. Health, safety, and animal handling video views were summarized descriptively across time and season. Lastly, time series regressions were used to determine the type of video that best predicted growth in the channel viewership metrics. The time series were not random but could be explained by autoregressive and moving average correlation structures. Health videos were the strongest predictors of future growth but were not the most watched type of video. Strong seasonality components indicated that videos were most watched during periods of high agricultural activity, but less so during the winter months. Generally, growth in YouTube viewership metrics was explained by past month viewership predicting future viewership. Outreach and media content may produce spikes of increased interest, but in order to sustainably grow the channel over time, Ag Centers and other agricultural stakeholders should continue to focus on the value of particular content to potential viewers, how and when content is released, and strategic promotion of the channel and its videos.

Objective: The difficulty of asking questions to each other during the appointment process with their doctor, the long waiting times of the patients in the waiting rooms in the clinics, and the fact that it is easier to get medical information on the internet due to the ongoing COVID-19 virus infection. Porcelain laminate veneers (PLVs) have been frequently used to restore anterior teeth for nearly three decades, thanks to their aesthetic appeal, durability and biocompatibility.Methods: YouTube™ was searched by a researcher of this study using the keyword 'laminate veneer' on April 17, 2021. The most frequently used terms in this regard are 'laminate veneer' and 'laminate veneer restoration'. Analyzes were made on the Instagram application, along with YouTube.Results: Thirty-three videos were determined on YouTube™ on the topic of laminate veneer. The mean number of the views was 46277.84 ± 128674.71, with the minimum view as ten and maximum as 682478. The videos had a mean duration of 5:48 ± 4:64 with a range of 0:24–14:55. The mean number of "likes" for each video was 198 ± 424, and the mean number of "dislikes" was 17 ± 48. The most-liked video received 1700 "likes." The most-disliked video received 236"dislikes." The mean number of comments was 13.65 ± 31.58. The interaction rate was 1.67 ± 5.18, and the viewing rate was 2939.11 ± 6076.75Conclusion: YouTube™ and Instagram still cannot be accepted as a completely confident resource of knowledge for patients who want to learn about oral aesthetic rehabilitation treatment with laminate veneers

This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

This study aimed to know factors influencing the voting preferences of the Millennial generations during the 2019 Philippine National Election. According to the official social media of the Commission on Elections (COMELEC), more than 61.8 million voters for the election belonged to the Millennial generation. Using a questionnaire survey, this study measured and correlated the respondents' profile, Candidate's Attribute, Endorsement Method, Platform and Campaign Strategy, and top ten characteristics of the candidates. These variables were all surveyed, tallied, and analyzed. The survey involved 138 college students who participated in the Voter’s Education Program led by COMELEC Taguig. This study reveals that the top three characteristics that the respondents wanted to see in a candidate are: not corrupt, wise, and a Godly person. In the future, this study can be replicated in a larger population of Millennials. It can be a great avenue of change in the political atmosphere of the Philippines. Finally, this study could bring new inspirations to those aspiring to run for public offices.

In this paper, we propose a Hierarchical Multimodal Variational Encoder-Decoder (HMMVED) to predict the popularity of micro-videos by comprehensively leveraging the user information and micro-video content in a hierarchical fashion. In particular, the multimodal variational encoder-decoder framework encodes the input modalities to a lower dimensional stochastic variable, from which the popularity of micro-videos can be decoded. Considering the important role of users social influence in social media for information dissemination, a user encoderdecoder is designed to learn the user Gaussian embedding from the user information, which is informative about the coarsegrained popularity for the micro-videos uploaded by the user. The learned user embedding then acts as the prior of the micro-video latent embeddings in the micro-video encoder-decoder, where the refined posterior embedding of the micro-video is obtained by taking the multimodal content into consideration. The finegrained popularity of each micro-video is generated from the posterior embedding of the micro-video. Based on the multimodal extension of variational information bottleneck theory, we show that the learned latent embeddings of micro-videos are maximally expressive about the popularity whilst maximally compressing the information from input modalities. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of the proposed method. Codes and datasets are available at: https://github.com/JennyXieJiayi/HMMVED.

The language employed by an individual when discussing topics of a moral nature (of the kind typically found in, e.g., social media) is revealing not only of the text affective contents itself, but also of the individual who wrote the text in the first place. Based on these observations, this work intends to illustrate how two kinds of morality-related information may be inferred from text by presenting a number of shallow and deep learning models of moral stance and moral foundations classification. In doing so, we introduce a novel corpus of texts labelled with moral foundation scores, and a novel approach to fine-grained, human-centric moral foundations classification that is, to the best of our knowledge, among the first NLP studies of this kind.

Recent statistics in suicide prevention show that people are increasingly posting their last words online and with the unprecedented availability of textual data from social media platforms researchers have the opportunity to analyse such data. Furthermore, psychological studies have shown that our state of mind can manifest itself in the linguistic features we use to communicate. In this paper, we investigate whether it is possible to automatically identify suicide notes from other types of social media blogs in two document-level classification tasks. The first task aims to identify suicide notes from depressed and blog posts in a balanced dataset, whilst the second experiment looks at how well suicide notes can be classified when there is a vast amount of neutral text data, which makes the task more applicable to real-world scenarios. Furthermore we perform a linguistic analysis using LIWC (Linguistic Inquiry and Word Count). We present a learning model for modelling long sequences in two experiment series. We achieve an f1-score of 88.26% over the baselines of 0.60 in experiment 1 and 96.1% over the baseline in experiment 2. Finally, we show through visualisations which features the learning model identifies, these include emotions such as love and personal pronouns.

Machine learning is the sub field of Artificial Intelligence which is responsible for developing a system that has the capability to learn and improve from past experience. There is an increasing demand for the offensive language detection because there is a large code-mixed text. Sometimes there is a possibility of code-mixed texts which are written using native scripts. If the system is getting trained on the data which is created using single language then it will leads to failure because of complexity. Using online communities and social media platforms has become increasing now. Recently many of the researchers have been investigating different ways of identifying the abusive content and developing systems to detect its different types like aggression identification, cyber bullying detection, hate speech identification, offensive language identification and toxic comments identification. Developing mechanisms to detect this is a challenging task. In the last few years, machine learning techniques and deep learning concept has been playing a significant role in the development of solutions for offensive language detection. So, considerable efforts have also been devoted to the development of machine learning techniques and deep learning models which is used to identify toxic content in recent years. First, this survey provides offensive language taxonomy and detection approaches. Then, the article focuses on the offensive language identification and toxic comments identification approaches. This article reviews various models used, type of learning model, languages for which the developed model is applied, other methods or approaches that were compared against this model, dataset used, offensive language detection type and performance metrics. The findings from the review are as well summarized and they urge that further effort is required to get improvement in the current state-of-the art approaches. This article also focuses on future work to improve the offensive language classification and toxic comments identification.

While customer loyalty is among the underlying reasons for servitized firms, the servitization–customer loyalty link is underexplored. In this article, informed by organizational information processing theory, we study the servitization–customer loyalty link and explore the joint effects of organizational practices on the link. We characterize servitization as services supporting products (SSP) and services supporting clients’ actions (SSC), and conceptualize organizational practices as organizational information processing capabilities comprising the use of different information technology (IT) tools, which include organizational IT (OIT) and social media (SM), and individual service-oriented cognition, which is manifested in service-oriented corporate culture (Culture). Based on a sample of 207 Chinese manufacturing firms, we find that while servitization does not necessarily enhance customer loyalty, successful servitization implementation depends on proper combinations of IT tools and Culture. Specifically, the SSP–OIT–Culture interaction and SSC–SM–Culture interaction are positively associated with customer loyalty. Our findings not only provide evidence against the conventional intuition that customer loyalty can be achieved by adopting servitization, but also explicitly show how to enhance customer loyalty by proper organizational practices, which provide new insights into both servitization and organizational information processing theory.

Purpose: Most young adult cancer survivors (YACS) do not meet physical activity (PA) guidelines. Although PA can improve health and quality of life (QOL), few randomized controlled trials (RCTs) of PA interventions for YACS exist. We conducted a pilot RCT to test feasibility of a PA intervention among YACS. Methods: We recruited 18-39-year-olds (≥1 and

Suicidal ideation detection from social media is an evolving research with great challenges. Many of the people who have the tendency to suicide share their thoughts and opinions through social media platforms. As part of many researches it is observed that the publicly available posts from social media contain valuable criteria to effectively detect individuals with suicidal thoughts. The most difficult part to prevent suicide is to detect and understand the complex risk factors and warning signs that may lead to suicide. This can be achieved by identifying the sudden changes in a user behavior automatically. Natural language processing techniques can be used to collect behavioral and textual features from social media interactions and these features can be passed to a specially designed framework to detect anomalies in human interactions that are indicators of suicidal intentions. We can achieve fast detection of suicidal ideation using deep learning and/or machine learning based classification approaches. For such a purpose, we can employ the combination of LSTM and CNN models to detect such emotions from posts of the users. In order to improve the accuracy, some approaches like using more data for training, using attention model to improve the efficiency of existing models etc. could be done. This paper proposes a LSTM-Attention-CNN combined model to analyze social media submissions to detect any underlying suicidal intentions. During evaluations, the proposed model demonstrated an accuracy of 90.3 percent and an F1-score of 92.6 percent, which is greater than the baseline models., 12 pages, 12 figures, 4 tables

Due to the Coronavirus (COVID-19) pandemic, which has resulted in tens of thousands of deaths and hundreds of thousands of infected cases, the international community has been confronted with one of the most urgent health crises in recent decades. With the COVID-19 pandemic, we confronted a new phenomenon known as an "Infodemic" or "epidemic of misleading information" regarding COVID-19. Presently, a large amount of unsubstantiated material on various elements of the COVID-19 disease, disease control and prevention techniques and its effects is being distributed via social media, news agencies and television networks. Due to their accessibility, social networks are increasingly becoming an integral part of our lives. They provide avenues where anybody can convey their ideas and post information without impeding or regulating their publishing validation. As a consequence, it facilitates the spread of "Fake News," material that is deliberately false. Because of the amount of social media users and the volume of followers, fake social media news could have major adverse social repercussions. Misinformation and disinformation can have detrimental effects on people's mental and physical health, increase stigma, jeopardize valuable health gains, and result in poor adherence to public health measures, hence reducing their efficiency and jeopardizing their ability to manage the pandemic. Misinformation can result in death. Without trust and accurate information, diagnostic tests go unutilized, immunization campaigns (or initiatives to enhance effectiveness of vaccines) fall short of their goals, and the virus thrives. What's more, disinformation is creating a rift in public debate on COVID-19-related issues, intensifying hate speech, increasing the chances of conflict, violent acts, and violations of human rights, and jeopardizing long-term prospects for advancing social cohesion, human rights, and democracy.

Event popularity prediction over social media is crucial for estimating information propagation scope, decision making, and emergency prevention. However, existing approaches only focus on predicting the occurrences of single attribute such as a message, a hashtag or an image, which are not comprehensive enough for representing complex social event propagation. In this paper, we predict the event popularity, where an event is described as a set of messages containing multiple hashtags. We propose a novel hashtag-influence-based event popularity prediction by mining the impact of an influential hashtag set on the event propagation. Specifically, we first propose a hashtag-influence-based cascade model to select the influential hashtags over an event hashtag graph built by the pairwise hashtag similarity and the topic distribution of event-related hashtags. A novel measurement is proposed to identify the hashtag influence of an event over its content and social impacts. A hashtag correlation-based algorithm is proposed to optimize the seed selection in a greedy manner. Then, we propose an event-fitting boosting model to predict the event popularity by embedding the feature importance over events into the XGBOOST model. Moreover, we propose an event-structure-based method, which incrementally updates the prediction model over social streams. We have conducted extensive experiments to prove the effectiveness and efficiency of the proposed approach.

There is an increasing interest in the study of detecting psychological stress from the social media like Twitter. However, Twitter has a limitation on the number of characters used per tweet, resulting in data sparsity. Many techniques were proposed to detect stress at the tweet level, but most of them failed to leverage the text content to reduce the impact of data sparsity. In this work, two solutions are proposed to further leverage the text content for the tweet level stress detection. First, a new feature, Sarcasm_Level, is computed to indicate the sarcasm present in the tweet’s content and its influence in detecting stress. Second, a novel neighborhood tweet-based stress detection method is developed, which is a logistic regression-based approach that integrates the content of previous tweets, also known as neighborhood tweets. Experimental results reveal that the proposed model has significantly better performance in detecting stress since it incorporates the information from neighborhood tweets. Also, with the inclusion of the new feature, the proposed model outperformed other well-known machine learning models like support vector machines, random forests and logistic regression with better accuracy and F1-score.

With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users' spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users' spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms. With the explosion of GPS-enabled smartphones and social media platforms, geo-social networks are increasing as tools for businesses to promote their products or services. Influence maximization, which aims to maximize the expected spread of influence in the networks, has drawn increasing attention. However, most recent work tries to study influence maximization by only considering geographic distance, while ignoring the influence of users' spatio-temporal behavior on information propagation or location promotion, which can often lead to poor results. To relieve this problem, we propose a Similarity-aware Influence Maximization (SIM) model to efficiently maximize the influence spread by taking the effect of users' spatio-temporal behavior into account, which is more reasonable to describe the real information propagation. We first calculate the similarity between users according to their historical check-ins, and then we propose a Propagation to Consumption (PTC) model to capture both online and offline behaviors of users. Finally, we propose two greedy algorithms to efficiently maximize the influence spread. The extensive experiments over real datasets demonstrate the efficiency and effectiveness of the proposed algorithms.

Mobile social networks include a large number of social members who forward messages cooperatively. However, spammers post links to viruses and advertisements, or follow a large number of users, which produces many misleading messages in mobile social networks. In this paper, we propose an adaptive social spammer detection (ASSD) model. We build a spammer classifier by using a small number of labeled patterns and some unlabeled patterns. The prediction accuracy is high compared with some conventional supervised learning methods. Moreover, the time and energy required to label the identity of social members are reduced by applying ASSD. Because social spammers frequently change their behavior to deceive the spammer detection model, an incremental learning method is designed to update the spammer detection model adaptively, without retraining. We evaluate ASSD by comparing it with other supervised and semi-supervised machine learning methods using the Social Honeypot Dataset. Experimental results show that the proposed model outperforms the baseline methods in terms of recall and precision. Additionally, ASSD maintains a high detection accuracy by adaptively updating the model with newly generated social media data.

In online peer-to-peer fundraising, individual fundraisers, acting on behalf of nonprofit organizations, mobilize their social networks using social media to request donations. Whereas existing studies focus on networks of donors to explain success, we examine the role of the networks of fundraisers and their effect on fundraising outcomes. By drawing on social capital and network theories, we investigate how social capital derived from social media networks and fundraising groups explains individual fundraising success. Using the Movember health campaign on Twitter as an empirical context, we find that fundraising success is associated with a moderate level of centrality in social media networks and moderate group network size. In addition, we find that fundraisers interact only marginally on social media but prefer to connect with each other outside these platforms and engage in group fundraising. Our article contributes to research on fundraising and social networks and provides recommendations for practice.

Influenced by digital technologies, the cost of media production has considerably decreased, and the traditional media is faced with new agile, flexible and low-cost media entrepreneurs. This article examines the dynamics of the Iraqi media market transformation with an emphasis on factors that help to merge media entrepreneurs and digital media firms that target an audience on social media. A qualitative method was adopted in this study using open, in-depth interviews with nineteen media entrepreneurs and three managers of media firms. The study revealed that relative freedom and advanced communication technologies have encouraged media entrepreneurs to drive the new media on producing short videos and broadcast them on social media, which has become popular among media consumers. This new era in Iraqi media entrepreneurship has created an abstract space in which media entrepreneurs get involved in the media market, collaborate with international media and deliver values through the use of user-generated content and flexible journalism. This opportunity is shaped by three key interrelated factors: first, the relative freedom of journalism that resulted from the political environment, current regulations and advanced communication technologies that provide more space of freedom; second, the development of communication technologies that allow journalists and media entrepreneurs to employ the media market effectively; third, the emergence of media entrepreneurs themselves who are convinced to seize the opportunities presented by the two previous factors.

Big data provided by social media has been increasingly used in various fields of research including disaster studies and emergency management. Effective data visualization plays a central role in generating meaningful insight from big data. However, big data visualization has been a challenge due to the high complexity and high dimensionality of it. The purpose of this study is to examine how the number and spatial distribution of tweets changed on the day Hurricane Harvey made landfall near Houston, Texas. For this purpose, this study analyzed the change in tweeting activity between the Friday of Hurricane Harvey and a typical Friday before the event.

BACKGROUND: The COVID-19 pandemic has caused isolation, fear, and impacted on maternal healthcare provision. AIM: To explore midwives' experiences about how COVID-19 impacted their ability to provide woman-centred care, and what lessons they have learnt as a result of the mandated government and hospital restrictions (such as social distancing) during the care of the woman and her family. METHODS: A qualitative interpretive descriptive study was conducted. Twenty-six midwives working in all models of care in all states and territories of Australia were recruited through social media, and selected using a maximum variation sampling approach. Data were collected through in-depth interviews between May to August, 2020. The interviews were recorded, transcribed verbatim, and thematically analysed. FINDINGS: Two overarching themes were identified: 'COVID-19 causing chaos' and 'keeping the woman at the centre of care'. The 'COVID-19 causing chaos' theme included three sub-themes: 'quickly evolving situation', 'challenging to provide care', and 'affecting women and families'. The 'Keeping the woman at the centre of care' theme included three sub-themes: 'trying to keep it normal', 'bending the rules and pushing the boundaries', and 'quality time for the woman, baby, and family unit'. CONCLUSION: Findings of this study offer important evidence regarding the impact of the pandemic on the provision of woman-centred care which is key to midwifery philosophy. Recommendations are made for ways to preserve and further enhance woman-centred care during periods of uncertainty such as during a pandemic or other health crises.

In recent days, social media, online services, smartphones, and the Internet of Things (IoT) produces large quantities of data every second. The generated data is structured, unstructured, or semi-structured and available in various formats. Therefore, traditional approaches are not sufficient to handle such kind of data effectively. High utility pattern mining is a famous study area of data analytics that incorporates utility measures to consider user-based constraints such as number of units and benefit, in addition to frequency statistics of datasets. It is also essential to make effective decisions, and their demand is increasing in the last decade. Several techniques have been suggested for utility-based frequent pattern extraction. However, these approaches are limited to data size and operate on standalone systems. We have proposed a parallel approach named distributed memory-optimized utility mining (DMOUM) for high utility-based frequent pattern mining to address this issue. A new pruning technique is implemented that greatly reduces processing time and memory use. The proposed approach can handle huge amounts of data, i.e., big data. DMOUM approach is implemented in cluster-node architecture using Spark. The results are validated on various real-time datasets and found proposed approach has better performance than cutting edge approaches in terms of run-time, memory consumption, and scalability measures. The proposed work will provide solutions to real-time issues such as health care, education, e-commerce, and so on.

The rise of “fake news” has become a major concern for social media platforms. In response, Facebook has proposed and tested the idea of users flagging and rating news articles and sources, much akin to how consumers rate products and services on the Internet. One obvious challenge with this crowdsourced rating approach is whether the users really know enough to rate news articles and sources. Perhaps, a side benefit of asking users to evaluate an article—and asking about their personal experience with the event described in the article—is making them realize that they do not know enough about the event to make an accurate judgment, thus pushing them to become more skeptical. We asked 68 social media users to assess the believability of 42 social media headlines. We found that, while users were generally more likely to believe articles that agreed with their point of view, asking users to rate pushed them to think more critically about the truthfulness of the articles. Moreover, once users had been asked to rate some articles, they remained critical of other articles as well, even without the rating prompt. Overall, our findings suggest that asking users to evaluate the truthfulness of articles may not only produce rating information that can be a useful reference at a later point in time but also have an immediate benefit of alerting users to think more critically about all articles they see.

Higher levels of trust in credible sources of information in times of crisis such as the current COVID-19 pandemic increase public compliance with official recommendations, minimizing health risks and helping authorities manage the crisis. Based on a national survey (N=1160), this article explores (a) actual levels of trust in various sources of information (government websites, legacy media, social media, and interpersonal communication) during the pandemic and (b) a number of predictors of such trust. Results show that during the period studied government websites were the most trusted source of information. Trust in an information source is correlated with consumption of COVID-19–related news from that specific source, media fact-checking, and self-perception about the incidence of COVID-19–related fake news. Only income and age are significant trust predictors, and only with respect to specific source types.

We live in an age of massive global disruption. Technological advancements threaten century-old business models, globalization is reordering supply chains, and people need to work with colleagues and customers who have vastly different backgrounds. On top of that, we have been in the midst of a global pandemic, and customers, employers, and investors are demanding more than just a Black Lives Matter social media post from organizations that purport to take social justice seriously. Organizations with high cultural intelligence (CQ) are able to navigate this volatility and complexity effectively. Over the last two decades, scholars from across the world have published hundreds of articles on CQ, the capability to relate and work effectively in complex, culturally diverse situations. Most of the work has examined CQ at the individual level. But what about organizations? Can organizations be culturally intelligent? The emerging research on CQ at the organizational level offers leaders and organizations critical insights for navigating today's diverse, digital world. Organizational CQ is a firm's capability to function effectively in a complex and unpredictable multicultural world. This article stresses the importance of the culturally intelligent organization and explains how to develop organizational CQ.

Although studies on digital labour platforms demonstrated how the internet has opened up access to income opportunities in the developing world, an exploration of how informal workers use the internet to access work without an intermediary is missing. Using data from digital ethnography and interviews with workers in Indonesia, this article examines how platform-based motorcycle taxi drivers and domestic workers accessed work through social media in the time of COVID-19 when the platforms were not allowed to operate. The evidence suggests that while social media can offer increased opportunities for workers, their success was largely dependent on their social networks and bounded by the algorithms designed by platform owners.

The use of influencers in marketing is not new. However, the advent of the internet, and more recently various forms of social media have both accelerated the use of influencers in marketing, and also democratized its use considerably. In this paper, we explore the nature of influence and power. We describe a study directed at understanding the promises and perils of influencer marketing from both the firm- and influencer perspectives, with the objective of identifying three key tensions that exist for marketers in the management of influencer relationships. We outline and explain these tensions and the lessons that can be learned from them, and then illustrate the lessons with a series of positive and negative cases. We close by speculating on the further evolution of influencer marketing.

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

Social media has become a vital platform for individuals, organizations, and governments worldwide to communicate and express their views. During the coronavirus disease 2019 (COVID-19) pandemic, social media sites play a crucial role in people communicating, sharing, and expressing their perceptions on various topics. Analyzing such textual data can improve the response time of governments and organizations to act on alarming issues. This study aims to perform sentiment analysis on the subject of COVID-19 vaccination, perform temporal and spatial analyses of the textual data, and find the most frequently discussed topics that may help organizations bring awareness to those topics. In this work, the sentiment analysis of tweets was performed using 14 different machine learning classifiers and natural language processing (NLP). Lexicon-based TextBlob and Vader are used for annotating the data. A natural language toolkit is used for preprocessing of textual data. Our analysis observed that unigram models outperform bigram and trigram models for all four datasets. Models using term frequency-inverse document frequency (TF-IDF) have higher accuracy than models using count vectorizer. In the count vectorizer class, logistic regression has the best average accuracy with 91.925%. In the TF-IDF class, logistic regression has the best average accuracy of 92%; logistic regression has the highest average recall, F1-score, and ten cross-validation scores, and a ridge classifier has the highest average precision. The unigram models show a standard deviation (SD) of less than 1 for all classifiers except for the Gaussian Naive Bayes showing 1.18. The experimental results reveal the dates and times in which most positive, negative, and neutral tweets are posted.

With the rapid development of web technology, social media platforms have become a breeding ground for rumors. These rumors can threaten peoples health, endanger the economy, and affect the stability of a country. In recent years, to mitigate the problem of rumors, computational detection of rumors have been studied, producing some promising early results. However, how to effectively capture the temporal information of retweet dynamics and the structural information of propagation structure are still neglected. In this paper, we innovatively propose a novel Multiview Structural-Temporal Learning Framework for Rumor Detection, MiSTR, to jointly learn the temporal features of retweet dynamics, structural features of propagation graph, and the textural features of source tweet. More specifically, we utilize the timestamp encoding, and timestamp level and sequential level attention mechanisms to learn the temporal correlation among individual retweets. We propose two specific methods to learn the overall representation of propagation structure among users from both microscopic and mesoscopic perspectives. Encouraging empirical results on three real large-scale datasets demonstrate the superiority of our proposed method over the state-of-the-art approaches.

Managerial relevance: In this competitive business world, social media penetrates online sales very rapidly. Businesses operating online are required to provide a safe and secured environment for customers to ensure their satisfaction. Our research results confirm that in addition to social media advertising, guaranteed 'e-safety' for customers will help establishing trust between the customer and the online businesses; it is also viewed important for present and future sales. The social media reviews need to be incorporated in managerial decision making. We try to analyse how these reviews are changing over the period of time and what are the ways to enhance customer satisfaction using information on customer expectations. This paper can help service companies to position their websites to compete in the market. Abstract In this era of social media, products and services are sold globally using a few simple clicks online. In such online purchases, trust and familiarity are considered two important driving forces of consumer decision making. While online sales advocate high levels of flexibility and choices for consumers, they also hold the online service provider responsible for ensuring the security of the online user's data. Using a Structural Equation Model (SEM) with data collected from the online service industry, we test the direct effects of 'social media-induced purchase intention' on customer satisfaction. We also test the mediating role of e-commerce/online sales (e-advertisement, e-safety and e-information) on customer satisfaction. In addition to social media advertising and information sharing, we find that a new factor-'e-safety'-mediates the relationship between customer purchase intention and customer satisfaction. Our analysis indicates that online e-trust can be established between the customer and the service company when online purchases are made. At the same time, the quality of online information and e-safety of online payments make the service company trustworthy for future purchases. We relate data analysis directly to managerial decision making to avoid any delay in online customer services in the era of social media.

Adolescents with cancer (AWC) in pediatrics may not have adequate opportunities for peer support. This mixed methods study aimed to characterize peer support needs of AWC during or shortly after treatment. Ten AWC with median age 16.5 (range 14-18) years completed a survey and semistructured interview. Three themes were apparent: cancer journey difficulties, current support system, and peer support perspectives. Participants felt incompletely understood by existing supports, lacked connection with other AWC, and craved experiential information. Peer support interventions should be flexible, facilitate various interactions, and include social media. The next step is to implement and evaluate an intervention.

Because of the recent surge in the number of unicorns and their role in spurring entrepreneurship and social impacts, venture capitalists, entrepreneurs, and regulators are concerned about how startup firms are valued. This is due to a lack of financial and historical information about startups as well as the need to understand their disruptive technological aspects. The length of observations is 2008–2018. The study uses the average value of variables for cross-sectional estimations. Our study investigates whether the types of recent technologies adopted by startups could serve as key factors to be used in startup valuations. Using a sample of 4903 startup in 13 subregions, we find that financial information (revenues) and nonfinancial information (social media) as well as sectoral and technological differences influence startup equity valuation. Technologies that involve big data, clean tech, mobile and augmented reality are prominent equity valuation premiums, regardless of the subsectors in which the startups originate. In addition, we find that e-commerce and mobile and big data are effective technologies for startup firms to employ for accumulating short-term capital.

Researchers around the world have been implementing machine learning as a method to detect cyberbullying text. The machine is trained using features such as variations in texts, through social media context and interactions in a social network environment. The machine can also identify and profile users through gender or use of hate speech. In this study, we analysed different types of mobile applications that manage cyberbullying. This study proposes a mechanism, which combines the best cyberbullying detection features to fill the gaps and limitations of existing applications. The results of the study have shown that the proposed mobile application records a higher accuracy in detecting cyberbully than other available applications.

The idea of ‘citizen sensing’ and ‘human as sensors’ is crucial for social Internet of Things, an integral part of cyber-physical-social systems (CPSS). Social media data, which can be easily collected from the social world, has become a valuable resource for research in many different disciplines, e.g. crisis/disaster assessment, social event detection, or the recent COVID-19 analysis. Useful information, or knowledge derived from social data, could better serve the public if it could be processed and analyzed in more efficient and reliable ways. Advances in deep neural networks have significantly improved the performance of many social media analysis tasks. However, deep learning models typically require a large amount of labeled data for model training, while most CPSS data is not labeled, making it impractical to build effective learning models using traditional approaches. In addition, the current state-of-the-art, pre-trained Natural Language Processing (NLP) models do not make use of existing knowledge graphs, thus often leading to unsatisfactory performance in real-world applications. To address the issues, we propose a new zero-shot learning method which makes effective use of existing knowledge graphs for the classification of very large amounts of social text data. Experiments were performed on a large, real-world tweet dataset related to COVID-19, the evaluation results show that the proposed method significantly outperforms six baseline models implemented with state-of-the-art deep learning models for NLP.

These days, social media users tend to express their feelings through sharing images online. Capturing the emotions embedded in these social images involves great research challenges and practical values. Most existing works concentrate on extracting the visual feature from a global view, while ignoring the fact that visual objects are also rich in emotion. How to leverage the multilevel visual features to improve the sentiment analysis performance is important yet challenging. Besides, existing works view each social image as an independent sample while ignoring the rich correlations among social images, which may be helpful in detecting visual emotion. In this article, we propose a novel model called social relations-guided multiattention networks (SRGMANs) to incorporate both the multilevel (region-level and object-level) visual features of a single image and the correlations among multiple social images to conduct visual sentiment analysis. Specifically, we first construct a heterogeneous network consisting of various types of social relations and introduce a heterogeneous network embedding method to learn the network representation for each image. Then, two visual attention branches (region attention network and object attention network) are devised to extract emotional and discriminative visual features. For each branch, we design a self-attention module to capture the emotional dependencies among visual parts. Besides, a network-guided attention module is also designed in each branch to focus on more network-related emotional visual parts with the guidance of the topology information. Finally, the attended visual features from the two attention models, together with network representation features, are combined within a holistic framework to predict the sentiment of social images. Extensive experiments demonstrate the superiority of our model on three benchmark datasets.

Recently, Social Media (SM)’s adoption as educational instruments in Higher Education (HE) has been gaining popularity among researchers and practitioners. Nonetheless it has been adopted to support various learning approaches such as Cooperative Learning (CL). Despite the growing research in this field, few studies have proposed a comprehensive SM integration framework as well as empirical studies to evaluate its effectiveness. This paper examines how SM is being used as a pedagogical tool in HE and identifies major factors that drives SM’s inclusion in HE classrooms. In addition, the paper investigates the effectiveness of a previously proposed integration framework which was developed to promote CL using a popular SM platform, LinkedIn. A survey was administered to collect data from students in different subjects at a university in Australia. Structural Equation Modelling (SEM) was adopted for data analysis using IBM’s SPSS26 and AMOS26. One of the notable results of this study is the significant positive relation found between the use of the adopted integration framework and CL, SM challenges’ mitigation, students’ intention to use SM and their learning process. The findings of this study have important implications for practice and pave the way for future empirical studies.

During the past quarter-century, digital technologies-based innovations for creating, communicating, and delivering products of value to customers have significantly risen in importance to the competitiveness of firms. Digital technologies-based innovations have been transformational in numerous ways, such as their impact on firms’ marketing behaviors, consumers’ search and buying behaviors, and the structural characteristics of markets and industries. Against this backdrop, this article provides a perspective on the evolution of research and practice in digital product innovations and digital marketing innovations. Specifically, the article focuses on (a) innovations for the greater good in the domain of the former and (b) direct and mediated communications through social media platforms and omnichannel marketing in the domain of the latter. In respect of each of the above, the article provides an overview of the evolution and current state of the field, highlights certain current issues and the trajectory of the field, and proposes directions for future research.

An influencer is an impactful content creator on social media. The emergence of influencers led to increased influencer marketing. The task of picking the right influencers is widely studied through influence maximization (IM). Existing IM studies have matured in terms of theoretical performance, but not very realistic in real-world. First, existing IM diffusion models didn't consider the engagement level and activeness of the users. Secondly, there were no studies that compare activated users against actual influenced users on Instagram. To address both problems, three new realistic diffusion models are proposed, based on the Independent Cascade and Linear Threshold models, namely IC-u, LT-u and UAD models. This study was implemented using Instagram data. Meanwhile, UAD model uses two thresholds, namely user's awareness, and user's tendency. These models incorporate user's activeness and engagement factors that represent the susceptibility to influence and the degree of influence, respectively. The proposed models were proven to be up to 2.72x more realistic and produced more engaging and more active users. The seeds set (influencers) identified by the IM algorithms under the proposed models are expected to have more impact on an actual brand marketing campaign if compared to existing models.

Objectives To perform a survey assessing the use of, attitudes towards, and perceived utility of social media (SoMe) in the 2021 urology residency match. Methods We distributed surveys to urology residency applicants and program directors (PDs) via the Urology Match 2021 Google Spreadsheet and email. The survey collected demographic information as well as SoMe activity, perceived pressure to use SoMe, match results, and attitudes regarding the utility of SoMe in the match process. Results A total of 108/528 (20%) applicants registered for the 2021 match and 61/142 (43%) PDs completed the survey. More applicants than PDs felt that SoMe helped them gain better insight into residency programs or applicants, respectively. Fewer applicants than PDs felt that SoMe activity provided a benefit to them in the match process. No significant relationship was found between SoMe viewing frequency, posting frequency, or tweetorial use with match outcomes. The majority of PDs believed that SoMe played a more important role in the 2021 match process than previous years while 15% and 12% reported that an applicant's SoMe activity helped or hurt the chances of matching to their program respectively. Conclusions SoMe, particularly Twitter, was widely used in the 2021 match by both applicants and PDs. A majority of applicants and PDs believed that SoMe use aided them in some way in the match process, yet there was no relationship between the volume or type of applicant SoMe activity and match outcomes.

Background The purpose of this investigation was to analyze the content and quality of YouTube videos related to shoulder arthroplasty (SA) and elbow arthroplasty (EA). In addition, we aimed to compare videos produced by academic sources to those that were produced by non-academic sources. We hypothesized that overall SA and EA YouTube video content and quality would be poor. Methods The most popular SA and EA YouTube videos were analyzed according to source and video characteristics were recorded. We determined video content and quality scores utilizing the Journal of the American Medical Association (JAMA) benchmark criteria, DISCERN criteria and a novel Arthroplasty Content Score. Results 84 and 34 unique SA and EA videos were included, respectively. Mean JAMA, DISCERN and Content scores were 1.2, 29.5, and 3.4 for SA videos and 0.9, 29.9, and 3.5 for EA, respectively. No videos were classified as “good” or “excellent” according to the DISCERN criteria. Mean quality and content scores were not significantly different for academic compared to non-academic sources for either SA or EA videos. Conclusions YouTube videos related to SA and EA are of poor quality and are unreliable sources of patient information. Videos from academic sources do not provide higher quality information than videos from non-academic sources. With the growing use of internet and social media for health information, surgeons should, at present, direct patients away from these sources. Upper-extremity surgeons and professional societies should endeavor to create quality, patient-directed educational videos related to SA and EA, as patients are increasingly seeking information from social media sources.