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Objectives: To select the best papers that made original and high impact contributions in human factors and organizational issues in biomedical informatics in 2021. Methods: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2021 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 3,206 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams. Results: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces and mobile health. This year three papers were clearly outstanding and help advance in the field. They provide examples of examining novel and important topics such as the nature of human-machine interaction behavior and norms, use of social-media based design for an electronic health record, and emerging topics such as brain-computer interfaces. thematic development of electronic health records and usability techniques, and condition-focused patient facing tools. Those concerning the Corona Virus Disease 2019 (COVID-19) were included as part of that section. Conclusion: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.

This position paper sets out the rationale, logistics and limitations for delivery of COVID-19, MMR and meningitis vaccines to university students. Key aspects considered are ascertainment of vaccine status and campus-based delivery of vaccines. The approaches described in this position paper require an inter-disciplinary approach. There will be a requirement for data analysts to enable personalised, secure access to vaccination records, for social media experts and sociologists to design appropriate pathways and literature for disseminating information and for public health experts to oversee literature outputs and delivery of campus-based immunisation programmes. Contributions from vaccinologists, economists and modellers will also be required for monitoring and assessing the utility of implemented recommendations.

This article considers the approach taken by the Online Harms White Paper to constructing a statutory duty of care and argues that while not explicit the approach taken is consistent with a ‘by des...

Progress toward improvement in cancer therapy relies on clinical trials. Yet, only a minority of eligible patients with cancer enroll as a result of multiple barriers at the patient, investigator, center, and national level. However, the rise of the Internet and mobile technology has created a slew of tools with medical applications, from Web sites to apps to social media platforms, all of which may aide clinicians in our quest to improve the clinical research enterprise. SWOG is one of five members in the National Cancer Institute’s National Clinical Trials Network—the nation’s oldest and largest publicly funded cancer research network—and is taking a leadership role in exploring and testing the promise of digital engagement through the empaneling of the Digital Engagement Committee. This article outlines the mission, principles, and priorities of the Digital Engagement Committee and proposes how this work may inform the use of digital tools for the cancer research community and, hopefully, translate to improved outcomes for our patients.

In this study, altmetrics for LIS research papers, and whether OA affects the altmetrics, were analyzed. In addition, by analyzing the differences in the altmetrics in 10 major research areas of LIS, this study identified sub-research topics that are frequently read outside of academia or mentioned on social media. This study sampled a thousand papers highly cited in the WOS, OA status and altmetrics of papers were collected through Unpaywall and PlumX. The collected data was analyzed as follows. Whether the OA affects the usage, capture, mention, and social media attention of the altmetrics was identified through a Mann–Whitney analysis, and was presented visually through a correspondence analysis. And then using factor analysis and correlation analysis, this study understand whether the article cited in an academic paper or bookmarked for reading showed a sensitive reaction to social media as well. Finally to understand the difference in altmetrics sensitivity of 10 LIS sub-subject topics, papers showing a high sensitivity for each of the 6 sources were selected, and a correspondence analysis was conducted on the relationship with the subject topics. The analysis results can be summarized as follows: First, 63% of the papers to be analyzed had a Mendeley bookmark reader, whereas 36%, 17%, 3%, and 4% had views, tweets, blogs, and Wiki references, respectively. Second, views, blogs, and tweets showed greater sensitivity in open-access papers, and the number of Mendeley bookmark readers was the only source of altmetrics that had a significant correlation with citations. Fourth, information technology and knowledge management topics showed a high number of citations and readers, and public libraries and websites appeared as research topics with a significant usage. In addition, academic communication appeared as a topic with high sensitivity to tweets.

Social media platforms have had an enormous impact on the dissemination of scientific work and have fared well in covering scientific papers. However, little is known about the general dissemination process from academia to social media and how various factors affect the dissemination of scientific papers at different stages. In this paper, we proposed a two-staged dissemination process to profile the diffusion of scientific papers from academia to social media. A two-step simultaneous equation modeling–artificial neural network approach was adopted to predict the retweet scale of scientific papers on Twitter by combining source-related and content-related factors. The analysis in the field of oncology suggests that the artificial neural network algorithm (ANN) with the input units generated from the simultaneous equation model (3SLS) can predict the retweet scale of scientific papers on Twitter with an accuracy of 78.05%. According to the normalized importance obtained from the ANN, we found that most factors related to the information source play critical roles in promoting the dissemination of scientific papers. The number of first-generation tweets has the most remarkable impact on subsequent dissemination. As for the content-related predictors, tweets attached with more URLs can provide richer information for audiences, thereby increasing the retweet scale of scientific papers. Besides, the influence of research topics on dissemination varies with different audiences. The findings of this study contribute to the literature on the dissemination of scientific papers beyond academia and provide practical implications for scholarly communication.

“To follow and to be followed” has become the new normal in news communication in the age of social media. News audience follow news via social media while they are being followed by news anytime anywhere. This new normal has created a pressing need to investigate whether social media have brought any changes to both party-controlled and market-oriented news media in China in reporting crises. Comparing Xinhua News Agency (party-controlled) and The Paper (market-oriented), this study investigated how they reported COVID-19 and how their news consumers engaged with their COVID-19 news stories on Jinri Toutiao, a popular and yet special form of social media. This study found that Xinhua News Agency continued to stay overwhelmingly positive, while The Paper was more neutral in reporting the health crisis. Xinhua News Agency was surprisingly more episodic than The Paper in framing the pandemic. The Paper, however, had a higher level of user engagement than Xinhua News Agency. To cater to the changing news-seeking behaviors and patterns, both party-controlled and market-oriented news media have changed their operations, but not their fundamental orientations.

This paper describes a user identification method conducted across online social networks (OSNs) using information regarding friends’ locations, in contrast to a conventional method based on the similarity of two display names. This latter method encounters the problem of decreased identification accuracy if a user registers different display names across OSNs. Our proposed method aims at addressing this issue by utilizing information on friends’ locations. We convert location information extracted from an OSN to that of such geographic administrative units as country, state and city, and calculate the weighted occurrence frequency of a location pair on the basis of distance between the pair. This is based on the hypothesis that a friend list relatively rarely includes an account pair whose locations are extremely distant from one another. We calculate a similarity score between two accounts’ weighted occurrence frequencies for each geographic administrative unit. Finally, we identify a user on the basis of individual similarity scores or weighted average scores for a given geographic administrative unit. Evaluation experiments show that our proposed method improves performance over that of a conventional method (accuracy: 83.1%$\rightarrow$91.2%).

In traditional research, the application of quantitative measurement on the value and influence of uncited papers is not feasible owing to the lack of data. The rapid advancement of social media has offered a new path for the dissemination of scientific papers and gain influence. In this thesis, the uncited papers published during 2006–2014 with a 5-year window on PLOS ONE journals from the Web of Science core collection was selected. A measurement model was built for the influence of uncited papers on social media dimension based on its Discussed, Saved, and Viewed data to explore the dynamic evolution track of the influence and determine the patterns of interaction. We found that uncited papers have a universal and significant influence on social media platforms. Besides, the evolution pattern of influence of the uncited papers can be illustrated by the layer-to-layer aggregation model of “document properties → three indicators → influence” Overall, the findings of this thesis are referential to the quantitative measurement of the influence of uncited papers.

Purpose The rapid spread and severity of the coronavirus (COVID-19) virus have prompted a spate of scholarly research that deals with the pandemic. The purpose of this study is to measure and assess the coverage of COVID-19 research on social media and the engagement of readers with COVID-19 research on social media outlets. Design/methodology/approach An altmetric analysis was carried out in three phases. The first focused on retrieving all papers related to COVID-19. Phase two of the research aimed to measure the presence of the retrieved papers on social media using altmetric application programming interface (API). The third phase aimed to measure Mendeley readership categories using Mendeley API to extract data of readership from Mendeley for each paper. Findings The study suggests that while social media platforms do not give accurate measures of the impact as given by citations, they can be used to portray the social impact of the scholarly outputs and indicate the effectiveness of COVID-19 research. The results confirm a positive correlation between the number of citations to articles in databases such as Scopus and the number of views on social media sites such as Mendeley and Twitter. The results of the current study indicated that social media could serve as an indicator of the number of citations of scientific articles. Research limitations/implications This study’s limitation is that the studied articles’ altmetrics performance was examined using only one of the altmetrics data service providers (altmetrics database). Hence, future research should explore altmetrics on the topic using more than one platform. Another limitation of the current research is that it did not explore the academic social media role in spreading fake information as the scope was limited to scholarly outputs on social media. The practical contribution of the current research is that it informs scholars about the impact of social media platforms on the spread and visibility of COVID-19 research. Also, it can help researchers better understand the importance of published COVID-19 research using social media. Originality/value This paper provides insight into the impact of COVID-19 research on social media. The paper helps to provide an understanding of how people engage with health research using altmetrics scores, which can be used as indicators of research performance.

In this paper I offer a philosophical analysis of the act of ‘liking’ a post on social media. First, I consider what it means to ‘like’ something. I argue that ‘liking’ is best understood as a phatic gesture; it signals uptake and anoints the poster’s positive face. Next, I consider how best to theorise the power that comes with amassing many ‘likes’. I suggest that ‘like’ tallies alongside posts institute and record a form of digital social capital. Finally, I consider whether ‘likes’ have ultimately improved online discourse. I argue that while the ‘liking’ function itself is relatively innocuous, public ‘like’ tallies introduce a corrosive motivation to online communication. By making the prospect of increased social capital perpetually salient to us, they encourage us to prioritise high levels of engagement over meaningful engagement.

Our proposed method focuses on offline friends who are acquainted with a target user in the real world. We use Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to distinguish between online friends of a target user who are also offline friends and online friends who are acquainted with the target user only in cyberspace. By utilizing the probability of friendship as a function of the geographical distance between users’ home locations, our proposed method achieves an estimation accuracy 1.7 percentage point better than that of a conventional method. We have also been able to verify the effectiveness of an optimization method that narrows down candidate areas for a target user’s home location.

Research on the formation structure and characteristics of top-impact papers can serve as the basis for the construction of social media-based information communication pattern, and guide papers to gain high attention in both the Internet and academia. In this study, we used the analytic hierarchy process (AHP) to assign index weights for comprehensive impact (CI) and uses this to determine high-impact papers (HIP). By analyzing evaluation indexes and text attributes, as well as by comparing the characteristics of high-citation papers (HCPs) and HIPs, we elucidated the mechanism of HIPs formation. For this, we extracted all data (2630 articles) from PLoS Biology (2009–2018) as our objects of analysis. In addition, a structural diagram was used to summarize the results. The study results revealed that academic and social impacts of HIPs are interdependent and complementary. The indicators’ variation frequency and amplitude of HIPs are far higher than those of HCPs. Two indicators are constituting the direct impact source for HIPs. Redundant keyword and title do not significantly contribute to the impact of the paper. Under a moderate number of conditions, the more collaborative the paper, the more it is recommended and communicated. Although citation remains the most widely accepted index of a paper, researchers should not ignore the impact of papers via social media.

PurposeThis study aims to compare the impacts of proceedings papers in the fields of social science and humanities (SSH) and science.Design/methodology/approachThis study involved not only citations but also altmetric indexes to compare the impacts of proceedings papers among multiple disciplines with 1,779,166 records from Conference Proceedings Citation Index (CPCI) in the Web of Science (WoS) in the period of 2013–2017. The mean value, concentration ratio, Lorenz curves and correlation analysis are utilized into the comparative analysis.Findings(1) Proceedings papers in science fields had higher scholarly impacts than those in SSH fields. (2) As for societal impact, clinical, pre-clinical and health still ranked first, whereas physical science and engineering and technologies were transcended by SSH fields, which is different from the scholarly impact of proceedings papers. (3) As for proceedings papers, citations and altmetric indexes have weak or moderate correlations in all six fields, indicating that altmetrics can be supplemented when assessing proceedings papers.Originality/valueThis study is expected to enhance the understanding of proceedings papers and to promote accuracy of evaluation for them by exhibiting the multidisciplinary differences of their scholarly and societal impacts.

Highly influential social users can guide public opinion and influence their emotional venting. Therefore, it is of great significance to identify high-impact users effectively. This paper starts with the users’ text content, users’ emotions, and fans’ behaviors. It combines the amount of information in the content and sentiment tendency with the fans’ forwarding, commenting, and Liking actions. And based on the principle of the three-degree influence, the users’ influence calculation model is constructed. Finally, the experimental results show that the three-degree force calculation model is more accurate and effective than other similar models.

Aim Using an integrated model of health literacy, we discuss the importance of health literacy among foreign domestic workers in the provision of informal caregiving during outbreaks of communicable diseases. COVID-19 pandemic is used as an example. Background Adequate health literacy in the population is known to be important for the prevention of communicable diseases. Foreign domestic workers, a group of marginalized caregivers in private households, are generally presumed to have limited health literacy because of numerous socio-cultural disadvantages. To date, there is limited evidence that these informal health care providers receive support from community-based nurses. Design A discursive paper. Conclusion Foreign domestic workers, with varying levels of health literacy, may be viewed either as a resource to break the chain of infection or as a potential reservoir of communicable diseases in the community. Meanwhile, restrictions imposed in response to diseases transmissions (e.g., stay-at-home measures for COVID-19) may directly exacerbate the social support received by these foreign domestic works and their ability to access health-related information. There are also concerns about their ability to appraise and evaluate information related to communicable diseases at a time when fake news and misinformation is being disseminated through social media. Language and cultural barriers are important issues that need to be addressed to ensure that foreign domestic workers are in a position to follow public health recommendations. Relevance to clinical practice Nurses hold an important position in empowering foreign domestic workers with adequate health literacy, by engaging groups of foreign domestic workers in the community and their consulates in planning educational programmes and effectively disseminating information. At a time of global pandemic, an assessment of the health literacy levels of foreign domestic workers in places like Hong Kong is urgently required.

BACKGROUND: Competence in teaching procedural skills is required for faculty in all specialties. Regardless of involvement in undergraduate medical education (UME) versus graduate medical education (GME), faculty will likely be involved in teaching procedures to novice learners at some point, with the goal of having the learner achieve graduated independence and technical competence in a skill set. A large body of literature exists addressing the best practices for teaching and maintaining procedural skills. We searched for articles that describe the best practices for teaching procedural skills to all levels of learners. METHODS: We conducted a literature search for papers on procedural skills training and teaching. We also made a call for papers on social media from members of the online #MedEd and #FOAMed communities. Once a list of the articles was compiled, we conducted a three‐round modified Delphi process to identify those illustrating best practices for teaching procedural skills by both junior and senior faculty. RESULTS: We identified 98 relevant articles on the topic of procedural skills training. Six articles were deemed to be highly relevant after three rounds of the modified Delphi. Best practices included using an established educational framework when designing procedural skills teaching sessions, providing positive feedback to learners with opportunities for improvement, and demonstrating the procedure to the learners. CONCLUSIONS: Medical educators should employ evidence‐based practices when designing and delivering procedural skills sessions. Educational frameworks provide faculty developers and facilitators with an organized approach to teaching these sessions. Maintenance of procedural skills over time is key; faculty can utilize simulation‐based procedural training and deliberate practice to prevent decay of learned skills.

As social issues like climate change become increasingly salient, digital traces left by scholarly documents can be used to assess their reach outside of academia. Our research examine who shared climate change research papers on Twitter by looking at the expressions used in profile descriptions. We categorized users in eight categories (academia, communication, political, professional, personal, organization, bots and publishers) associated to specific expressions. Results indicate how diverse publics may be represented in the communication of scholarly documents on Twitter. Supplementing our word detection analysis with qualitative assessments of the results, we highlight how the presence of unique or multiple categorizations in textual Twitter descriptions provides evidence of the publics of research in specific contexts. Our results show a more substantial communication by academics and organizations for papers published in 2016, whereas the general public comparatively participated more in 2015. Overall, there is significant participation of publics outside of academia in the communication of climate change research articles on Twitter, although the extent to which these publics participate varies between individual papers. This means that papers circulate in specific communities which need to be assessed to understand the reach of research on social media. Furthermore, the flexibility of our method provide means for research assessment that consider the contextuality and plurality of publics involved on Twitter.

Facebook, Twitter, Instagram, and LinkedIn are all popular online social media sites these days. Everyone uses different social media platforms, from children to adults. The use of these social media applications is increasing, which leads to a rise in social media crime. Here is where the word “fake profiles” comes into play; these fake profiles are responsible for distributing misleading details about a particular user or attempting fraudulent activities with bad intentions. Many researchers have been carried in response to the issue of fake profiles that exist in any social media application. There is research available in different directions to get a real problem solution. This paper summarizes all of the research on fake profile identification in social media platforms. This paper gives survey of work, indicating whether the user profile is fake or real.

Over the last decade, research on derogatory communication has focused on ordinary speech contexts and the use of conventional pejoratives, like slurs. However, the use of social media has given rise to a new type of derogatory behavior that theorists have yet to address: internet trolling. Trolls make online utterances aiming to frustrate and offend other internet users. Their ultimate goal is amusement derived from observing a good faith interlocutor engage with their provocative posts. The basis for condemning a pejorative utterance is often taken to be the harm it causes or a defective attitude in the speaker. However, trolling complicates this picture, since trolling utterances are by definition insincere and should be recognizable as such to other trolls. Further, these utterances seem morally questionable even when they cause little to no harm (e.g. when a troll’s utterance fails to secure uptake), and they often do not feature conventional pejoratives. I argue that while the potential for negative effects is relevant to ethical assessment, in general trolling is pro tanto wrong because the troll fails to accord others the proper respect that is their due (independently of whether they harm them). However, this characteristic wrong-making feature is sometimes overridden.

Purpose The aim of this study is to determine the relationship between social media and libraries in terms of marketing. The present research is to find out the factors and impact as well as the issues related to libraries and marketing using social media. This study will give massive information regarding social media use for the marketing of library sources and services. Design/methodology/approach This study so far has been taken into account to review all the related literature of social media and library marketing. All the related literature studies were reviewed according to the suggested objectives of the study. Findings The present study reveals after reviewing literature that there is a great impact of social media on libraries. The literature shows that social media is the biggest change in the twenty-firstst century for library professionals and librarians. The reviewed literature studies have discovered that the libraries are facing too many challenges and issues to the adoption of social media for the marketing of library sources and services. The collected and analyzed works of the literature indicated that social media would be the best tool to promote library sources and services. Research limitations/implications This research or study can serve the LIS professionals and librarians in the field of library and information science and librarianships that they are looking for the adoption and application of social media in the marketing of library sources and services to the targeted customers. Practical implications This research or study can serve the LIS professionals and Librarians in the field of library and information science and librarianships that they are looking for the adoption and application of social media in the marketing of library sources and services to the targeted customers. Originality/value To the best of the authors’ knowledge, this study is one of the first among those researches that is to disclose the close relationship between social media and libraries in terms of marketing of library sources and services.

Background Central to effective public health policy and practice is the trust between the population served and the governmental body leading health efforts, but that trust has eroded in the years preceding the pandemic. Vaccine hesitancy among adults is also a growing concern across the US. Recent data suggests that the trustworthiness of information about the COVID-19 vaccine was a larger concern than either vaccine side effects or risks. Objective This paper aims to describe the methods used to create a public health microinfluencer social media vaccine confidence campaign for the COVID-19 vaccine in underserved Tennessee communities. A secondary objective is to describe how the social ecological model and social cognitive theory may address vaccine hesitancy using community pharmacies. Methods In late 2020, 50 independent community pharmacies in underserved communities across Tennessee were involved in a public health project with the State of Tennessee Department of Health and the University of Tennessee Health Science Center College of Pharmacy. The project involved a three-pronged, pharmacy-based COVID-19 vaccination outreach project, including: (a) social media messaging (i.e., microinfluencer approach), (b) community partner collaboration, and (c) in-pharmacy promotion. Quantitative and qualitative data will assess the quality and effectiveness of the program. Social media outcomes will also be assessed to measure the impact of the microinfluencer social media training. Results Project implementation is planned for six months (January 2021-June 2021), after an initial month of planning by the research team (December 2020) and preceding several months of assessment (July 2021 and beyond). Conclusions Novel, theory-based approaches will be necessary to improve vaccine confidence. One approach to promoting public health, derived from the Social Ecological Model, may be to use trusted microinfluencers on social media platforms, such as local community pharmacists and community leaders.

The evaluation of patients who have experienced a fall has been an integral part of geriatric emergency care. All physicians who engage in the care of the geriatric population in acute settings need to familiarize themselves with the current literature on this topic. However, it can be challenging to navigate the large body of literature on this topic. The purpose of this article is to identify and summarize the key studies that can be helpful for faculty interested in an evidence-based fall evaluation. The authors compiled a list of key papers on emergency department (ED) based upon a structured literature search supplemented with suggestions by key informants and an open call on social media; 32 studies on ED evaluation were identified. Our authorship group then engaged in a modified Delphi technique to develop consensus on the most important studies about fall evaluation for emergency physicians. This process eventually resulted in the selection of the top five articles on fall evaluation. Additionally, we summarize these studies with regard to their relevance to emergency medicine (EM) trainees and junior faculty. Evaluation of older patients with a history of falls is a challenging but crucial component of EM training. We believe our review will be educational for junior and senior EM faculty to better understand these patients' care and to design an evidence-based practice.

Cyber security and data science are two of the fastest growing fields in Computer Science and more recently they are being integrated for various applications. This position paper will review the developments in applying Data science for cyber security and cyber security for data science and then discuss the applications in Social Media.

Purpose The purpose of this paper is to investigate the usage, captures, mentions, social media and citations of highly cited papers of Library and information science (LIS). Design/methodology/approach This study is quantitative research that was conducted using scientometrics and altmetrics indicators. The research sample consists of LIS classic papers. The papers contain highly cited papers of LIS that are introduced by Google Scholar. The research data have been gathered from Google Scholar, Scopus and Plum Analytics Categories. The data analysis has been done by Excel and SPSS applications. Findings The data indicate that among the highly cited articles of LIS, the highest score regarding the usage, captures, mentions and social media and the most abundance of citations belong to “Citation advantage of open access articles” and “Usage patterns of collaborative tagging systems.” Based on the results of Spearman statistical tests, there is a positive significant correlation between Google Scholar Citations and all studied indicators. However, only the correlation between Google Scholar Citations with capture metrics (p-value = 0.047) and citation metrics (p-value = 0.0001) was statistically significant. Originality/value Altmetrics indicators can be used as complement traditional indicators of Scientometrics to study the impact of papers. Therefore, the Altmetrics knowledge of LIS researchers and experts and practicing new studies in this field will be very important.

The potential of social media to disseminate, aggregate, channel and democratize social accountability processes has encouraged a variety of organizations to actively promote and champion such initiatives. These initiatives typically envision a three step social accountability process where, for example, the publication of previously-private financial information about the inappropriate wealth accumulation activities of politicians and their business allies (step #1), combined with social media dissemination and discussion of these activities (step #2), can result in an accountability conversation that spills out of the medium and that sometimes results in positive social change (step #3). The current study examines Twitter reactions to the International Consortium of Investigative Journalist’s (ICIJ) publication of the Panama Papers. The analysis illustrates that there was a Twitter reaction: furthermore, that there were different styles of response and that certain styles were more likely to elicit an audience reaction, especially if the tweeter was a journalist or organization. While the provided analysis focuses on step #2 within the social accountability process, the results imply that publicly-interested accounting academics qua activists can facilitate social accountability by helping to make previously-private financial information public and by cultivating sympathetic individuals within the traditional media as well as within organizations that are active on social media.

Supervised machine learning, in which models are automatically derived from labeled training data, is only as good as the quality of that data. This study builds on prior work that investigated to what extent “best practices” around labeling training data were followed in applied ML publications within a single domain (social media platforms). In this paper, we expand by studying publications that apply supervised ML in a far broader spectrum of disciplines, focusing on human-labeled data. We report to what extent a random sample of ML application papers across disciplines give specific details about whether best practices were followed, while acknowledging that a greater range of application fields necessarily produces greater diversity of labeling and annotation methods. Because much of machine learning research and education only focuses on what is done once a “ground truth” or “gold standard” of training data is available, it is especially relevant to discuss issues around the equally important aspect of whether such data is reliable in the first place. This determination becomes increasingly complex when applied to a variety of specialized fields, as labeling can range from a task requiring little-to-no background knowledge to one that must be performed by someone with career expertise.

In the view of Social Exchange Theory (SET), this article provides an insight into the knowledge sharing behavior of an employee in the organization. Trust is a core factor affecting knowledge shar...

Summary Objectives : Analyze papers published in 2019 within the medical natural language processing (NLP) domain in order to select the best works of the field. Methods : We performed an automatic and manual pre-selection of papers to be reviewed and finally selected the best NLP papers of the year. We also propose an analysis of the content of NLP publications in 2019. Results : Three best papers have been selected this year including the generation of synthetic record texts in Chinese, a method to identify contradictions in the literature, and the BioBERT word representation. Conclusions : The year 2019 was very rich and various NLP issues and topics were addressed by research teams. This shows the will and capacity of researchers to move towards robust and reproducible results. Researchers also prove to be creative in addressing original issues with relevant approaches.

Social media marketing has emerged as a new tool for brands to reach certain target groups. While the use of platforms such as Instagram and Facebook has created a multi-billion dollar business, it has also multiplied the amount of sensitive data that is being collected. The associated risks for the end user in terms of data privacy have grown accordingly and most users are unaware of what happens to their data in the background. We propose a novel privacy-preserving social media advertising architecture, which offers a solution that is beneficial to all parties: user privacy is protected and advertisers receive sufficient statistical data, such that all marketing services can be provided in a privacy-friendly manner without sacrificing functionality. Additionally, all computed statistics can be cryptographically verified such that the currently trust-based relationship between brands and social media marketers is replaced by strong cryptographic guarantees.

This study aims to investigate the altmetric activity of papers published by the University of Zagreb School of Medicine in internationally visible journals and to identify differences in altmetric activity between the papers published in international and local journals and between those published in English and Croatian. We also investigated changes in altmetric activity over time and the characteristics of papers with the highest Twitter and Mendeley activity. The sample included 390 papers collected from the bibliographic database Scopus. Their altmetric and citation activities were measured at three time points: in July 2014, 2015, and 2016. The findings generally correspond to those observed in the large-scale studies of medical papers. Papers in renowned journals, and papers reporting clinical guidelines and multicentric studies had the most intense altmetric activity. In contrast, papers published in local, Croatian journals showed minimal altmetric activity, especially the papers published in Croatian. These results indicate that the local publishing community has not yet recognised social media as a tool for promoting research and that non-English language publications have minimal chances to receive attention, even in social media. The evaluative potential of altmetric indicators has to be further explored in a broader context.

In The Academic Teaching Librarian’s Handbook , Claire McGuinness, faculty in the School of Information and Communication Studies at the University College Dublin, offers comprehensive insights into different topics that are relevant to instruction librarians. These topics include the changing context of information literacy, social media and the rise of “fake news,” digital learning, and professional identity. The author also discusses the value of an articulated personal teaching philosophy, the importance of self-analysis and self-reflection, developing a teaching role as a new instruction librarian, and leadership and advocacy skills. McGuinness’ academic narrative style, set to small black font, is supported by a wide array of citations and data from reports and surveys. In addition, each topic offers “personal reflection points” along with figures and tables that highlight important details. The chapters conclude with exercises inviting readers to reflect on the content covered through different hypothetical scenarios.

We are presented with statistical claims in the news, on social media, and in the workplace. How can we determine if these claims are trustworthy? University of Cambridge professor David Spiegelhalter describes how real-life problems are solved with statistical analysis as a way of introducing concepts necessary for data literacy. He defines data literacy as “the ability to not only carry out statistical analysis on real-world problems, but also to understand and critique any conclusions drawn by others on the basis of statistics.” This book will help readers acquire these abilities.

Dissemination of scientific content has been amplified with increasing use of social media (SoMe) in cardiovascular (CV) medicine ([1][1]). Traditionally, the performance of a scholarly journal paper is measured by the number of times it is cited. A newer metric, the Altmetric Attention Score (AAS

During the past decade or so, knowledge graphs have stealthily made way into our daily lives, whether through voice assistants (the likes of Alexa, Google Assistant or Siri), spontaneous search results or customized personal shopping experiences through online stores recommenders. Today, we are surrounded and constantly interacting with knowledge graphs on a regular basis. The scope and impact of knowledge graphs and underlying graph databases are still a mystery to people. Considering its smooth entry into our lives, most of us are unaware of how dependent we actually are on the technology. In this paper, we have shown the working of the Google knowledge graph and how the knowledge graph works as the most effective recommendation system. We have discussed how it finds its way through daily life in various fields like Banking, Social media (Facebook, LinkedIn, Netflix), Food application (Uber Eats) and the Healthcare sector.

INTRODUCTION: All emergency medicine (EM) residency programs must recruit new medical school graduates each year. The process is often overwhelming, with each program receiving far more applicants than available positions. We searched for evidence‐based best practices to guide residency programs in screening, interviewing, and ranking applicants to ensure a high‐performing and diverse residency class. METHODS: A literature search was conducted on the topic of residency recruitment, utilizing a call on social media as well as multiple databases. After identifying relevant articles, we performed a modified Delphi process in three rounds, utilizing junior educators as well as more senior faculty. RESULTS: We identified 51 relevant articles on the topic of residency recruitment. The Delphi process yielded six articles that were deemed most highly relevant over the three rounds. Transparency with selection criteria, holistic application review, standardized letters of evaluation, and blinding applicant files for interviewers were among noted best practices. CONCLUSIONS: Well‐supported evidence‐based practices exist for residency recruitment, and programs may benefit from understanding which common recruitment practices offer the most value. The articles discussed here provide a foundation for faculty looking to improve their program's recruiting practices.

Research has shown that Violence Against Women is a pervasive problem which has been increasing. Until a few years ago, it took place both in public and private spaces, but it has now broken into Digital Space, adopting more symbolic expressions. There has been some important related work from Data Science approaches. Mainly about cyberbullying, detection of language patterns through supervised algorithms, a few works on unsupervised learning, and some on violence against women. This article is part of a larger investigation related to the Violence Against Women in Digital Space phenomena in Mexico from Spanish-language interactions in microblogging social network, Twitter. We present the framework to address these phenomena from a Data Science perspective and go through some first stage results. The goal is to give Data Science perspectives and insights related to the understanding of Digital Violence Against Women, with the purpose of generating awareness about this problem by, modifying manners and implementing public policies that could counteract this social problem.

Recommender system is a strategy in e-commerce, which recommends items based on the user’s interest. It has the capability to predict whether a particular user would prefer an item or not based on the user’s profile. Recommender systems are useful for both e-commerce service provider and users. So it should be required for a recommendation system to provide most preferable items to the user’s interest. This paper presents a dynamic recommendation system to provide recommendations on the user’s interest. In this dynamic recommendation system first of all the web usage information is utilized to find the user’s behavior and then similar user behavior score is computed. In the second phase, product information is collected on the basis of current search information about the user through which sentiment score and social media popularity score are computed. On the other side coefficient matrix is used to calculate user’s purchasing power. These three factors- similar score behavior, sentiment score and popularity score are used to calculate the combined weight for the particular product. Then a coefficient matrix and the computed weights are used to calculate the possible recommendation of the product for the user.

The development of new technology such as internet and social media makes the future of the paper industries are getting worse. Local media does not immune from the phenomena. Serving to its communities is one of the strategy to be survive. It is done by several papers in Java Island. One of them is Suara Merdeka daily. How did this paper actualize its strategy on its framing content? Describing the frame of the information which was used to serve its communities was the main objective of this research. A framing theory and framing analysis in constructive paradigm was used to answer the problem. The content of Suara Merdeka daily was chosen purposively to get the evidence. There were some rubrics that contained the service to communities namely “Fokus Jateng”, “Liputan Khusus”, “Telisik”, “Spektrum”, “Her Spirit”, “Jalan-jalan”, “Layar”, “Trending Topic”, and “Hello Kampus”. The result showed more information framing on news than another framing. Besides, a certain education institution was published more than others. There was an unequal treatment to communities to be published in the paper. It is suggested, in the future, the paper should give the same treatment to its communities.

Social media continues to lead imprudent users into over-sharing, exposing them to various privacy threats. Recent research thus focusses on nudging the user into the 'right' direction. In this paper, we propose Comparison-based Privacy CbP, a design paradigm for privacy nudges that overcomes the limitations and challenges of existing approaches. CbP is based on the observation that comparison is a natural human behavior. With CbP, we transfer this observation to decision-making processes in the digital world by enabling the user to compare herself along privacy-relevant metrics to user-selected comparison groups. In doing so, our approach provides a framework for the integration of existing nudges under a self-adaptive, user-centric norm of privacy. Thus, we expect CbP not only to provide technical improvements, but to also increase user acceptance of privacy nudges. We also show how CbP can be implemented and present preliminary results.

The rapid development of social media technology has revolutionized the business environment in tourism industry. User generated reviews on the social media platforms are believed to create an incredible effect on customer’s purchase behaviour of tourism product and services. The ongoing concern among the researchers over the presence of consumer community on tourism social media (TSM) to influence tourists’ consumption behaviour is substantially evident. Some of the scholars believe social media community have negative consequences on consumer perceived value (CPV) and behaviour such as inducing trust crisis and jealousy; while others found it to improve consumer’s perceived value. Existing mixed findings and ongoing debate clearly suggest the need for further research. This article conducted a literature review of the consumer community on tourism social media and customer’s perceived value. An integrated conceptual model of consumer community on TSM and CPV is proposed. Consequently, empirical research and practical implications are discussed.

Social media platforms have taken political and cultural conversations to an online platform making them more accessible. Ability to anonymously post has allowed more people to participate fearlessly. However, this has also led to an opportunity to spread miss information and manipulative content. Political groups around the globe have used Bot accounts to help spread their preferred narrative online during elections. In the midst of 2019 Indian Lok Sabha Elections speculations were made about the presence of cyber-troops/IT Cells which operate fake accounts and push propaganda. Our finding suggests that a portion of Bot accounts seems to be operated by humans in the background. These accounts have a very distinct usage pattern on Twitter compared to legitimate human users. Our experiments also point out that only 1.3% of total interactions are directed from Humans to Bots, showing Bot accounts inability to gel well in the online social network.

Adversarial Machine Learning has been gaining attention from the NLP community due to low interpretability and low robustness of the current state-of-the-art systems. In this work, we study the effect of various adversarial attacks for detection of suicidal intent in social media setting. Suicide Ideation is a sensitive issue and is a leading cause of death. We show how various models are rendered useless after attacks and perform adversarial training using the most ideal attacks to improve their robustness. We also conduct several experiments with the attacks to study their effect and propose an approach for adversarial training using Generative Adversarial Networks.