The Influence of E-Leadership on Task Performance in New Work Era (preprint)

Organization began to adapt to remote working patterns in the early pandemic, while human resource management faces new challenges in adapting to these changes.  Furthermore, leadership is the main key in maintaining and improving Employee Job Performance. Therefore, this study aims to find effective New Leadership Styles in adapting to the New Work era. It was performed by analyzing employee perceptions of the Leadership Style (Transformational, Servant and Empowering Leadership) and measuring their effects on Employee Job Performance in current conditions. The survey was conducted through an online to 387 employees who had worked for at least 5 years and at least 2 years had worked virtually in an automotive manufacturing company in Indonesia which had implemented a virtual work pattern for most of its employees. Then, the data were processed using SEM Amos 25.0. Subsequently, the results showed that Employee Job Performance (EJP) has two important dimensions, namely Courtesy, and Sportsmanship, and is positively and directly influenced by the New Leadership Style. This study found an effective the New Leadership Style since the COVID-19 pandemic, particularly a leadership style with 9 dimensions namely (1)Openness, (2)Orientation to problem-solving, (3)Freedom at work, (4)Inspirational Motivation, (5)Intellectual Stimulation, (6)Individualized Consideration, (7)Coaching, (8)Participatory Decision Making and (9)Showing concern. The contribution of this study for organizational managers is as a soft reference for effective leadership competencies during the New Work period and a new reference for Leadership Science in Human Resources Management.

Comparing human and artificial intelligence in writing for health journals: an exploratory study (preprint)

Aim: and objectives: The aim was to contribute to the editorial principles on the possible use of Artificial Intelligence (AI)- based tools for scientific writing. The objectives included: A. Enlist the inclusion and exclusion criteria to test ChatGPT use in scientific writing B. Develop evaluation criteria to assess the quality of articles written by human authors and ChatGPT C. Compare prospectively written manuscripts by human authors and ChatGPT Design: Prospective exploratory study Intervention: Human authors and ChatGPT were asked to write short journal articles on three topics: 1) Promotion of early childhood development in Pakistan 2) Interventions to improve gender-responsive health services in low-and-middle-income countries, and 3) The pitfalls in risk communication for COVID-19. We content analyzed the articles using an evaluation matrix. Outcome measures: The completeness, credibility, and scientific content of an article. Completeness meant that structure (IMRaD) and organization was maintained. Credibility required that others work is duly cited, with an accurate bibliography. Scientific content required specificity, data accuracy, cohesion, inclusivity, confidentiality, limitations, readability, and time efficiency. Results: The articles by human authors scored better than ChatGPT in completeness and credibility. Similarly, human-written articles scored better for most of the items in scientific content except for time efficiency where ChatGPT scored better. The methods section was absent in ChatGPT articles, and a majority of references in its bibliography were unverifiable. Conclusions: ChatGPT generates content that is believable but may not be true. The creators of this powerful model must step up and provide solutions to manage its glitches and potential misuse. In parallel, the academic departments, editors, and publishers must expect a growing utilization of ChatGPT and similar tools. Disallowing ChatGPT as a co-author may not be enough on their part. They must adapt the editorial policies, use measures to detect AI-based writing, and stop its likely implications for human health and life.

An Innovative Faculty Intervention at HBCUs: Self Care, Writing as a Practice and Building Community (preprint)

The HBCU STEM Undergraduate Success (STEM-US) Research Center, funded in 2020 by the National Science Foundation, was created to gain an understanding of what types of academic interventions work at Historically Black Colleges and Universities and why they work. For all of us in higher education, teaching, research, and scholarly production have been greatly impacted by COVID-19. The response to the new normal has been particularly taxing for faculty at HBCUs. Recent public displays of blatant anti-black sentiment not only adds to our own stress load but has served to exacerbate the emotional needs of our students. The additional strain of the pandemic response added to already stressed organizational systems at HBCU’s. All of which has compelled HBCU faculty to re-examine how we relate to our work, our families and ourselves. The value of ‘sharing our stories’ lies within the deep relationships fostered among the HBCU faculty that are in partnership with the STEM US Center’s research arm, the Analytic Hub. By supporting Communities of Practice, the Hub provides the opportunity to share information on aspects of the Science of Teaching and Learning that are applicable to HBCUs in an open and transparent manner. One of the Hub’s faculty development initiatives is called the CareFull Scholars Program which seeks to encourage adoption of practices that provide exceptional productivity while at the same time emphasizing enhanced health and well-being. This model is based on the premise that we can be scholarly and productive in a healthy and generative way while also centering self-care and collaborative relationships within our working lives. This paper is a reflective account of the inauguration of the CareFull Scholars community of practice. This post-pandemic intervention for faculty productivity promoted self-care through daily writing. We learned that by creating mental, emotional, physical and technical structures of support, daily writing could be easily adopted and with accountability from a caring community be sustained over time.

The rise and spread of the SARS-CoV-2 AY.122 lineage in Russia (preprint)

Background Delta has outcompeted most preexisting variants of SARS-CoV-2, becoming the globally predominant lineage by mid-2021. Its subsequent evolution has led to emergence of multiple sublineages, many of which are well-mixed between countries. Aim Here, we aim to study the emergence and spread of the Delta lineage in Russia. Methods We use a phylogeographic approach to infer imports of Delta sublineages into Russia, and phylodynamic models to assess the rate of their spread. Results We show that nearly the entire Delta epidemic in Russia has probably descended from a single import event despite genetic evidence of multiple Delta imports. Indeed, over 90% of Delta samples in Russia are characterized by the nsp2:K81N+ORF7a:P45L pair of mutations which is rare outside Russia, putting them in the AY.122 sublineage. The AY.122 lineage was frequent in Russia among Delta samples from the start, and has not increased in frequency in other countries where it has been observed, suggesting that its high prevalence in Russia has probably resulted from a random founder effect. Conclusion The apartness of the genetic composition of the Delta epidemic in Russia makes Russia somewhat unusual, although not exceptional, among other countries.

Medical-VLBERT: Medical Visual Language BERT for COVID-19 CT Report Generation With Alternate Learning (preprint)

Medical imaging technologies, including computed tomography (CT) or chest X-Ray (CXR), are largely employed to facilitate the diagnosis of the COVID-19. Since manual report writing is usually too time-consuming, a more intelligent auxiliary medical system that could generate medical reports automatically and immediately is urgently needed. In this article, we propose to use the medical visual language BERT (Medical-VLBERT) model to identify the abnormality on the COVID-19 scans and generate the medical report automatically based on the detected lesion regions. To produce more accurate medical reports and minimize the visual-and-linguistic differences, this model adopts an alternate learning strategy with two procedures that are knowledge pretraining and transferring. To be more precise, the knowledge pretraining procedure is to memorize the knowledge from medical texts, while the transferring procedure is to utilize the acquired knowledge for professional medical sentences generations through observations of medical images. In practice, for automatic medical report generation on the COVID-19 cases, we constructed a dataset of 368 medical findings in Chinese and 1104 chest CT scans from The First Affiliated Hospital of Jinan University, Guangzhou, China, and The Fifth Affiliated Hospital of Sun Yat-sen University, Zhuhai, China. Besides, to alleviate the insufficiency of the COVID-19 training samples, our model was first trained on the large-scale Chinese CX-CHR dataset and then transferred to the COVID-19 CT dataset for further fine-tuning. The experimental results showed that Medical-VLBERT achieved state-of-the-art performances on terminology prediction and report generation with the Chinese COVID-19 CT dataset and the CX-CHR dataset. The Chinese COVID-19 CT dataset is available at https://covid19ct.github.io/.

Using automated decision-making (ADM) to allocate Covid-19 vaccinations? Exploring the roles of trust and social group preference on the legitimacy of ADM vs. human decision-making (preprint)

In combating the ongoing global health threat of the Covid-19 pandemic, decision-makers have to take actions based on a multitude of relevant health data with severe potential consequences for the affected patients. Because of their presumed advantages in handling and analyzing vast amounts of data, computer systems of automated decision-making (ADM) are implemented and substitute humans in decision-making processes. In this study, we focus on a specific application of ADM in contrast to human decision-making (HDM), namely the allocation of Covid-19 vaccines to the public. In particular, we elaborate on the role of trust and social group preference on the legitimacy of vaccine allocation. We conducted a survey with a 2x2 randomized factorial design among n=1602 German respondents, in which we utilized distinct decision-making agents (HDM vs. ADM) and prioritization of a specific social group (teachers vs. prisoners) as design factors. Our findings show that general trust in ADM systems and preference for vaccination of a specific social group influence the legitimacy of vaccine allocation. However, contrary to our expectations, trust in the agent making the decision did not moderate the link between social group preference and legitimacy. Moreover, the effect was also not moderated by the type of decision-maker (human vs. algorithm). We conclude that trustworthy ADM systems must not necessarily lead to the legitimacy of ADM systems.

Reinforcement Learning Based Decision Support Tool For Epidemic Control (preprint)

Rationale: Covid-19 Is Certainly One Of The Worst Pandemics Ever. In The Absence Of A Vaccine, Classical Epidemiological Measures Such As Testing In Order To Isolate The Infected People, Quarantine And Social Distancing Are Ways To Reduce The Growing Speed Of New Infections As Much As Possible And As Soon As Possible, But With A Cost To Economic And Social Disruption. It Is Therefore A Challenge To Implement Timely And Appropriate Public Health Interventions. Objective: This Study Investigates A Reinforcement Learning Based Approach To Incrementally Learn How Much Intensity Of Each Public Health Intervention Should Be Applied At Each Period In A Given Region. Methods: First We Define The Basic Components Of A Reinforcement Learning (Rl) Set Up (I.E., States, Reward, Actions, And Transition Function), This Represents The Learning Environment For The Agent (I.E., An Ai-Model). Then We Train Our Agent Using Rl In An Online Fashion, Using A Reinforcement Learning Algorithm Known As Reinforce. Finally, A Developed Flow Network, Serving As An Epidemiological Model Is Used To Visualize The Results Of The Decisions Taken By The Agent Given Different Epidemic And Demographic State Scenarios. Main Results: After A Relatively Short Period Of Training, The Agent Starts Taking Reasonable Actions Allowing A Balance Between The Public Health And Economic Considerations. In Order To Test The Developed Tool, We Ran The Rl-Agent On Different Regions (Demographic Scale) And Recorded The Output Policy Which Was Still Consistent With The Training Performance. The Flow Network Used To Visualize The Results Of The Simulation Is Considerably Useful Since It Shows A High Correlation Between The Simulated Results And The Real Case Scenarios. Conclusion: This Work Shows That Reinforcement Learning Paradigm Can Be Used To Learn Public Health Policies In Complex Epidemiological Models. Moreover, Through This Experiment, We Demonstrate That The Developed Model Can Be Very Useful If Fed In With Real Data. Future Work: When Treating Trade-Off Problems (Balance Between Two Goals) Like Here, Engineering A Good Reward (That Encapsulates All Goals) Can Be Difficult, Therefore Future Work Might Tackle This Problem By Investigating Other Techniques Such As Inverse Reinforcement Learning And Human-In-The-Loop. Also, Regarding The Developed Epidemiological Model, We Aim To Gather Proper Real Data That Can Be Used To Make The Training Environment More Realistic, As Well As To Apply It For Network Of Regions Instead Of A Single Region.

Exploration of "Online + Offline" Blended Teaching Mode in Molecular Biology Experiment Under the Background of COVID-19 (preprint)

Backgrounds: The outbreak of coronavirus has had a serious impact on the economy, life and education of virtually all countries around the world. In China, the pandemic continues to pose a great challenge to the traditional in-person education model of schools. Educators in China are facing the agonizing decision of whether to resume in-person instruction while there’s still no effective cure for the new coronavirus. Therefore, in order to ensure the quality of teaching and learning during the period of special time, we explore the "online + offline" blended teaching mode. Methods: Four hundred medical undergraduates from Dalian Medical University in China took part in a simulated teaching model to improve their learning ability of innovation and exploration. Taking molecular biology experiment - cell apoptosis as an example, we experimented with the blending of "online + offline " teaching mode and evaluated the learning outcome under this mode from the perspectives of experiment operation, report writing and group cooperation.Results: Over 95% of them totally agree with this teaching mode and expect to have it applied to other subjects, while less than 10% of the students have the opinion that the traditional teaching mode is better. The result of the innovation project competition also shows that the students trained under this teaching mode perform better than those under the traditional teaching mode in both objective questions and answers and experimental operation.Conclusions: Adoption of this "online + offline" blended teaching mode is effective and provides a new perspective to solve the problems encountered by medical students when they reach a higher level of development. In the process of carrying out this teaching mode, the cultivation of students' independent learning and innovative exploration ability were emphasized. Furthermore, it also helps students to lay a solid foundation for their future study and career development.

Effects of COVID-19 Mental Health Interventions among Community-based Children, Adolescents, and Adults: A Living Systematic Review of Randomised Controlled Trials (preprint)

Background: Scalable interventions to address COVID-19 mental health are needed. Our objective was to assess effects of mental health interventions for community-based children, adolescents, and adults. Methods: We searched 9 databases (2 Chinese-language) from December 31, 2019 to March 22, 2021. We included randomised controlled trials with non-hospitalised, non-quarantined participants of interventions to address COVID-19 mental health challenges. We synthesized results descriptively but did not pool quantitatively due to substantial heterogeneity of populations and interventions and concerns about risk of bias. Findings: We identified 9 eligible trials, including 3 well-conducted, well-reported trials that tested interventions designed specifically for COVID-19 mental health challenges, plus 6 trials of standard interventions (e.g., individual or group therapy, expressive writing, mindfulness recordings) minimally adapted for COVID-19, all with risk of bias concerns. Among the 3 COVID-19-specific intervention trials, one (N = 670) found that a self-guided, internet-based cognitive-behavioural intervention targeting dysfunctional COVID-19 worry significantly reduced COVID-19 anxiety (standardized mean difference [SMD] 0.74, 95% CI 0.58 to 0.90) and depression symptoms (SMD 0.38, 95% CI 0.22 to 0.55) in Swedish general population participants. A lay-delivered telephone intervention for homebound older adults in the United States (N = 240) and a peer-moderated education and support intervention for people with a rare autoimmune condition from 12 countries (N = 172) significantly improved anxiety (SMD 0.35, 95% CI 0.09 to 0.60; SMD 0.31, 95% CI 0.03 to 0.58) and depressive symptoms (SMD 0.31, 95% CI 0.05 to 0.56; SMD 0.31, 95% CI 0.07 to 0.55) 6-weeks post-intervention, but these were not significant immediately post-intervention. No trials in children or adolescents were identified. Interpretation: Internet-based programs for the general population and lay- or peer-delivered interventions for vulnerable groups may be effective, scalable options for public mental health in COVID-19. More well-conducted trials, including for children and adolescents, are needed.

The Quantified Pandemic: Digitised Surveillance, Containment and Care in Response to the COVID-19 Crisis (preprint)

In this chapter, I present a sociocultural analysis of how automated decision-making (ADM) tools and related software were deployed or anticipated in response to the COVID-19 crisis during the first year of the pandemic. These technologies included apps used to monitor people in quarantine and self-isolation, contact tracing apps, surveillance drones, digitised temperature checking devices, apps for delivering COVID test results, software for identifying ‘at risk’ patients and for selecting recipients of vaccines, and digital vaccine ‘passport’ apps, as well as automated symptom checker apps, platforms and chatbots designed to help people determine whether they were infected with the novel coronavirus or needed to seek medical attention. Building on scholarship in critical public health, technocultures and critical data studies, I identify and discuss the social and political contexts and effects of these technologies. I demonstrate that despite techno-utopian promissory narratives routinely promoting their advantages, while some of these technologies have assisted with COVID-19 surveillance, control and medical care, many have failed. Furthermore, the deployment of these technologies has in many cases exacerbated existing socioeconomic disadvantage and stigmatisation, excluded some social groups and populations from economic support or healthcare and flouted human rights relating to privacy and freedom of movement.

Preparing Developed Democracies for Long-Run Economic Slowdowns (preprint)

Economic growth in the 19th and 20th centuries, following the Industrial Revolutions, was much faster than in preceding centuries. This unprecedented global growth coincided with the global proliferation of democracy, with some evidence for bidirectional causation. Macroeconomic forecasts have predicted slower economic growth in the 21st century--perhaps substantially slower--for structural reasons such as aging populations, slowdowns in innovation, and debt. Long-run effects of COVID-19 and climate change could further slow growth. Moreover, some sustainability scientists assert that slower growth, stagnation, or even de-growth is an environmental imperative. Whether slow growth is inevitable or planned, we argue that democracies should prepare for additional fiscal and social stress--some of which is already apparent. Focusing on developed democracies, we propose that preparations should include efforts to: (i) reduce inequality; (ii) socially integrate diverse populations and build shared identities; (iii) increase economic opportunity for youth; (iv) improve return on investment in taxation and public spending; (v) strengthen formal democratic institutions; and (vi) invest in improving non-economic drivers of subjective well-being. Many aspects of our analysis likely also apply to other types of societies besides developed democracies.

Anxiety-Driven Cardiopulmonary Complaints during COVID-19 Pandemic Outbreak (preprint)

Background We intend to examine whether the COVID-19 outbreak influences medical decision-making (MDM) among Non-COVID patients. Method We recruit 287 patients who admit to ER department due to cardiovascular complaints. Anxiety level was measured using three questionnaires (GAD-7, Beck Inventory, and the cardiac anxiety questionnaire). A fourth survey was designed to assess MDM considerations. Results 64% of patients were male (median age 54). Almost half of the patients were found to have moderate to severe levels of anxiety.79.3% of patients reported that the outbreak influenced their MDM. 44.5% of patients sought medical care 2-3 from the onset of symptoms. Coronary artery disease was found in only 26 patients (9.1%). Almost half of the patients stated that they would have gone earlier if not for the current pandemic. Conclusion Non-COVID patients seeking medical care had a high anxiety level that directly affected decision-making and put them at unnecessary risk.

CovidSIMVL --Transmission Trees, Superspreaders and Contact Tracing in Agent Based Models of Covid-19 (preprint)

ABSTRACT The agent-based model CovidSIMVL ( github.com/ecsendmail/MultiverseContagion ) is employed in this paper to delineate different network structures of transmission chains in simulated COVID-19 epidemics, where initial parameters are set to approximate spread from a single transmission source, and R 0 ranges between 1.5 and 2.5. The resulting Transmission Trees are characterized by breadth, depth and generations needed to reach a target of 50% infected from a starting population of 100, or self-extinction prior to reaching that target. Metrics reflecting efficiency of an epidemic relate closely to topology of the trees. It can be shown that the notion of superspreading individuals may be a statistical artefact of Transmission Tree growth, while superspreader events can be readily simulated with appropriate parameter settings. The potential use of contact tracing data to identify chain length and shared paths is explored as a measure of epidemic progression. This characterization of epidemics in terms of topological characteristics of Transmission Trees may complement equation-based models that work from rates of infection. By constructing measures of efficiency of spread based on Transmission Tree topology and distribution, rather than rates of infection over time, the agent-based approach may provide a method to characterize and project risks associated with collections of transmission events, most notably at relatively early epidemic stages, when rates are low and equation-based approaches are challenged in their capacity to describe or predict. MOTIVATION – MODELS KEYED TO CONTEMPLATED DECISIONS Outcomes are altered by changing the processes that determine them. If we wish to alter contagion-based spread of infection as reflected in curves that characterize changes in transmission rates over time, we must intervene at the level of the processes that are directly involved in preventing viral spread. If we are going to employ models to evaluate different candidate arrays of localized preventive policies, those models must be posed at the same level of granularity as the entities (people enacting processes) to which preventive measures will be applied. As well, the models must be able to represent the transmission-relevant dynamics of the systems to which policies could be applied. Further, the parameters that govern dynamics within the models must embody the actions that are prescribed/proscribed by the preventive measures that are contemplated. If all of those conditions are met, then at a formal or structural level, the models are conformant with the provisions of the Law of Requisite Variety 1 or the restated version of that law – the good regulator theorem. 2 On a more logistical or practical level, the models must yield summary measures that are responsive to changes in key parameters, highlight the dynamics, quantify outcomes associated with the dynamics, and communicate that information in a form that can be understood correctly by parties who are adjudicating on policy options. If the models meet formal/structural requirements regarding requisite variety, and the parameters have a plausible interpretation in relationship to real-world situations, and the metrics do not overly-distort the data contents that they summarize, then the models provide information that is directly relevant to decision-making processes. Models that meet these requirements will minimize the gap that separates models from decisions, a gap that will otherwise be filled by considerations other than the data used to create the models (for equation-based models) or the data generated by the simulations. In this work, we present an agent-based model that targets information requirements of decision-makers who are setting policy at a local level, or translate population level directives to local entities and operations. We employ an agent-based modeling approach, which enables us to generate simulations that respond directly to the requirements of the good regulator theorem. Transmission events take place within a spatio-temporal frame of reference in this model, and rates are not conditioned by a reproduction rate (R0) that is specified a priori . Events are a function of movement and proximity. To summarize dynamics and associated outcomes of simulated epidemics, we employ metrics reflecting topological structure of transmission chains, and distributions of those structures. These measures point directly to dynamic features of simulated outbreaks, they operationalize the “efficiency” construct, and they are responsive to changes in parameters that govern dynamics of the simulations.

Clinical and Pathological Correlation between SARS-CoV-2 Infection with Coexisting Health Conditions: A Review (preprint)

Globally, the COVID-19 pandemic has brought the world to a standstill with the infected cases surpassing millions. The causative agent of COVID-19, the SARS-CoV-2 is a novel coronavirus that emerged from the wet animal market in Wuhan, China in early December 2019. Soon after, human-to-human transmission increased the rate of infection making the disease widespread with new hotspots emerging around the world.The epidemiological reports based on clinical characteristics including age, gender, symptoms (both severe and non-severe), and the conditions requiring intensive medical care, along with case fatality revealed that people with co-existing health conditions like diabetes, hypertension, cigarette smoking, and others with cardiovascular and kidney diseases were more susceptible to COVID-19 infection with poor prognosis in cases related to the severity of symptoms and requiring ICU, medical ventilators with a high fatality rate. Even people with immunosuppressed conditions like HIV and cancer, alongwith old age and pregnant women are vulnerable to COVID-19 infection and can cause severe health complications.It is extremely important to have a comprehensive idea of the underlying pathophysiology related to these health conditions which makes them more susceptible to contract SARS-CoV-2 infection in correlation with the development of severe symptoms. This review will provide an extensive viewpoint related to COVID-19 patients having coexisting health conditions together with the association between the prognosis of the disease and the pathogenesis of the SARS-CoV-2 infection, based on the current information available.

Investigating the Effect of Self-regulatory Strategy Development on Iranian EFL Learners’ Metadiscoursal Writing Skill (preprint)

The current research was an attempt to investigate the effect of self-regulatory strategy instruction on Iranian EFL learners’ metadiscoursal writing skill. To this end, 50 Iranian EFL intermediate learners, studying English language in an institute were selected through convenience random sampling. All of them were Persian native speakers. Three research instruments were utilized to gather the data, namely, Self-regulated Strategies Intervention, and Metadiscoursal writing pretest and posttest. Due to the situation of Covid-19, the instructor made a WhatsApp group, besides the virtual group using Adobe Connect to be in contact with the learners. The learners took a metadiscoursal writing pretest. For the next six sessions, the participants were taught intervention, via self-regulated strategies and they were supposed to follow the instructions. The instructor checked every learner’s progress. Finally, they took a posttest, and the results obtained from the research instruments were analyzed through paired samples t-test. The findings revealed that self-regulatory strategy instruction had a positively significant effect on Iranian EFL learners’ metadiscoursal writing skill and it is suggested that teachers are better to get familiar with the self-regulated strategy and its positive outcomes.

Expressive Interviewing: A Conversational System for Coping with COVID-19 (preprint)

The ongoing COVID-19 pandemic has raised concerns for many regarding personal and public health implications, financial security and economic stability. Alongside many other unprecedented challenges, there are increasing concerns over social isolation and mental health. We introduce \textit{Expressive Interviewing}--an interview-style conversational system that draws on ideas from motivational interviewing and expressive writing. Expressive Interviewing seeks to encourage users to express their thoughts and feelings through writing by asking them questions about how COVID-19 has impacted their lives. We present relevant aspects of the system's design and implementation as well as quantitative and qualitative analyses of user interactions with the system. In addition, we conduct a comparative evaluation with a general purpose dialogue system for mental health that shows our system potential in helping users to cope with COVID-19 issues.

Political Actors Participation within Nation-Wide Social Distancing during CoVID-19 Pandemic in Twitter (preprint)

Government of Indonesia’s decision after concerning the increasing of CoVID-19 confirmed case evidences is conducted by stating that there must be required an initial step to reduce its spread, that is by nation-wide social distancing (PSBB). Accordingly, this PSBB issue was then responded by citizen in many ways, including through social media Twitter. The aim of the research is to extract the issues and key actors within the social network of Twitter in Indonesia. The research mined around 5,000 tweets on May 6th, 2020, which included the keyword ’psbb’ and hashtag ‘#psbb’. The implementation of PSBB in nation scale actually impacted some sectors in society. By using text mining and Social Network Analysis (SNA), it is found some important key points. First, the issue of PSBB in social media Twitter in Indonesia are mostly about annecdotal terms of PSBB became ‘social distancing ends up to relationship break’ which is initially popularized by entertainer named FiersaBesari rather than the origin ‘nation-wide social distancing’. Second, looking deeper into the core network, it is found that the most influential Twitter user is named yunartowijaya, an executive director of political consultant institution Charta Politika. Third, online news media which actively involved within PSBB issue are detikcom and CNNIndonesia. And fourth, despite each actor’s political interest, especially those of Demokrat Party, almost all key users in this networks are critizing the Government of Indonesia’s ideas and policies of PSBB during CoVID-19 pandemic.

Networks in a World Unknown: Public WhatsApp Groups in the Venezuelan Refugee Crisis (preprint)

By early March 2020, five million Venezuelans had fled their home country after its complete economic and institutional collapse, and over 1.6 million have migrated to Colombia. Migrants struggle to start their lives over in Colombia, having arrived with few economic resources, and often no legal documentation, in cities with little to offer them. Venezuelan migrants, however, rely heavily on mobile phones and social media networks as lifelines for information, opportunities, and resources -- making WhatsApp both a critical tool for migrants' settlement and integration, as well as an invaluable source of data through which we can better understand migrant experiences. This thesis explores the dynamics of public WhatsApp groups used by Venezuelan migrants to Colombia, and what they can tell us about how migrants use and share information. We center our research on information spread and trust, especially as they intersect with concentration and geographic heterogeneity within groups. We analyze messages and memberships broadly, then explore interaction within groups, fake news and economic scams, and effects of the coronavirus pandemic. Our results have a range of policy implications, from reflections on Colombia's decision to shut its borders amidst the coronavirus pandemic, to understandings of how aid organizations can effectively share information over social media channels.