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1.
20th IEEE International Conference on Emerging eLearning Technologies and Applications, ICETA 2022 ; : 15-21, 2022.
Article in English | Scopus | ID: covidwho-2191849

ABSTRACT

Contract cheating has become a profound issue in academics with the onset of the COVID-19 pandemic as digitised evaluation has become common practice. This evaluation method opens up for examining students remotely, either by online home exams or longer written assessments done away from the classroom. Contract cheating refers to a problem where the students hire a third party to complete their assignment and submit it for grading as their own. Manually dealing with contract cheating is a cumbersome task and tools for plagiarism detection are not able to detect contract cheaters as students do not use the work of other authors without consent. In this paper, a machine learning based system is designed to specifically detect the cases of contract cheating in academics. The system uses keystroke biometric behaviour where typing style is analysed to discriminate cheaters from genuine students. The experiments are conducted on two datasets where one is existing and another is designed by performing data collection in a university for recording the keystroke features. Two categories of keystroke dynamics, namely duration and latency-based features are studied for designing the various machine learning-based systems for investigating the efficient one. Furthermore, the performance of the systems are evaluated under the setting of zero false accusations in order to avoid genuine students being charged as imposters. © 2022 IEEE.

2.
Mental Health Review Journal ; 2023.
Article in English | Scopus | ID: covidwho-2191587

ABSTRACT

Purpose: This paper aims to evaluate service user (SU) and clinician acceptability of video care, including future preferences to inform mental health practice during COVID-19, and beyond. Design/methodology/approach: Structured questionnaires were co-developed with SUs and clinicians. The SU online experience questionnaire was built into video consultations (VCs) via the Attend Anywhere platform, completed between July 2020 and March 2021. A Trust-wide clinician experience survey was conducted between July and October 2020. Chi-squared test was performed for any differences in clinician VC rating by mental health difficulties, with the content analysis used for free-text data. Findings: Of 1,275 SUs completing the questionnaire following VC, most felt supported (93.4%), and their needs were met (90%). For future appointments, 51.8% of SUs preferred video, followed by face-to-face (33%), with COVID-related and practical reasons given. Of 249 clinicians, 161 (64.7%) had used VCs. Most felt the therapeutic relationship (76.4%) and privacy (78.7%) were maintained. Clinicians felt confident about clinical assessment and management using video. However, they were less confident in assessing psychotic symptoms and initiating psychotropic medications. There were no significant differences in clinician VC rating by mental health difficulties. For future, more SUs preferred using video, with a quarter providing practical reasons. Originality/value: The study provides a real-world example of video care implementation. In addition to highlighting clinician needs, support at the wider system/policy level, with a focus on addressing inequalities, can inform mental health care beyond COVID-19. © 2022, Emerald Publishing Limited.

3.
Medical Journal of Dr DY Patil Vidyapeeth ; 15(8):311-316, 2022.
Article in English | Scopus | ID: covidwho-2202103

ABSTRACT

Background: Due to its physiologic immune suppression, pregnancy is a vulnerable time for severe respiratory infections including COVID-19. However, information regarding the effect of COVID-19 during pregnancy is limited. Objectives: To study the clinical profile of patients suffering from coronavirus disease-2019 (COVID-19) during pregnancy and to evaluate the effect of COVID-19 on maternal, perinatal, and neonatal outcomes. Methodology: This is a cross-sectional observational study over a period of one year from June 2020 to May 2021, in Level-3 Covid facility in Ghaziabad. All pregnant females with confirmed positive for Corona virus infection admitted to the covid ward under the department of Obstetrics & Gynecology were included in the study. Results: A total of 233 pregnant women were included in the study. Maximum patients were from age group 21-30 years (53.2), multigravida (62.7%), and presented in the third trimester (80.7%). On admission, 198 patients (85%) had no covid related symptoms and only three patients had severe symptoms requiring ICU care. Total 102 patients delivered (43.77%), out of whom 40 had a normal vaginal delivery and 62 had a cesarean section. The incidence of preterm birth was 22.5% and maternal death was in three patients (1.3%). Conclusion: The common presentation of COVID-19 during pregnancy is either a mild disease or even asymptomatic. The maternal outcomes observed in late pregnancy and fetal and neonatal outcomes appear good in most cases. Further studies are required to know long-term outcomes and potential intrauterine vertical transmission. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.

4.
Glob Cardiol Sci Pract ; 2022(3):e202216, 2022.
Article in English | PubMed Central | ID: covidwho-2204940

ABSTRACT

Background: Our understanding of arrhythmias is minimal with SARS-CoV-2 virus and with the emergence of its double mutant, virtually nonexistent. Patients with the double mutant (B.1.617) SARS-CoV infection had more cardiac manifestations, including arrhythmias and sudden death, than with the traditional variant.Objective: To determine the incidence of arrhythmias in COVID-19 patients with double mutant strain of SARS-CoV-2 virus (B.1.617).Materials and methods: We describe a prospective observational study conducted in the Department of Medicine, United Institute of Medical Sciences, Prayagraj, Uttar Pradesh on patients admitted to the hospital during the period March 2021 to May 2021. Different type of arrhythmias were studied in the admitted patients.Results: Sinus bradycardia is the most common arrhythmia, followed by atrial fibrillation. Malignant arrhythmias, such as ventricular tachycardia/ventricular fibrillation and Torsades de pointes due to QT prolongation, were present in small number of patients with high mortality outcomes. Sinus tachycardia and high-grade AV blocks were also present in some of the patients.Conclusions: Current literature lacks studies on arrhythmias secondary to COVID-19 (double mutant) strain and its possible mechanisms. This makes it difficult to distinguish between arrhythmias secondary to COVID-19 (double mutant) infection due to hypoxemia, dyselectrolytemia, SIRS, comorbidities, and medications or direct viral effects on the cardiomyocytes.

5.
Pediatric Diabetes ; 23(Supplement 31):50, 2022.
Article in English | EMBASE | ID: covidwho-2137170

ABSTRACT

Introduction: Strict isolation measures and interrupted healthcare services during the COVID-19 pandemic are contemplated to instigate stress universally, particularly in those with chronic illnesses such as Type 1 Diabetes (T1D). Objective(s): To evaluate the determinants of stress and its impact on glycemic control among Indian adolescents and young adults (aged 10-25 years), living with T1D. Method(s): A cross-sectional observational study using online, semistructured survey, including Perceived Stress Scale (PSS-10). Result(s): A total of 97 patients (49 males;mean age 18.8 +/- 4.5 years, mean diabetes duration 8.0 +/- 5.0 years;mean HbA1c 8.1 +/- 1.5%) were analyzed. Age (y) (r = 0.325, p = 0.005) and HbA1c (%) within the preceding 3 months (r = 0.274, p = 0.036) correlated positively with PSS-10 score, Figure 1. There was a statistically significant difference in PSS-10 score based on gender (t [70] = -2.147;p = 0.035), education (F [4,67] = 4.34, p = 0.003) and occupation (F [3,68] = 4.50, p = 0.006). On multiple linear regression, gender, occupation and HbA1c were the significant determinants of PSS-10 (F [3,55] =12.01, p < 0.001, R2 = 0.363). One-way ANOVA showed a significant impact of mean PSS-10 score on the glycemic control (F [2,69] = 3.813, p = 0.027). Conclusion(s): Female gender, salaried individuals, and pre-existing poorly controlled diabetes contributed to an increased risk of stress. Increased stress resulted in worsened glycemic control.

6.
15th International Conference on Social Computing, Behavioral-Cultural Modeling and Prediction and Behavior Representation in Modeling and Simulation Conference, SBP-BRiMS 2022 ; 13558 LNCS:46-56, 2022.
Article in English | Scopus | ID: covidwho-2059739

ABSTRACT

Focal Structures are key sets of individuals who may be responsible for coordinating events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structures that can promote online social campaigns is important but complex. Unlike influential individuals, focal structures can effect large-scale complex social processes. In our prior work, we applied a greedy algorithm and bi-level decomposition optimization solution to identify focal structures in social media networks. However, the outcomes lacked a contextual representation of the focal structures that affected interpretability. In this research, we present a novel Contextual Focal Structure Analysis (CFSA) model to enhance the discovery and the interpretability of the focal structures to provide the context in terms of the content shared by individuals in the focal structures through their communication network. The CFSA model utilizes multiplex networks, where the first layer is the users-users network based on mentions, replies, friends, and followers, and the second layer is the hashtag co-occurrence network. The two layers have interconnections based on the user hashtag relations. The model's performance was evaluated on real-world datasets from Twitter related to domestic extremist groups spreading information about COVID-19 and the Black Lives Matter (BLM) social movement during the 2020–2021 time. The model identified Contextual Focal Structure (CFS) sets revealing the context regarding individuals’ interests. We then evaluated the model's efficacy by measuring the influence of the CFS sets in the network using various network structural measures such as the modularity method, network stability, and average clustering coefficient values. The ranking Correlation Coefficient (RCC) was used to conduct a comparative evaluation with real-world scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

7.
5th International Conference on Communication, Device and Networking, ICCDN 2021 ; 902:401-412, 2023.
Article in English | Scopus | ID: covidwho-2048170

ABSTRACT

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

8.
2nd IEEE International Conference on Intelligent Technologies, CONIT 2022 ; 2022.
Article in English | Scopus | ID: covidwho-2029212

ABSTRACT

The prediction of future development of a natural phenomenon is one of the main objectives of recent technology, but this is a great challenge when dealing with an epidemic or pandemic. This proved to be particularly true in the case of Covid-19 global pandemic that the world is suffering and facing since January 2020. The response to the virus infection are partially known, however the immune system is mostly affected especially in patients with pre-existing respiratory or systemic diseases. Most infections by coronavirus are mild and self-treated. Therefore, in early stages of the disease, it will be misleading to estimate the real spread of the virus based on the reports of hospital. Moreover, such reports vary according to how measurements are performed, and the number of tests related only to the number of symptomatic patients. Despite all this, the large amount of official data published in last months, and updated daily has motivated various mathematical models, which are required to predict the evolution of an epidemic and plan effective control strategies. Due to the incompleteness of the data and intrinsic complexity, predicting the evolution, the peak or the end of the pandemic is a challenge. In this paper, a deep learning based approach is proposed aiming to evaluate a-priori risk of an epidemic caused by Covid-19. The proposed algorithm leverages image processing and deep learning algorithms to detect Covid and differentiate between normal, Covid affected, lung opacity and viral pneumonia affected chest x-rays. This results in setting strategies to prevent or decrease the impact of future epidemic waves. The accuracy for the proposed algorithm is 95.01% and Recall is 98.5% on validation data. The inference is that combining image processing with deep learning can improve performance of Covid detection. © 2022 IEEE.

9.
Journal of Clinical and Diagnostic Research ; 16(6):TR01-TR04, 2022.
Article in English | EMBASE | ID: covidwho-1928866

ABSTRACT

Computed Tomography has played a vital role in Coronavirus Disease 2019 (COVID-19) infection, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) over the last two years. The typical features of COVID-19 on High Resolution Computed Tomography (HRCT) of chest including ground glass opacities and consolidation with a peripheral and lower lobar predilection have been very well documented in literature worldwide. However, thin-walled lucencies in the lung parenchyma called cysts is not very well documented. Authors thus present a case series comprising six SARS-CoV-2 Reverse Transcription-Polymerase Chain Reaction (RT-PCR) positive patients admitted to the hospital during the period 1stApril 2021 to 31stMay 2021 with lung cysts on HRCT. It was a retrospective study wherein details of the patients were drawn from the case record sheets and the clinical parameters along with HRCT chest findings were analysed, and correlations were drawn to study the cause, timing and significance of these cysts. In this study, the cysts were found to be thin-walled, varying in size from 5-20 mm in diameter and subpleural in distribution with no obvious lobar predilection.The immediately surrounding lung parenchyma showed features of maximal involvement by the atypical pneumonitis. All six cases had moderate to severe lung involvement entailing oxygen therapy. The high flow oxygen therapy and its duration along with degree of lung involvement, are important determinants of cystic degeneration. In the present case series, cystic changes were observed somewhere between day 15 to day 40 of the disease and thus a part of postacute fibrosis in COVID-19 infection.

10.
2nd International Conference on Biologically Inspired Techniques in Many Criteria Decision Making, BITMDM 2021 ; 271:159-170, 2022.
Article in English | Scopus | ID: covidwho-1919731

ABSTRACT

Our article COVID-19 AWARENESS is created to provide latest and correct information regarding the current pandemic situation. It provides the public with statistics of active, recovered, and death cases country-wise, all around the world. It also provides them with latest news regarding the pandemic every 24 h. It also provides helpline numbers with search functions for the people to call for help. Our site also provides guidelines on preventive measures along with the steps that a person should follow if they are infected with the coronavirus. The other guidelines are displayed on the Web site in an attractive and responsive way. We also provide a search function for helpline numbers which searches on the basis on the name of a states or a part of it. Our article provides all the necessary information to the public that are required to be healthy and safe in these difficult times for the humanity because of the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

11.
Journal of SAFOG ; 14(2):136-143, 2022.
Article in English | EMBASE | ID: covidwho-1917986

ABSTRACT

Aim: We have witnessed diverse presentations of coronavirus disease-2019 (COVID-19) in pregnant females during first and second waves. The aim of this study was to evaluate the usefulness of chest X-ray and its correlation of severity scoring with clinical, laboratory parameters and maternal-fetal outcome during management of COVID-19 pregnant women in low resource settings. Methodology: This was a retrospective observational study conducted at the Government Institute of Medical Sciences, Greater Noida, from May 2020 to May 2021. The study included 185 pregnant women in second and third trimesters with reverse transcription-polymerase chain reaction (RT-PCR)-confirmed COVID-19 disease. The chest radiographs of all patients were analyzed and severity scoring was done using modified radiographic assessment of lung edema (RALE) criteria. The correlation of severity index with clinical and biochemical profile of patients with normal and abnormal X-ray findings was compared. Two-tailed p-value of <0.05 was considered significant in our study. Results: Out of 185 patients, 38 had abnormal X-ray findings, whereas 147 had normal X-ray. A significant difference was observed in mean values of lactate dehydrogenase (LDH), ferritin, C-reactive protein (CRP), D-dimer, total leukocyte count (TLC), and interleukin 6 (IL-6) levels across both X-ray groups. The proportion of pregnant mothers with live birth, high-risk pregnancy, steroid treatment, oxygen supplementation, invasive ventilation, and number of presenting symptoms varied statistically across both the X-ray groups (p-value <0.05). Receiver-operating characteristic (ROC) analysis revealed that an X-ray score of “5.5” has the best prognostic significance of maternal death with sensitivity of 87.5 and 96.6% specificity. Conclusion: Chest radiography for the assessment of disease status in COVID-19 pregnancies is an effective and affordable alternative to CT scan in low resource settings.

12.
Advanced Sciences and Technologies for Security Applications ; : 47-79, 2022.
Article in English | Scopus | ID: covidwho-1844294

ABSTRACT

Throughout the COVID-19 pandemic, people have grown more reliant on social media for obtaining news, information, and entertainment. However, the information environment has become a breeding ground for disinformation tactics. Formal recommendations from medical experts are becoming muffled by the avalanche of toxic content and social media echo chambers are being created in hopes that users only consume stories that support certain beliefs. Despite the advantages of utilizing online social networks (OSNs), a consensus is emerging suggesting the presence of an ever-growing population of malicious actors who utilize these networks to spread misinformation and harm others. These actors are using advanced techniques and are engaging on multiple platforms to propagate their disinformation campaigns. As such, researchers have had to evolve their methods to detect disinformation. In this chapter, we present novel multimethod socio-computational approaches to analyze disinformation content and actors on OSNs during the initial months after COVID-19 was made public. These techniques are presented as case studies in narrative analysis of COVID-19 misinformation themes on blogs, identifying anti-lockdown protestor coordination through connective action on Twitter, analysis of hate speech and divisive discourse on YouTube through toxicity analysis, and modeling of misinformation contagion using an epidemiological approach. We end the chapter by presenting a COVID-19 misinformation tracker tool developed in collaboration with the Arkansas Office of the Attorney General. Our results offer policymakers valuable data to make informed decisions about the information environment and derive appropriate and timely countermeasures to combat insidious forms of cyber threats. Our efforts demonstrate that when researchers coordinate with policymakers it can make a difference, especially when that coordination remains an ongoing process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

13.
12th International Conference on Computer Communication and Informatics, ICCCI 2022 ; 2022.
Article in English | Scopus | ID: covidwho-1831784

ABSTRACT

This work has mainly targeted in performing comparative real time predictive analysis of mortality rate after having COVID-19 vaccination using different machine learning approaches. In this paper various deep learning models viz. RNN, LSTM and CNN have been utilized to make future prediction on mortality rate on the basis of administered vaccine doses. Firstly, the dataset of confirmed active cases, death cases and administered vaccine doses have been converted from time-series format to supervised learning format, and secondly different deep learning models have been trained and compared based on the transformed dataset. The prediction analysis is performed strictly based on the newest COVID-19 Delta Variant infected cases. The predictive analysis has resulted 15.53% of reduction in mortality rate and 24.67% of reduction in confirmed active cases with increase in vaccination rate. © 2022 IEEE.

14.
Digital Government: Research and Practice ; 2(1), 2021.
Article in English | Scopus | ID: covidwho-1772444

ABSTRACT

Managing the ongoing COVID-19 (aka Coronavirus) pandemic has presented both challenges and new opportunities for urban local body administrators. With the Indian government's Smart City mission taking firm roots in some of the Indian cities, the authors share their learnings and experiences of how a Smart City Integrated Command and Control Centre (ICCC) can be extended to become the nerve centre of pandemic-related operations and management, leveraging the Smart City IoT infrastructure such as surveillance cameras for monitoring and enforcement. The authors are of the opinion that the lessons learned and experiences gained from these cities are extremely valuable and can easily be replicated in other cities in a relatively short time period, thus providing a standard and uniform method across the nation for handling epidemics in the future. © 2020 ACM.

15.
Studies in Computational Intelligence ; 1007:337-353, 2022.
Article in English | Scopus | ID: covidwho-1767464

ABSTRACT

The COVID-19 pandemic poses a major obstacle for educational systems. The pandemic has essentially upset the higher education part too, which is a basic determinant of a nation’s economic future. Students, parents, and instructors around the globe are feeling the uncommon expanding influence of the novel coronavirus as schools are closing down and isolation strategies are being requested to adapt to the worldwide pandemic. While governments and well-being authorities are doing their best hindering the episode, worldwide education systems are working together to react and give quality training to all during these troublesome occasions. On these difficult occasions, devices, assets, and tips are abundant;however, how would we discover ones that truly work? There is a rise in the usage of online meeting tools/apps for taking classes online like Zoom, Skype, Google Meet/Classroom, etc. and so is the hazard from these types of apps. Consequently, the objective of this study is to assess the role of ICT instruments accessible in imparting quality instruction and also to examine the effect and techniques for training division in India. The study also suggests ways to curb the security risks of online education using ICT. A structured questionnaire has been used to collect data using random purposive techniques from teachers using ICT for their teaching-learning process during the COVID-19 pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

16.
Indian Journal of Medical Microbiology ; 39:S57, 2021.
Article in English | EMBASE | ID: covidwho-1734464

ABSTRACT

Background:COVID-19 being an airborne High Consequence Infectious Disease (HCID) warrants early detection to con- tain spread especially in a pandemic that has gripped the world by storm. RT-PCR (Real time polymerase chain reaction) is considered the gold standard confirmatory test for COVID-19. The assay is based on detection of viral RNA and genes located in different regions of SARS-COV 2 genome with a potent detection limit of >=10 genomic copies per reaction. Average duration of RT-PCR result becoming negative from positive gives an idea as to how long a patient needs quaran- tine and also a perception of clinical recovery. Methods:This study includes 1766 positive patients tested at JLN Medical College Ajmer, Rajasthan, out of the total pa- tient who underwent RT-PCR testing from 26-08-2020 to 17-11-2020. The samples were collected through oro or naso- pharyngeal swabs. Automated RNA extraction was done using Thermofisher and Perkin Elmer machines and RT -PCR was done on Bio-Rad Machines. Results:Out of 1766 samples, 61 samples in the age group of 0-14 years (children and young adolescents) showed an average duration of 10.5 days and range of 3-18 days to be reported negative, 1537 in the age group of 15-65 years (working age population) had an average duration of 11.3 days and range 1 -32 days, 168 for >=66 years (elderly popula- tion) had an average duration of 10.7 days and range of 1-23 days. When gender is compared, 505 were females with average illness duration of 10.7 days and range 1-32 days and 1261 were males with average duration of 11.2 days and range 1-31 days. Conclusions:Once tested positive there is a very subtle difference in duration of being reported negative between the various age groups and gender with children and young adolescents getting an earlier negative result than others and females earlier than the male population.

17.
Working Paper Series National Bureau of Economic Research ; 33(56), 2021.
Article in English | GIM | ID: covidwho-1733006

ABSTRACT

Calls for eliminating prioritization for SARS-CoV-2 vaccines are growing amid concerns that prioritization reduces vaccination speed. We use an SEIR model to study the effects of vaccination distribution on public health, comparing prioritization policy and speed under mitigation measures that are either eased during the vaccine rollout or sustained through the end of the pandemic period. NASEM's recommended prioritization results in fewer deaths than no prioritization, but does not minimize total deaths. If mitigation measures are eased, abandoning NASEM will result in about 134,000 more deaths at 30 million vaccinations per month. Vaccination speed must be at least 53% higher under no prioritization to avoid increasing deaths. With sustained mitigation, discarding NASEM prioritization will result in 42,000 more deaths, requiring only a 26% increase in speed to hold deaths constant. Therefore, abandoning NASEM's prioritization to increase vaccination speed without substantially increasing deaths may require sustained mitigation.

18.
International Journal of Pharmaceutical and Clinical Research ; 14(2):31-37, 2022.
Article in English | EMBASE | ID: covidwho-1716740

ABSTRACT

Background: The novel coronavirus is not exclusively a respiratory disease but have a neurological manifestation that is associated with high rates of mortality and morbidity. The aim of study was to examine stroke in COVID-19 patient in a tertiary care hospital. Method: This was a single center retrospective, observational study of total 960 confirmed COVID-19 patients admitted in Ayush Hospital between July 1, 2020, and March 30, 2021. The medical history, demographic characteristics, laboratory and chest CT scan findings were extracted from electronic medical records. All neurological symptoms of stroke patients were reviewed and confirmed. The data were collected, segregated and analyzed. Results: The study shows that 0.7% COVID-19 patients had stroke during hospitalization. Further, the older patients, co morbidity (hypertensive) and severity of infection were found to be associated risk factors. Conclusion: The present study concludes that patients with older age group, co morbidity especially hypertensive and severe COVID-19 infection had possible risk factor for cerebrovascular disease like stroke.

19.
Online Social Networks and Media ; 28, 2022.
Article in English | Scopus | ID: covidwho-1712896

ABSTRACT

This research proposes a conceptual framework for determining the adoption trajectory of information diffusion in connective action campaigns. This approach reveals whether an information campaign is accelerating, reached critical mass, or decelerating during its life cycle. The experimental approach taken in this study builds on the diffusion of innovations theory, critical mass theory, and previous s-shaped production function research to provide ideas for modeling future connective action campaigns. Most social science research on connective action has taken a qualitative approach. There are limited quantitative studies, but most focus on statistical validation of the qualitative approach, such as surveys, or only focus on one aspect of connective action. In this study, we extend the social science research on connective action theory by applying a mixed-method computational analysis to examine the affordances and features offered through online social networks (OSNs) and then present a new method to quantify the emergence of these action networks. Using the s-curves revealed through plotting the information campaigns usage, we apply a diffusion of innovations lens to the analysis to categorize users into different stages of adoption of information campaigns. We then categorize the users in each campaign by examining their affordance and interdependence relationships by assigning retweets, mentions, and original tweets to the type of relationship they exhibit. The contribution of this analysis provides a foundation for mathematical characterization of connective action signatures, and further, offers policymakers insights about campaigns as they evolve. To evaluate our framework, we present a comprehensive analysis of COVID-19 Twitter data. Establishing this theoretical framework will help researchers develop predictive models to more accurately model campaign dynamics. © 2022

20.
13th IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2021 ; : 309-312, 2021.
Article in English | Scopus | ID: covidwho-1709534

ABSTRACT

Blogging has become an essential part of the new print media of the 21st century despite the emergence of social media platforms like Twitter and Facebook, with many news agencies, media outlets, journalists and users using this medium to write without any restriction on topics of choice or events that happen over the world. Although social networking sites have also become a hotbed where users share their views, it suffers from distraction when users try to air their views on topics that affect them due to character limitation, real-time toxic behavior, and content ownership rights. Social networking sites like Facebook, Twitter and Reddit are sometimes used to drive traffic to blogs sites. The blogosphere, defined as the network of blogs, is growing at an exponential rate. Medium.com and WordPress.com are among the top blogging platforms, with WordPress leading the way as a top blogging platform and followed by other platforms like Medium, Hashnode, Tumblr, and blogger. Analyzing blog data helps understand the pulse of a society, know what resonates with a community, and recognize the grievances of a group, among other reasons. Since there is no character limit in blogs, unlike Twitter, blogs allow much depth in discourse, allowing it to be an effective platform for setting narratives. Blogs also provide a convenient platform to develop situational awareness during a socio-political crisis or humanitarian crisis in a conflict-torn region or a disaster-struck area. To address the difficulty of having a publicly accessible blog data analytical solution since solutions like Blogdex, among others, were either discontinued or made proprietary, we present BlogTracker. This tool helps users analyze public discussions with real-time data update capability and analyze narratives and emotion distribution on associated blog posts and trackers. This demonstration shows how the BlogTracker application analyses blog data with a case study about COVID-19. © 2021 ACM.

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