Measuring the Interference Effect of Bots in Disseminating Opposing Viewpoints Related to COVID-19 on Twitter Using Epidemiological Modeling

The activity of bots can influence the opinions and behavior of people, especially within the political landscape where hot-button issues are debated. To evaluate the bot presence among the propagation trends of opposing politically-charged viewpoints on Twitter, we collected a comprehensive set of hashtags related to COVID-19. We then applied both the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models to three different dataset states including, total tweets in a dataset, tweets by bots, and tweets by humans. Our results show the ability of both models to model the diffusion of opposing viewpoints on Twitter, with the SEIZ model outperforming the SIR. Additionally, although our results show that both models can model the diffusion of information spread by bots with some difficulty, the SEIZ model outperforms. Our analysis also reveals that the magnitude of the bot-induced diffusion of this type of information varies by subject. © 2023 IEEE Computer Society. All rights reserved.

Applying an Epidemiological Model to Evaluate the Propagation of Toxicity related to COVID-19 on Twitter

The prevalence of social media has increased the propagation of toxic behavior among users. Toxicity can have detrimental effects on users' emotion and insight and disrupt beneficial discourse. Evaluating the propagation of toxic content on social networks such as Twitter can provide the opportunity to understand the characteristics of this harmful phenomena. Identifying a mathematical model that can describe the propagation of toxic content on social networks is a valuable approach to this evaluation. In this paper, we utilized the SEIZ (Susceptible, Exposed, Infected, Skeptic) epidemiological model to find a mathematical model for the propagation of toxic content related to COVID-19 topics on Twitter. We collected Twitter data based on specific hashtags related to different COVID-19 topics such as covid, mask, vaccine, and lockdown. The findings demonstrate that the SEIZ model can properly model the propagation of toxicity on a social network with relatively low error. Determining an efficient mathematical model can increase the understanding of the dynamics of the propagation of toxicity on a social network such as Twitter. This understanding can help researchers and policymakers to develop methods to limit the propagation of toxic content on social networks. © 2022 IEEE Computer Society. All rights reserved.

Profound Epistaxis in Cytokine Release Syndrome Following Chimeric Antigen Receptor T-Cell Therapy

INTRODUCTION: Chimeric antigen receptor T-cells (CAR-T) represent a promising immunotherapeutic approach in the treatment of refractory malignancies, but carry the risk of unique inflammatory toxicities including cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome (ICANS). In moderate to severe cases, these toxicities necessitate intensive care unit (ICU) admission for aggressive support and management. DESCRIPTION: A 63-year-old male with a history of pulmonary embolism, prostate cancer with resection and relapsed/refractory IgG-kappa multiple myeloma (diagnosed 4 years earlier and status-post multiple chemotherapy regimens) was admitted for conditioning chemotherapy and CAR-T cell infusion. 1 day post-infusion on the ward, he developed CRS and ICANS, with fever and altered mental status, for which he received tocilizumab, dexamethasone, and anakinra, in addition to empiric antibiotics. He progressed with worsening hypotension and encephalopathy and was admitted to the ICU and required vasopressors, pulse-dose steroids, and siltuximab. A nasal swab was performed to rule out COVID-19, following which he developed persistent epistaxis, requiring packing and, after aspiration of blood, intubation for airway protection. Laboratory data showed anemia and thrombocytopenia, prolonged PT and aPTT, low fibrinogen, and elevated levels of ferritin of 44,124 mg/ mL, D-Dimer of 1.35 mug/mL, interleukin-6 of 8,595 pg/ mL, interleukin-10 of 1,042 pg/mL, tumor necrosis factor- alpha of 103 pg/mL, and interferon-gamma above 244 pg/mL. The patient received numerous red cell and platelet transfusions, aminocaproic acid, cryoprecipitate, and additional packing and thrombin application by ENT before his epistaxis was controlled. Ultimately, he was weaned from vasopressors, extubated after two weeks and transferred out of the ICU, discharged to rehabilitation, and later home. DISCUSSION: Severe CRS can be associated with hemophagocytic lymphohistiocytosis and disseminated intravascular coagulation (DIC), which can lead to lifethreatening bleeding as demonstrated in our patient. Effective and timely treatment of bleeding associated with DIC and severe CRS can be life-saving. It behooves the intensivist to recognize the toxicities of CAR-T as therapeutic applications broaden in the coming years.

Developing Approaches to Detect and Mitigate COVID-19 Misinfodemic in Social Networks for Proactive Policymaking

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.

Applying diffusion of innovations theory to social networks to understand the stages of adoption in connective action campaigns

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

Development of a Cancer Support Nurse Telehealth Clinic to facilitate symptom management and urgent clinical review

Introduction: The COVID-19 pandemic required health services to find novel approaches to provide facilitate timely review for patients receiving systemic cancer therapies (SCT). Telehealth permits greater efficiency and access and visual assessment of specific adverse effects. A pilot Telehealth Cancer Support Nurse Review Clinic (TCSN-RC) was established. This service complements existing cancer support nurse service (CSNS). The clinic is staffed by advanced practice oncology nurses, with care guided by a triage tool and an established referral pathway. Aim: To explore the use of telehealth to enhance the CSNS in providing rapid review for symptom and toxicity management of patients receiving SCT. Method: Quality improvement methodology has been used to evaluate and improve the TCSN-RC. Patient and CSN (Cancer Support Nurse) experience survey questionnaires were developed and analysed. Regular project team meetings reviewing interim results has allowed for improvement of processes. Results : Since the commencement in October 2020, there have been 18 CSN telehealth consultations. Twenty-eight per cent (n = 5) of patients and 100% (n = 4) of Cancer Support Nurses (CSN) have completed an evaluation survey. All patients (n = 5) strongly agreed or agreed they felt confident using the technology, the telehealth saved them travel time, the need to see their GP and enabled them to better manage their own symptoms. Participant reported feeling reassured by seeing a familiar face and prompt service from knowledgeable nurses. They reported having good video connections, could see and hear well. All CSN's (n = 4) strongly agreed or agreed that the telehealth consultation was convenient for them, helped to conduct better symptom assessments and were comfortable with telehealth technology Conclusion: Early results indicate the TCSN-RC service is a valuable addition to the CSN's in the delivering symptom and urgent clinical review with ongoing evaluation of results and potential for improvements in process as the study continue.

The Ebb and flow of the COVID-19 misinformation themes

The COVID-19 pandemic has seen the emergence of unique misinformation narratives in various outlets, through social media, blogs, etc. This online misinformation has been proven to spread in a viral manner and has a direct impact on public safety. In an effort to improve public understanding, we curated a corpus of 543 misinformation pieces whittled down to 243 unique misinformation narratives along with third party proofs debunking these stories. Building upon previous applications of topic modeling to COVID-19 related material, we developed a tool leveraging topic modeling to create a chronological visualization of these stories. From our corpus of misinformation stories, this tool has shown to accurately represent the ground truth reported by our curator team. This highlights some of the misinformation narratives unique to the COVID-19 pandemic and provides a quick method to monitor and assess misinformation diffusion, enabling policymakers to identify themes to focus on for communication campaigns. © 2020 CEUR-WS. All rights reserved.