Enabling low-resource transfer learning across COVID-19 corpora by combining event-extraction and co-training

Social-science investigations can benefit from a direct comparison of heterogenous corpora: in this work, we compare U.S. state-level COVID-19 policy announcements with policy discussions on Twitter. To perform this task, we require classifiers with high transfer accuracy to both (1) classify policy announcements and (2) classify tweets. We find that co-training using event-extraction views significantly improves the transfer accuracy of our RoBERTa classifier by 3% above a RoBERTa baseline and 11% above other baselines. The same improvements are not observed for baseline views. With a set of 576 COVID-19 policy announcements, hand-labeled into 1 of 6 categories, our classifier observes a maximum transfer accuracy of .77 f1-score on a hand-validated set of tweets. This work represents the first known application of these techniques to an NLP transfer learning task and facilitates cross-corpora comparisons necessary for studies of social science phenomena. © ACL 2020.All right reserved.

Orthodontic emergencies and patients' perceptions of orthodontic patients during the COVID-19 pandemic

Aim: This retrospective research was aimed to evaluate the impact of coronavirus disease 2019 (COVID-19) on orthodontic emergencies and Patients' perceptions of orthodontic patients. Material(s) and Method(s): A total of 204 patients were gathered who were not seen for nearly 5 months from the first of March 2020 to the end of July 2020 due to dental clinic closure. The mean age of the samples was 20.2 (SD = 12.5) years consisting of 134 females (66%) and 70 males (34%). All patients had undergone active orthodontic treatment with fixed and removable appliances before the pandemic. The survey included demographics, types of orthodontic emergencies, and Patients' perceptions of orthodontic treatment during the closure of the dental clinic. Continuous variables were analyzed by mean and standard deviation, while categorical variables were analyzed by frequency and percentage. Result(s): In general, 46.5% of the patients suffered from various emergencies. The incidence of emergencies was approximately 3 times higher than that of the normal appointment times. Debonding and poking wire had the most frequently reported classification respectively (14.2%) (7.9%). 30.4% of patients stated that pandemic had a significant impact on the efficacy of orthodontic treatment. Conclusion(s): This study showed that the COVID-19 pandemic had a negative impact on patient care due to a higher number of emergencies and in turns, it delayed the therapeutic progress of patients. 16% of patients with active orthodontic appliances did not continue their treatment due to pandemics. More than half of the patients were willing to be seen every 8 weeks. Copyright © by BIOLIFE, s.a.s.

Navigation systems in maxillofacial surgery and neurosurgery and their potential advantages

Operating microscopes, navigation systems and intraoperative neurophysiological monitoring are essential in modern neurosurgical and maxillofacial procedures. Advances in surgical planning in neurosurgery and maxillofacial surgery led to the more common navigation system that helps surgeons know more information and ultimately do more for their patients. The benefits of a contemporary navigation system in the complicated brain, skull-base, maxillofacial and spine surgery are undeniable. Workflow analyses and cost-benefit evaluations must be carried out to increase the efficiency of neuronavigation systems in the next future.

Augmented and virtual reality as training tool for maxillofacial surgeons and neurosurgeons: is it the future?

Recent biotechnological advances, including three-dimensional microscopy and endoscopy, virtual reality, surgical simulation, surgical robotics, and advanced neuroimaging, have moulded the surgeon-computer relationship. For developing neurosurgeons and maxillofacial surgeons, such tools can reduce the learning curve, improve conceptual understanding of complex anatomy, and enhance visuospatial skills. However, current clinical trials in dental virtual reality must still be experimental.

Surgical multidisciplinarity for precision medicine: the challenge for the future

Multidisciplinary care has been shown to improve patient outcomes, and interprofessional collaboration has been shown to improve one's medical knowledge. Multidisciplinary interventions in the field of surgery are designed to address a specific problem occurring in a particular patient population and/or within the context of an individual hospital system. The importance of multidisciplinarity and interdisciplinarity at all levels, including clinical oncology, craniofacial trauma, and brain abscess caused by dental peri-implantitis, is well established. The challenge for future research is to further develop and validate medical team performance assessment instruments;this will help improve medical and surgical team training efforts and aid the design of clinical work systems supporting effective teamwork and safe patient care.

Neurosurgical and craniofacial surgery during the Covid-19 pandemic

The pandemic of coronavirus disease (COVID-19) resulted in an unprecedented global public health crisis and impacted all spheres of life, including all economic activity, travel, governance, education, surgery and, of course, healthcare. Neurosurgery, dentistry, and maxillofacial surgery are also not spared. Surgeons operating near the aerodigestive tract are at particularly high risk of being infected, and consequently, they shifted their practices toward more protective personal protective equipment. In the present digital era, surgeons use more and more web, teleconsulting, and virtual reality to effectively communicate with patients and their relatives on treatment strategies and appointments for surgical works. The COVID-19 situation also provides a novel opportunity to learn, update our knowledge, and update ourselves such that we continue to save lives. In conclusion, it seems appropriate to request that every healthcare institution receives well-researched and documented protocols for dealing with future inevitable global pandemics. © by BIOLIFE, s.a.s.

Periodontal disease and clinical association with COVID-19 infection

SARS-CoV-2 infection can cause long-standing damage to the immune system characterized by increased inflammatory cytokine activation. Maintaining periodontal health may reduce host susceptibility to COVID-19 and prevent COVID-19 aggravation in infected patients. There is sufficient evidence in the literature to warrant an association between the presence of PDs and the development and course of respiratory illnesses. Optimum oral health, maintaining good systemic health, and elimination of smoking habits may be beneficial for the prevention and management of COVID-19 infections. Future studies on the periodontal status of patients with COVID-19, including from mild to severe forms, could allow the opportune identification of people at risk of severe illness and generate relevant recommendations. The connection, if any, between the oral microbiome and COVID-19 complications is urgently required to establish the importance of oral hygiene and pre-existing oral disease in the severity and mortality risk of COVID-19.

Construction of Large-Scale Misinformation Labeled Datasets from Social Media Discourse using Label Refinement

Malicious accounts spreading misinformation has led to widespread false and misleading narratives in recent times, especially during the COVID-19 pandemic, and social media platforms struggle to eliminate these contents rapidly. This is because adapting to new domains requires human intensive fact-checking that is slow and difficult to scale. To address this challenge, we propose to leverage news-source credibility labels as weak labels for social media posts and propose model-guided refinement of labels to construct large-scale, diverse misinformation labeled datasets in new domains. The weak labels can be inaccurate at the article or social media post level where the stance of the user does not align with the news source or article credibility. We propose a framework to use a detection model self-trained on the initial weak labels with uncertainty sampling based on entropy in predictions of the model to identify potentially inaccurate labels and correct for them using self-supervision or relabeling. The framework will incorporate social context of the post in terms of the community of its associated user for surfacing inaccurate labels towards building a large-scale dataset with minimum human effort. To provide labeled datasets with distinction of misleading narratives where information might be missing significant context or has inaccurate ancillary details, the proposed framework will use the few labeled samples as class prototypes to separate high confidence samples into false, unproven, mixture, mostly false, mostly true, true, and debunk information. The approach is demonstrated for providing a large-scale misinformation dataset on COVID-19 vaccines. © 2022 Owner/Author.

Identifying Coordinated Accounts on Social Media through Hidden Influence and Group Behaviours

Disinformation campaigns on social media, involving coordinated activities from malicious accounts towards manipulating public opinion, have become increasingly prevalent. Existing approaches to detect coordinated accounts either make very strict assumptions about coordinated behaviours, or require part of the malicious accounts in the coordinated group to be revealed in order to detect the rest. To address these drawbacks, we propose a generative model, AMDN-HAGE (Attentive Mixture Density Network with Hidden Account Group Estimation) which jointly models account activities and hidden group behaviours based on Temporal Point Processes (TPP) and Gaussian Mixture Model (GMM), to capture inherent characteristics of coordination which is, accounts that coordinate must strongly influence each other's activities, and collectively appear anomalous from normal accounts. To address the challenges of optimizing the proposed model, we provide a bilevel optimization algorithm with theoretical guarantee on convergence. We verified the effectiveness of the proposed method and training algorithm on real-world social network data collected from Twitter related to coordinated campaigns from Russia's Internet Research Agency targeting the 2016 U.S. Presidential Elections, and to identify coordinated campaigns related to the COVID-19 pandemic. Leveraging the learned model, we find that the average influence between coordinated account pairs is the highest. On COVID-19, we found coordinated group spreading anti-vaccination, anti-masks conspiracies that suggest the pandemic is a hoax and political scam. © 2021 Owner/Author.

Digital civic participation and misinformation during the 2020 taiwanese presidential election

From fact-checking chatbots to community-maintained misinformation databases, Taiwan has emerged as a critical case-study for citizen participation in politics online. Due to Taiwan’s geopolitical history with China, the recent 2020 Taiwanese Presidential Election brought fierce levels of online engagement led by citizens from both sides of the strait. In this arti-cle, we study misinformation and digital participation on three platforms, namely Line, Twitter, and Taiwan’s Professional Technology Temple (PTT, Taiwan’s equivalent of Reddit). Each of these platforms presents a different facet of the elec-tions. Results reveal that the greatest level of disagreement occurs in discussion about incumbent president Tsai. Chinese users demonstrate emergent coordination and selective discussion around topics like China, Hong Kong, and President Tsai, whereas topics like Covid-19 are avoided. We discover an imbalance of the political presence of Tsai on Twitter, which sug-gests partisan practices in disinformation regulation. The cases of Taiwan and China point toward a growing trend where regular citizens, enabled by new media, can both exacerbate and hinder the flow of misinformation. The study highlights an overlooked aspect of misinformation studies, beyond the veracity of information itself, that is the clash of ideologies, practices, and cultural history that matter to democratic ideals. © 2021 by the authors;licensee Cogitatio (Lisbon, Portugal). tion 4.0 International License (CC BY).

ReCOVery: A Multimodal Repository for COVID-19 News Credibility Research

First identified in Wuhan, China, in December 2019, the outbreak of COVID-19 has been declared as a global emergency in January, and a pandemic in March 2020 by the World Health Organization (WHO). Along with this pandemic, we are also experiencing an "infodemic" of information with low credibility such as fake news and conspiracies. In this work, we present ReCOVery, a repository designed and constructed to facilitate research on combating such information regarding COVID-19. We first broadly search and investigate ∼2,000 news publishers, from which 60 are identified with extreme [high or low] levels of credibility. By inheriting the credibility of the media on which they were published, a total of 2,029 news articles on coronavirus, published from January to May 2020, are collected in the repository, along with 140,820 tweets that reveal how these news articles have spread on the Twitter social network. The repository provides multimodal information of news articles on coronavirus, including textual, visual, temporal, and network information. The way that news credibility is obtained allows a trade-off between dataset scalability and label accuracy. Extensive experiments are conducted to present data statistics and distributions, as well as to provide baseline performances for predicting news credibility so that future methods can be compared. Our repository is available at http://coronavirus-fakenews.com. © 2020 ACM.