Search Results
Check square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features
2020, Cheema, Gullasl S., Hakimov, Sherzod, Ewerth, Ralph, Cappellato, Linda, Eickhoff, Carsten, Ferro, Nicola, Névéol, Aurélie
In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first prob-lem, claim check-worthiness prediction, we explore the fusion of syntac-tic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similar-ity, and perform KD-search to retrieve verified claims with respect to a query tweet.
An Overview on the Role of Relative Humidity in Airborne Transmission of SARS-CoV-2 in Indoor Environments
2020, Ahlawat, Ajit, Wiedensohler, Alfred, Mishra, Sumit Kumar
COVID-19 disease is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which originated in Wuhan, China and spread with an astonishing rate across the world. The transmission routes of SARS-CoV-2 are still debated, but recent evidence strongly suggests that COVID-19 could be transmitted via air in poorly ventilated places. Some studies also suggest the higher surface stability of SARS-CoV-2 as compared to SARS-CoV-1. It is also possible that small viral particles may enter into indoor environments from the various emission sources aided by environmental factors such as relative humidity, wind speed, temperature, thus representing a type of an aerosol transmission. Here, we explore the role of relative humidity in airborne transmission of SARS-CoV-2 virus in indoor environments based on recent studies around the world. Humidity affects both the evaporation kinematics and particle growth. In dry indoor places i.e., less humidity (< 40% RH), the chances of airborne transmission of SARS-CoV-2 are higher than that of humid places (i.e., > 90% RH). Based on earlier studies, a relative humidity of 40–60% was found to be optimal for human health in indoor places. Thus, it is extremely important to set a minimum relative humidity standard for indoor environments such as hospitals, offices and public transports for minimization of airborne spread of SARS-CoV-2. © The Author(s).
The COVID‐19 Pandemic Not Only Poses Challenges, but Also Opens Opportunities for Sustainable Transformation
2021, Pradhan, Prajal, Subedi, Daya Raj, Khatiwada, Dilip, Joshi, Kirti Kusum, Kafle, Sagar, Chhetri, Raju Pandit, Dhakal, Shobhakar, Gautam, Ambika Prasad, Khatiwada, Padma Prasad, Mainaly, Jony, Onta, Sharad, Pandey, Vishnu Prasad, Parajuly, Keshav, Pokharel, Sijal, Satyal, Poshendra, Singh, Devendra Raj, Talchabhadel, Rocky, Tha, Rupesh, Thapa, Bhesh Raj, Adhikari, Kamal, Adhikari, Shankar, Chandra Bastakoti, Ram, Bhandari, Pitambar, Bharati, Saraswoti, Bhusal, Yub Raj, Bahadur BK, Man, Bogati, Ramji, Kafle, Simrin, Khadka, Manohara, Khatiwada, Nawa Raj, Lal, Ajay Chandra, Neupane, Dinesh, Neupane, Kaustuv Raj, Ojha, Rajit, Regmi, Narayan Prasad, Rupakheti, Maheswar, Sapkota, Alka, Sapkota, Rupak, Sharma, Mahashram, Shrestha, Gitta, Shrestha, Indira, Shrestha, Khadga Bahadur, Tandukar, Sarmila, Upadhyaya, Shyam, Kropp, Jürgen P., Bhuju, Dinesh Raj
The COVID-19 pandemic has impacted social, economic, and environmental systems worldwide, slowing down and reversing the progress made in achieving the Sustainable Development Goals (SDGs). SDGs belong to the 2030 Agenda to transform our world by tackling humankind's challenges to ensure well-being, economic prosperity, and environmental protection. We explore the potential impacts of the pandemic on SDGs for Nepal. We followed a knowledge co-creation process with experts from various professional backgrounds, involving five steps: online survey, online workshop, assessment of expert's opinions, review and validation, and revision and synthesis. The pandemic has negatively impacted most SDGs in the short term. Particularly, the targets of SDG 1, 4, 5, 8, 9, 10, 11, and 13 have and will continue to have weakly to moderately restricting impacts. However, a few targets of SDG 2, 3, 6, and 11 could also have weakly promoting impacts. The negative impacts have resulted from impeding factors linked to the pandemic. Many of the negative impacts may subside in the medium and long terms. The key five impeding factors are lockdowns, underemployment and unemployment, closure of institutions and facilities, diluted focus and funds for non-COVID-19-related issues, and anticipated reduction in support from development partners. The pandemic has also opened a window of opportunity for sustainable transformation, which is short-lived and narrow. These opportunities are lessons learned for planning and action, socio-economic recovery plan, use of information and communication technologies and the digital economy, reverse migration and “brain gain,” and local governments' exercising authorities.
Resorting to Context-Aware Background Knowledge for Unveiling Semantically Related Social Media Posts
2022, Sakor, Ahmad, Singh, Kuldeep, Vidal, Maria-Esther
Social media networks have become a prime source for sharing news, opinions, and research accomplishments in various domains, and hundreds of millions of posts are announced daily. Given this wealth of information in social media, finding related announcements has become a relevant task, particularly in trending news (e.g., COVID-19 or lung cancer). To facilitate the search of connected posts, social networks enable users to annotate their posts, e.g., with hashtags in tweets. Albeit effective, an annotation-based search is limited because results will only include the posts that share the same annotations. This paper focuses on retrieving context-related posts based on a specific topic, and presents PINYON, a knowledge-driven framework, that retrieves associated posts effectively. PINYON implements a two-fold pipeline. First, it encodes, in a graph, a CORPUS of posts and an input post; posts are annotated with entities for existing knowledge graphs and connected based on the similarity of their entities. In a decoding phase, the encoded graph is used to discover communities of related posts. We cast this problem into the Vertex Coloring Problem, where communities of similar posts include the posts annotated with entities colored with the same colors. Built on results reported in the graph theory, PINYON implements the decoding phase guided by a heuristic-based method that determines relatedness among posts based on contextual knowledge, and efficiently groups the most similar posts in the same communities. PINYON is empirically evaluated on various datasets and compared with state-of-the-art implementations of the decoding phase. The quality of the generated communities is also analyzed based on multiple metrics. The observed outcomes indicate that PINYON accurately identifies semantically related posts in different contexts. Moreover, the reported results put in perspective the impact of known properties about the optimality of existing heuristics for vertex graph coloring and their implications on PINYON scalability.
On the Role of Images for Analyzing Claims in Social Media
2021, Cheema, Gullal S., Hakimov, Sherzod, Müller-Budack, Eric, Ewerth, Ralph
Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
Identification of herbal teas and their compounds eliciting antiviral activity against SARS-CoV-2 in vitro
2022, Le-Trilling, Vu Thuy Khanh, Mennerich, Denise, Schuler, Corinna, Sakson, Roman, Lill, Julia K., Kasarla, Siva Swapna, Kopczynski, Dominik, Loroch, Stefan, Flores-Martinez, Yulia, Katschinski, Benjamin, Wohlgemuth, Kerstin, Gunzer, Matthias, Meyer, Folker, Phapale, Prasad, Dittmer, Ulf, Sickmann, Albert, Trilling, Mirko
Background: The SARS-CoV-2/COVID-19 pandemic has inflicted medical and socioeconomic havoc, and despite the current availability of vaccines and broad implementation of vaccination programs, more easily accessible and cost-effective acute treatment options preventing morbidity and mortality are urgently needed. Herbal teas have historically and recurrently been applied as self-medication for prophylaxis, therapy, and symptom alleviation in diverse diseases, including those caused by respiratory viruses, and have provided sources of natural products as basis for the development of therapeutic agents. To identify affordable, ubiquitously available, and effective treatments, we tested herbs consumed worldwide as herbal teas regarding their antiviral activity against SARS-CoV-2. Results: Aqueous infusions prepared by boiling leaves of the Lamiaceae perilla and sage elicit potent and sustained antiviral activity against SARS-CoV-2 when applied after infection as well as prior to infection of cells. The herbal infusions exerted in vitro antiviral effects comparable to interferon-β and remdesivir but outperformed convalescent sera and interferon-α2 upon short-term treatment early after infection. Based on protein fractionation analyses, we identified caffeic acid, perilla aldehyde, and perillyl alcohol as antiviral compounds. Global mass spectrometry (MS) analyses performed comparatively in two different cell culture infection models revealed changes of the proteome upon treatment with herbal infusions and provided insights into the mode of action. As inferred by the MS data, induction of heme oxygenase 1 (HMOX-1) was confirmed as effector mechanism by the antiviral activity of the HMOX-1-inducing compounds sulforaphane and fraxetin. Conclusions: In conclusion, herbal teas based on perilla and sage exhibit antiviral activity against SARS-CoV-2 including variants of concern such as Alpha, Beta, Delta, and Omicron, and we identified HMOX-1 as potential therapeutic target. Given that perilla and sage have been suggested as treatment options for various diseases, our dataset may constitute a valuable resource also for future research beyond virology.
The role of blogs and news sites in science communication during the COVID-19 pandemic
2022, Fraumann, Grischa, Colavizza, Giovanni
We present a brief review of literature related to blogs and news sites; our focus is on publications related to COVID-19. We primarily focus on the role of blogs and news sites in disseminating research on COVID-19 to the wider public, that is knowledge transfer channels. The review is for researchers and practitioners in scholarly communication and social media studies of science who would like to find out more about the role of blogs and news sites during the COVID-19 pandemic. From our review, we see that blogs and news sites are widely used as scholarly communication channels and are closely related to each other. That is, the same research might be reported in blogs and news sites at the same time. They both play a particular role in higher education and research systems, due to the increasing blogging and science communication activity of researchers and higher education institutions (HEIs). We conclude that these two media types have been playing an important role for a long time in disseminating research, which even increased during the COVID-19 pandemic. This can be verified, for example, through knowledge graphs on COVID-19 publications that contain a significant amount of scientific publications mentioned in blogs and news sites.
Changes in black carbon emissions over Europe due to COVID-19 lockdowns
2021, Evangeliou, Nikolaos, Platt, Stephen M., Eckhardt, Sabine, Lund Myhre, Cathrine, Laj, Paolo, Alados-Arboledas, Lucas, Backman, John, Brem, Benjamin T., Fiebig, Markus, Flentje, Harald, Marinoni, Angela, Pandolfi, Marco, Yus-Dìez, Jesus, Prats, Natalia, Putaud, Jean P., Sellegri, Karine, Sorribas, Mar, Eleftheriadis, Konstantinos, Vratolis, Stergios, Wiedensohler, Alfred, Stohl, Andreas
Following the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) responsible for COVID-19 in December 2019 in Wuhan (China) and its spread to the rest of the world, the World Health Organization declared a global pandemic in March 2020. Without effective treatment in the initial pandemic phase, social distancing and mandatory quarantines were introduced as the only available preventative measure. In contrast to the detrimental societal impacts, air quality improved in all countries in which strict lockdowns were applied, due to lower pollutant emissions. Here we investigate the effects of the COVID-19 lockdowns in Europe on ambient black carbon (BC), which affects climate and damages health, using in situ observations from 17 European stations in a Bayesian inversion framework. BC emissions declined by 23 kt in Europe (20 % in Italy, 40 % in Germany, 34 % in Spain, 22 % in France) during lockdowns compared to the same period in the previous 5 years, which is partially attributed to COVID-19 measures. BC temporal variation in the countries enduring the most drastic restrictions showed the most distinct lockdown impacts. Increased particle light absorption in the beginning of the lockdown, confirmed by assimilated satellite and remote sensing data, suggests residential combustion was the dominant BC source. Accordingly, in central and Eastern Europe, which experienced lower than average temperatures, BC was elevated compared to the previous 5 years. Nevertheless, an average decrease of 11 % was seen for the whole of Europe compared to the start of the lockdown period, with the highest peaks in France (42 %), Germany (21 %), UK (13 %), Spain (11 %) and Italy (8 %). Such a decrease was not seen in the previous years, which also confirms the impact of COVID-19 on the European emissions of BC.
Beyond just “flattening the curve”: Optimal control of epidemics with purely non-pharmaceutical interventions
2020, Kantner, Markus, Koprucki, Thomas
When effective medical treatment and vaccination are not available, non-pharmaceutical interventions such as social distancing, home quarantine and far-reaching shutdown of public life are the only available strategies to prevent the spread of epidemics. Based on an extended SEIR (susceptible-exposed-infectious-recovered) model and continuous-time optimal control theory, we compute the optimal non-pharmaceutical intervention strategy for the case that a vaccine is never found and complete containment (eradication of the epidemic) is impossible. In this case, the optimal control must meet competing requirements: First, the minimization of disease-related deaths, and, second, the establishment of a sufficient degree of natural immunity at the end of the measures, in order to exclude a second wave. Moreover, the socio-economic costs of the intervention shall be kept at a minimum. The numerically computed optimal control strategy is a single-intervention scenario that goes beyond heuristically motivated interventions and simple “flattening of the curve”. Careful analysis of the computed control strategy reveals, however, that the obtained solution is in fact a tightrope walk close to the stability boundary of the system, where socio-economic costs and the risk of a new outbreak must be constantly balanced against one another. The model system is calibrated to reproduce the initial exponential growth phase of the COVID-19 pandemic in Germany. © 2020, The Author(s).
How to Optimize the Supply and Allocation of Medical Emergency Resources During Public Health Emergencies
2020, Wang, Chunyu, Deng, Yue, Yuan, Ziheng, Zhang, Chijun, Zhang, Fan, Cai, Qing, Gao, Chao, Kurths, Jürgen
The solutions to the supply and allocation of medical emergency resources during public health emergencies greatly affect the efficiency of epidemic prevention and control. Currently, the main problem in computational epidemiology is how the allocation scheme should be adjusted in accordance with epidemic trends to satisfy the needs of population coverage, epidemic propagation prevention, and the social allocation balance. More specifically, the metropolitan demand for medical emergency resources varies depending on different local epidemic situations. It is therefore difficult to satisfy all objectives at the same time in real applications. In this paper, a data-driven multi-objective optimization method, called as GA-PSO, is proposed to address such problem. It adopts the one-way crossover and mutation operations to modify the particle updating framework in order to escape the local optimum. Taking the megacity Shenzhen in China as an example, experiments show that GA-PSO effectively balances different objectives and generates a feasible allocation strategy. Such a strategy does not only support the decision-making process of the Shenzhen center in terms of disease control and prevention, but it also enables us to control the potential propagation of COVID-19 and other epidemics. © Copyright © 2020 Wang, Deng, Yuan, Zhang, Zhang, Cai, Gao and Kurths.