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The COVID-19 pandemic generated (and keeps generating) a huge corpus of news articles, easily retrievable in Factiva with very targeted queries. This dataset, generated with an ad-hoc parser and NLP pipeline, analyzes the frequency of lemmas and named entities in news articles (in German, French, Italian and English ) regarding Switzerland and COVID-19. The analysis of large bodies of grey literature via text mining and computational linguistics is an increasingly frequent approach to understand the large-scale trends of specific topics. We used Factiva, a news monitoring and search engine developed and owned by Dow Jones, to gather and download all the news articles published between January 2020 and May 2021 on Covid-19 and Switzerland. Due to Factiva's copyright policy, it is not possible to share the original dataset with the exports of the articles' text; however, we can share the results of our work on the corpus. All the information relevant to reproduce the results is provided. Factiva allows a very granular definition of the queries, and moreover has access to full text articles published by the major media outlet of the world. The query has been defined as follows (syntax in bold, explanation in italics): ((coronavirus or Wuhan virus or corvid19 or corvid 19 or covid19 or covid 19 or ncov or novel coronavirus or sars) and (atleast3 coronavirus or atleast3 wuhan or atleast3 corvid* or atleast3 covid* or atleast3 ncov or atleast3 novel or atleast3 corona*)) Keywords for covid19; must appear at least 3 times in the text and ns=(gsars or gout) Subject is “novel coronaviruses” or “outbreaks and epidemics” and “general news” and la=X Language is X (DE, FR, IT, EN) and rst=tmnb Restrict to TMNB (major news and business publications) and wc>300 At least 300 words and date from 20191001 to 20212005 Date interval and re=SWITZ Region is Switzerland It is important to specify some details that characterize the query. The query is not limited to articles published by Swiss media, but to articles regarding Switzerland. The reason is simple: a Swiss user googling for “Schweiz Coronavirus” or for “Coronavirus Ticino” can easily find and read articles published by foreign media outlets (namely, German or Italian) on that topic. If the objective is capturing and describing the information trends to which people are exposed, this approach makes much more sense than limiting the analysis to articles published by Swiss media. Factiva’s field “NS” is a descriptor for the content of the article. “gsars” is defined in Factiva’s documentation as “All news on Severe Acute Respiratory Syndrome”, and “gout” as “The widespread occurrence of an infectious disease affecting many people or animals in a given population at the same time”; however, the way these descriptors are assigned to articles is not specified in the documentation. Finally, the query has been restricted to major news and business publications of at least 300 words. Duplicate check is performed by Factiva. Given the incredibly large amount of articles published on COVID-19, this (absolutely arbitrary) restriction allows retrieving a corpus that is both meaningful and manageable. metadata.xlsx contains information about the articles retrieved (strategy, amount) This work is part of the PubliCo research project. This work is part of the PubliCo research project, supported by the Swiss National Science Foundation (SNF). Project no. 31CA30_195905

We have before us the sixth issue of INSAM Journal of Contemporary Music, Art and Technology. This is the second issue in a row dedicated to the global crisis caused by the Covid-19 pandemic. After the overwhelming response from all over the world to the call for papers and provocative inspections that ensued, here we wanted to discuss the ways in which technology shapes and enables work in the areas of music, arts, humanities, and the education process, this time inviting our collaborators to discuss the shortcomings and struggles of the working processes in these fields. The main theme, “Music, Art and Humanities in the Time of Global Crisis”, expanded from the Main Theme section into the interviews as well.

Background: Searching through the COVID-19 research literature to gain actionable clinical insight is a formidable task, even for experts. The usefulness of this corpus in terms of improving patient care is tied to the ability to see the big picture that emerges when the studies are seen in conjunction rather than in isolation. When the answer to a search query requires linking together multiple pieces of information across documents, simple keyword searches are insufficient. To answer such complex information needs, an innovative artificial intelligence (AI) technology named a knowledge graph (KG) could prove to be effective. Methods: We conducted an exploratory literature review of KG applications in the context of COVID-19. The search term used was "covid-19 knowledge graph". In addition to PubMed, the first five pages of search results for Google Scholar and Google were considered for inclusion. Google Scholar was used to include non-peer-reviewed or non-indexed articles such as pre-prints and conference proceedings. Google was used to identify companies or consortiums active in this domain that have not published any literature, peer-reviewed or otherwise. Results: Our search yielded 34 results on PubMed and 50 results each on Google and Google Scholar. We found KGs being used for facilitating literature search, drug repurposing, clinical trial mapping, and risk factor analysis. Conclusions: Our synopses of these works make a compelling case for the utility of this nascent field of research.

At a time when the Indian economy is in full swing and the growth rate has been declining since 2014, the picture is that Covid 19 has reached the economy by early 2020. Corona, a contagious disease that originated in China, is now spreading all over the world and across India. The disease has infected over 41,94,728 people worldwide to date. And you see it growing steadily. Developed as well as developing countries have not escaped its effects. The result of this Covid 19 is a question mark over human existence. The question is how to sustain the means of survival. The development to date has been hampered by Covid 19. It will create new solutions on how to sustain the development, but it will be difficult and laborious to fill the gaps that have been reached. The lockdown accepted by India has had an impact on the entire economy. In this, many global organizations have indicated that India's growth rate will be 0%.

This repository contains the datasets created and extracted for the paper: Giuseppe Serna García, Ruba Al Khalaf, Francesco Invernici, Stefano Ceri, and Anna Bernasconi. 2022. "CoVEffect: Interactive System for Mining the Effects of SARS-CoV-2 Mutations and Variants Based on Deep Learning". (Available online at http://gmql.eu/coveffect) -------------------------------------------------------------------------------- LIST OF FILES WITH DESCRIPTION: -------------------------------------------------------------------------------- AdditionalFile1-effects-taxonomy: Descriptions of legal values for the 'Effect' field, based on a categorized taxonomy. AdditionalFile2-levels-taxonomy: Descriptions of legal values for the 'Level' field. AdditionalFile3-training_dataset_target: List of target tuples (manually annotated) of 221 abstracts considered for training the model. For each abstract, target tuples follow the schema ID, DOI, title, entity, effect, level, type (mutation or variant), tuples_count (>1 when an effect/level is shared by multiple entities, #abstracts containing the same effect described in the tuple). AdditionalFile4-validation_dataset_target: List of target tuples (manually annotated) of 50 abstracts considered for validating the prepared prediction model. For each abstract, target tuples follow the schema defined for AdditionalFile3. AdditionalFile5-validation_dataset_highlighted: Textual abstracts of the 50 manuscripts considered for validation; the text used to support the manual target annotations has been highlighted in yellow. AdditionalFile6-validation_dataset_prediction: List of predicted annotations of 50 abstracts considered for validating the prepared prediction model. The file is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d). AdditionalFile7-keywords_query_list: Keyword-based search run on the CORD-19 dataset to extract a relevant subset of abstracts regarding the scope of interest of CoVEffect. The Boolean logic used to combine keywords is explained in the section 'Annotations of the biology-related CORD-19 cluster'. AdditionalFile8-CORD-19_batch_dataset_metadata: Metadata of the 7,230 papers extracted by the keyword-based query in AdditionalFile7. These abstracts have been annotated by the prediction framework. AdditionalFile9-CORD-19_batch_dataset_prediction: List of predicted annotations of 7,230 abstracts extracted from the biology-related cluster of CORD-19. AdditionalFile10-test_dataset_target: List of target tuples (manually annotated) of 100 abstracts randomly selected from the 7,230 extracted as in AdditionalFile8. For each abstract, target tuples follow the schema defined for AdditionalFile3. AdditionalFile11-test_dataset_prediction: List of predicted annotations of 100 abstracts considered for testing the prediction model on a subset of the CORD-19 biology-related cluster. As AdditionalFile6, it is split in 4 TSV, respectively for entity (a), effect (b), level (c), and whole tuple predictions (d).

Underlying data of the article: "Open Access of COVID-19 related publications in the first quarter of 2020: a preliminary study based in PubMed". Version 1 and Version 2 (after Unpaywall update). Data was analysed using Unpaywall and OpenRefine.

In the seventh issue of INSAM Journal of Contemporary Music, Art and Technology, we are continuing our series on themes dedicated to art, music, and humanities in times of global crisis. After dealing with more general questions regarding these areas of creation, in this volume we are thinking about the issue of mental and bodily health during the Covid-19 pandemic and its possible ties and representations in music and art.

The peer-reviewed publication for this dataset has been presented in the 2022 Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL), and can be accessed here: https://arxiv.org/abs/2205.02596. Please cite this when using the dataset. This dataset contains a heterogeneous set of True and False COVID claims and online sources of information for each claim. The claims have been obtained from online fact-checking sources, existing datasets and research challenges. It combines different data sources with different foci, thus enabling a comprehensive approach that combines different media (Twitter, Facebook, general websites, academia), information domains (health, scholar, media), information types (news, claims) and applications (information retrieval, veracity evaluation). The processing of the claims included an extensive de-duplication process eliminating repeated or very similar claims. The dataset is presented in a LARGE and a SMALL version, accounting for different degrees of similarity between the remaining claims (excluding respectively claims with a 90% and 99% probability of being similar, as obtained through the MonoT5 model). The similarity of claims was analysed using BM25 (Robertson et al., 1995; Crestani et al., 1998; Robertson and Zaragoza, 2009) with MonoT5 re-ranking (Nogueira et al., 2020), and BERTScore (Zhang et al., 2019). The processing of the content also involved removing claims making only a direct reference to existing content in other media (audio, video, photos); automatically obtained content not representing claims; and entries with claims or fact-checking sources in languages other than English. The claims were analysed to identify types of claims that may be of particular interest, either for inclusion or exclusion depending on the type of analysis. The following types were identified: (1) Multimodal; (2) Social media references; (3) Claims including questions; (4) Claims including numerical content; (5) Named entities, including: PERSON − People, including fictional; ORGANIZATION − Companies, agencies, institutions, etc.; GPE − Countries, cities, states; FACILITY − Buildings, highways, etc. These entities have been detected using a RoBERTa base English model (Liu et al., 2019) trained on the OntoNotes Release 5.0 dataset (Weischedel et al., 2013) using Spacy. The original labels for the claims have been reviewed and homogenised from the different criteria used by each original fact-checker into the final True and False labels. The data sources used are: - The CoronaVirusFacts/DatosCoronaVirus Alliance Database. https://www.poynter.org/ifcn-covid-19-misinformation/ - CoAID dataset (Cui and Lee, 2020) https://github.com/cuilimeng/CoAID - MM-COVID (Li et al., 2020) https://github.com/bigheiniu/MM-COVID - CovidLies (Hossain et al., 2020) https://github.com/ucinlp/covid19-data - TREC Health Misinformation track https://trec-health-misinfo.github.io/ - TREC COVID challenge (Voorhees et al., 2021; Roberts et al., 2020) https://ir.nist.gov/covidSubmit/data.html The LARGE dataset contains 5,143 claims (1,810 False and 3,333 True), and the SMALL version 1,709 claims (477 False and 1,232 True). The entries in the dataset contain the following information: - Claim. Text of the claim. - Claim label. The labels are: False, and True. - Claim source. The sources include mostly fact-checking websites, health information websites, health clinics, public institutions sites, and peer-reviewed scientific journals. - Original information source. Information about which general information source was used to obtain the claim. - Claim type. The different types, previously explained, are: Multimodal, Social Media, Questions, Numerical, and Named Entities. Funding. This work was supported by the UK Engineering and Physical Sciences Research Council (grant no. EP/V048597/1, EP/T017112/1). ML and YH are supported by Turing AI Fellowships funded by the UK Research and Innovation (grant no. EP/V030302/1, EP/V020579/1). References - Arana-Catania M., Kochkina E., Zubiaga A., Liakata M., Procter R., He Y.. Natural Language Inference with Self-Attention for Veracity Assessment of Pandemic Claims. NAACL 2022 https://arxiv.org/abs/2205.02596 - Stephen E Robertson, Steve Walker, Susan Jones, Micheline M Hancock-Beaulieu, Mike Gatford, et al. 1995. Okapi at trec-3. Nist Special Publication Sp,109:109. - Fabio Crestani, Mounia Lalmas, Cornelis J Van Rijsbergen, and Iain Campbell. 1998. “is this document relevant?. . . probably” a survey of probabilistic models in information retrieval. ACM Computing Surveys (CSUR), 30(4):528–552. - Stephen Robertson and Hugo Zaragoza. 2009. The probabilistic relevance framework: BM25 and beyond. Now Publishers Inc. - Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, and Jimmy Lin. 2020. Document ranking with a pre-trained sequence-to-sequence model. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: Findings, pages 708–718. - Tianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q Weinberger, and Yoav Artzi. 2019. Bertscore: Evaluating text generation with bert. In International Conference on Learning Representations. - Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. Roberta: A robustly optimized bert pretraining approach. arXiv preprint arXiv:1907.11692. - Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, et al. 2013. Ontonotes release 5.0 ldc2013t19. Linguistic Data Consortium, Philadelphia, PA, 23. - Limeng Cui and Dongwon Lee. 2020. Coaid: Covid-19 healthcare misinformation dataset. arXiv preprint arXiv:2006.00885. - Yichuan Li, Bohan Jiang, Kai Shu, and Huan Liu. 2020. Mm-covid: A multilingual and multimodal data repository for combating covid-19 disinformation. - Tamanna Hossain, Robert L. Logan IV, Arjuna Ugarte, Yoshitomo Matsubara, Sean Young, and Sameer Singh. 2020. COVIDLies: Detecting COVID-19 misinformation on social media. In Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, Online. Association for Computational Linguistics. - Ellen Voorhees, Tasmeer Alam, Steven Bedrick, Dina Demner-Fushman, William R Hersh, Kyle Lo, Kirk Roberts, Ian Soboroff, and Lucy Lu Wang. 2021. Trec-covid: constructing a pandemic information retrieval test collection. In ACM SIGIR Forum, volume 54, pages 1–12. ACM New York, NY, USA.

A 6 flyer-set (1HEX): #01—attempt; #02—try again; #03—do something else; #04—return; #05—unlearn; #06—crossover. In this flyer-series, content is queried as a vectored relationship between image and writing. The reference framework is Samuel Beckett’s queries in the “novellas” Ill Seen Ill Said/Company/ Worstward Ho/Stirrings Still (FF—faber & faber). The concept of excavation determines the hatching of content through a work of staging: the performed container. A ontological status is ascribed to such containers: they feature a category of dis/play which is neither an exhibit, nor a show. The lineup (Germ. Ausstellung, Norw. Oppstilling) is discussed as a dis/play in which the nature of what is seen and said is yet undetermined, or uncertain. From this basis the notion that the Beckett estate clusters the signature, event and context—rather than being external to Beckett’s work—is suggested. From this ground the possibility that Covid19 (pandemic and the lockdown as life-work) can fruitfully be considered an estate, is tentatively demonstrated and argued, from the vantage point of terrestrial estates, that may hatch and develop in the wake of Bruno Latour’s work, and Donna Haraway’s scheme of staying with the trouble. Which is also why the geological time-frame (cf, anthropocene) is discussed. Hereby, the /Covid-19 estate/ is launched.

ABSTRACTObjectives: We aimed to understand the public perception of COVID-19 vaccines using survey and Twitter data. For the survey study, we focused on examining the COVID-19 vaccine perspectives of the rural population in the Central Valley of California, which was predominantly Latinx. Specifically, we looked at the level of trust in the source and content of the vaccine information they received, their view of the safety and effectiveness of vaccines, and their accessibility to vaccines and information at the time when vaccines were readily available to the public. For the Twitter study, we focused on metropolitan and nonmetropolitan communities in the United States and examined tweet sentiment and emotion scores in the early stage of the pandemic and through the public release of the first COVID-19 vaccines. Methods: For the survey data, a total of 900 survey responses were collected in two rural counties in the State of California from March 30 to April 25, 2021. The survey was offered via web and phone in English, Spanish, Punjabi, and Hmong. The respondents were asked about their perceptions of COVID-19 vaccines, messaging, and sources of information. For the Twitter data, we used 127,648 tweets for the analysis after data cleaning, reverse geocoding of tweets, and assigning geographical designations to compare public perception between metropolitan and nonmetropolitan areas. We quantified public perception using the VADER (Valence Aware Dictionary for Sentiment Reasoning) lexicon to calculate sentiment scores and the NRCLex (National Research Council Canada Lexicon) to calculate emotions scores for the tweets. Next, we explored patterns in public perception of COVID-19 vaccines from March 11, 2020, to September 12, 2021. Then, we compared public perception between two separate periods (i.e., before and after December 11, 2020, when the Food and Drug Administration issued an emergency use authorization of Pfizer, the first COVID-19 vaccine). Results: In the survey approach, 41% of respondents were Latinx. The most frequent concerns noted for COVID-19 vaccine hesitancy were lack of confidence in the vaccine and the state and federal government (46-56%). However, complacency about the seriousness of the COVID-19 vaccines and disease (35%) and convenience or issues in access, travel time, and cost of vaccines (20%) were not associated with decisions regarding COVID-19 vaccination. In the Twitter approach, we found that public sentiment and emotion varied by geography though our findings did not significantly differ for metropolitan and nonmetropolitan residents. Fear was prevalent in the early times when COVID-19 was announced as a pandemic. However, this was quickly taken over by the emotion of trust later as the breakthroughs in COVID-19 vaccines were announced. Specifically, trust peaked on November 9, 2020, when Pfizer announced its vaccine was 90% effective. Then, around December 11, 2020, positive and negative tweet sentiments started diverging more clearly than the extreme sentiment fluctuations before this period.Conclusions: For urban or rural and metropolitan or nonmetropolitan communities, news and social media are potent outlets for health information and can significantly change the public’s perceptions about COVID-19 vaccines. The survey data shows rural residents in the Central Valley of California, predominantly Latinx, have high confidence or trust in healthcare providers, and the county public health department. However, approximately 40% of these rural residents were still unlikely to get vaccinated, similar to rural populations throughout the country. Recommendations to combat COVID-19 vaccine hesitancy amongst Latinx rural residents include leveraging trusted sources such as local doctors, family/friends, and local public health departments to encourage vaccination amongst this population. In addition, Twitter data shows that announcements from the public media or private institutions appear associated with the public’s perception of COVID-19 vaccines, such as the first news of the effectiveness of the Pfizer COVID-19 vaccine or the notice of blood clot issues caused by the Johnson & Johnson COVID-19 vaccine. Also, there was no significant difference in the mean sentiment or emotion scores between geographical distributions from March 2020 to September 2021. Overall, COVID-19 vaccine news appears to penetrate the public whether people are in urban or rural and metropolitan or nonmetropolitan communities.