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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.

This essay is comprised of three separate but interconnected sections, each working through at a different level the history of how Southern Illinois came to be called Egypt, and the implications of such a regional nicknaming. In the first, I consider the history of the moniker along with histories of the region through critical discussions of religion, race, and nineteenth century American Egyptomania. In the second, I retrace two cataclysmic events which occurred in Cairo, Illinois, and suggestively implicate by proximity Southern Illinois’ overidentification with Egypt. Finally, I recount and reconsider my own relation to the region and to its history and folklore, and describe my thesis exhibition, which has not yet been mounted due to COVID-19.

The fifth issue of INSAM Journal of Contemporary Music, Art and Technology is the second one we are preparing and publishing in the Covid-19 pandemic. And while the theme for the previous issue was conceived in a world unburdened with what has preoccupied our minds and lives in 2020, the theme for this one is directly shaped by it. During the Spring, when we were taken aback by the governmental measures and the fear of the “invisible enemy” (the use of militant vocabulary is rather prominent in the discourse surrounding the virus), the uncertainty for the future grew strong. However, at that time, we could not predict the longevity, brevity and consequences of the pandemic – in December we are still not certain, but we are getting tired. This is why I would like to thank all the authors for working with us in these trying times, unpacking what can only be a beginning of ‘a global crisis’ during the Summer and Autumn of 2020. The main theme of the issue, Music, Art, and Technology in the Time of Global Crisis, strives to capture this period through the lens of workers in art, music, and academia around the world, focusing on the role and place of arts and technology in our/their relocated institutional realities.

Fake news is not a novel concept but the scale of its spread and the damage it has and continues to cause is alarming. From the US presidential elections in 2016 to the COVID-19 pandemic and today, fake news has been circulating in news media corrupting the public opinion. Fake news alters democratic discussions polarizing people’s opinions sowing distrust in national institutions and setting different groups against each other. It is a threat to democracy and national security. It is crucial to prevent fake news from spreading and one solution is to create an automatic fake news detection system. A solution is researched using natural language processing (NLP) tasks, namely text classification. NLP is a type of artificial intelligence that is essentially taught to understand human language. Using thematic analysis, the main steps and techniques of fake news detection models are described and through a comparative analysis the state-of-the-art models are distinguished. And while there are many potential fake news detection models for English there is not much variety for other languages. So, it is additionally analysed if these benchmark models can be implemented for Finnish language as well. Valeuutiset eivät ole uusi käsite, mutta niiden leviämisen laajuus ja niiden aiheuttamat vahingot ovat huolestuttavia. Yhdysvaltain presidentinvaaleista vuonna 2016 COVID-19-pandemiaan ja nykypäivään asti, uutismediassa on kiertänyt valeuutisia, jotka muokkaavat yleisön mielipidettä. Valeuutiset muuttavat demokraattista keskustelua polarisoimalla ihmisten mielipiteitä kylväen epäluottamusta kansallisiin instituutioihin ja asettaen erilaisia ryhmiä toisiaan vastaan. Se on uhka demokratialle ja kansalliselle turvallisuudelle. On tärkeää estää valeuutisten leviäminen, ja yksi ratkaisu on luoda automaattinen valeuutisten havaitsemisjärjestelmä. Ratkaisua tutkitaan käyttämällä luonnollisen kielen käsittelyn (NLP) tehtäviä, etenkin tekstin luokittelua. NLP on tekoälyn tyyppi, missä tietokone opetetaan ymmärtämään ihmisten kieltä. Temaattisen analyysin avulla kuvataan valeuutisten havaitsemismallien päävaiheet sekä tekniikat, ja vertailevan analyysin avulla valikoidaan uusimmat ja onnistuneimmat mallit. Ja vaikka englannin kielellä on monia mahdollisia valeuutisten havaitsemismalleja, muille kielille ei ole paljon valikoimaa. Lisäksi analysoidaan, voidaanko nämä mallit toteuttaa myös suomen kielelle.

Black Americans presently and have historically faced disproportionately negative experiences in the U.S. healthcare system, as spotlighted by the COVID-19 pandemic. In my dissertation, I employ diverse methodologies, including quantitative analyses of nationally representative data, qualitative analyses of focus groups, and experimental methods aiming to understand and illuminate potential ways to address Black Americans’ experiences of injustice in healthcare. The introduction (Chapter 1) builds upon previous research to illustrate a model which emphasizes the importance of individuals and systems (and the histories of individuals and systems) to better understand racial injustice in healthcare. In Chapter 2, I provide a narrative review of the present and historical experiences of Black Americans in the healthcare system. Next, in Chapter 3, across two studies (N=13,054), including a nationally representative sample of Black and White Americans during the COVID-19 pandemic, Black (relative to White) Americans reported less positive experiences in healthcare, which explained early COVID-19 vaccination hesitancy and lower medical system trust. Current knowledge of the Tuskegee Syphilis Study was not related significantly to medical trust or vaccination intention, however. In Chapter 4, qualitative data and thematic analysis were used to interrogate the quality of healthcare provider-Black patient interactions in a sample of 37 Black American women who had been diagnosed with breast cancer. In a community-academic collaboration, three focus groups were conducted across California. Results demonstrated that participants experienced discrimination, stereotyping, and hostility from healthcare providers and within the healthcare system which undermined their medical trust. Further, participants offered suggestions for improving the healthcare experiences of Black women diagnosed with breast cancer. A critical step toward dismantling racial injustice is acknowledging its existence. Thus, in Chapter 5, I tested specific ways to shift dominant group members’ perceptions to recognize both individual and systemic racism and how to increase behavioral intentions to combat injustice in healthcare. Results from this online experiment conducted with 1853 adults suggested that when White Americans learned about critical Black history in healthcare (i.e., history of injustice) vs. celebratory Black history (i.e., history of achievement) or control information, they reported significantly more perspective-taking with Black Americans, which in turn predicted more individual and systemic racism recognition and support for anti-racist policies in healthcare. Ultimately, my dissertation studies highlight specific experiences of injustice that Black Americans face in healthcare and identifies a mechanism to increase White Americans’ recognition of and support for addressing injustices toward Black Americans.

Contributing metadata to the COVID-19 collection of the Ethnic and Migrant Minorities (EMM) Survey Registry as a data producer A training video targeting COVID-19 survey producers to entice contributions to the COVID-19 collection of the EMM Survey Registry Target Audience for the video: Survey producers (academic and non-academic) of COVID-19 surveys with EMM respondents

This text corresponds to a body of work that was developed at the University of California, San Diego with the intent to have a selection of objects exhibited together in the Main Gallery of the Visual Arts department. The exhibition was cancelled due to the Covid-19 pandemic. This text is thus independent of the unrealized exhibition and solely focuses on the work as a series of object-types that were conceived of at the University of California, San Diego. The body of work is divided into four series of object-types; Windows, Blocks, Portals, and Columns. The artworks presented utilize sculpture, installation, and photography and engage with the mold and the camera as base frameworks for how each object-type was produced. This text is divided into three sections: Objects, Frameworks, and Influences. In the Objects section, object-types are discussed in terms of their visual presentation, qualities, conditions, and orientations. This section directly focuses on each object-type. In the Frameworks section, the text focuses on philosophical and historical content that were conceptual considerations that helped to develop this body of work. Finally, in the Influences section, the text focuses on concepts, artworks, and sites that helped to influence the development of a between. This text does not justify or validate the work in any capacity, nor does it explain the work or how it may be experienced by an individual.

Information extraction (IE) plays a significant role in automating the knowledge acquisition process from unstructured or semi-structured textual sources. Named entity recognition and relation extraction are the major tasks of IE discussed in this thesis. Traditional IE systems rely on high-quality datasets of large scale to learn the semantic and structural relationship between the observations and labels while such datasets are rare especially in the area of low-resource language processing (e.g. figurative language processing and clinical narrative curation). This leads to the problems of inadequate supervision and model over-fitting. In this thesis, we work on the low-resource IE algorithms and applications. We believe incorporating the supervision from domain-specific auxiliary knowledge and learning transferable representations can mitigate the deficiency of low-resource IE. Specifically, we explore pre-training domain-specific deep language models to acquire informative word/sentence embeddings to curate clinical narratives. We experiment with multi-modal learning techniques to recognize humor and to recommend keywords for advertisement designers. We also extract attributes of interest from the semi-structured web data by building transferable knowledge representations across different websites. For more applications of the low-resource IE, we build a COVID-19 surveillance system by inspecting users' daily social media data. Extensive experiments prove that our algorithms and systems outperform the state-of-the-art approaches and are of impressive interpretability as well.