User Experience Design for Automatic Credibility Assessment of News Content About COVID-19

The increasingly rapid spread of information about COVID-19 on the web calls for automatic measures of credibility assessment [18]. If large parts of the population are expected to act responsibly during a pandemic, they need information that can be trusted [20]. In that context, we model the credibility of texts using 25 linguistic phenomena, such as spelling, sentiment and lexical diversity. We integrate these measures in a graphical interface and present two empirical studies to evaluate its usability for credibility assessment on COVID-19 news. Raw data for the studies, including all questions and responses, has been made available to the public using an open license: https://github.com/konstantinschulz/credible-covid-ux. The user interface prominently features three sub-scores and an aggregation for a quick overview. Besides, metadata about the concept, authorship and infrastructure of the underlying algorithm is provided explicitly. Our working definition of credibility is operationalized through the terms of trustworthiness, understandability, transparency, and relevance. Each of them builds on well-established scientific notions [41, 65, 68] and is explained orally or through Likert scales. In a moderated qualitative interview with six participants, we introduce information transparency for news about COVID-19 as the general goal of a prototypical platform, accessible through an interface in the form of a wireframe [43]. The participants' answers are transcribed in excerpts. Then, we triangulate inductive and deductive coding methods [19] to analyze their content. As a result, we identify rating scale, sub-criteria and algorithm authorship as important predictors of the usability. In a subsequent quantitative online survey, we present a questionnaire with wireframes to 50 crowdworkers. The question formats include Likert scales, multiple choice and open-ended types. This way, we aim to strike a balance between the known strengths and weaknesses of open vs. closed questions [11]. The answers reveal a conflict between transparency and conciseness in the interface design: Users tend to ask for more information, but do not necessarily make explicit use of it when given. This discrepancy is influenced by capacity constraints of the human working memory [38]. Moreover, a perceived hierarchy of metadata becomes apparent: the authorship of a news text is more important than the authorship of the algorithm used to assess its credibility. From the first to the second study, we notice an improved usability of the aggregated credibility score's scale. That change is due to the conceptual introduction before seeing the actual interface, as well as the simplified binary indicators with direct visual support. Sub-scores need to be handled similarly if they are supposed to contribute meaningfully to the overall credibility assessment. By integrating detailed information about the employed algorithm, we are able to dissipate the users' doubts about its anonymity and possible hidden agendas. However, the overall transparency can only be increased if other more important factors, like the source of the news article, are provided as well. Knowledge about this interaction enables software designers to build useful prototypes with a strong focus on the most important elements of credibility: source of text and algorithm, as well as distribution and composition of algorithm. All in all, the understandability of our interface was rated as acceptable (78% of responses being neutral or positive), while transparency (70%) and relevance (72%) still lag behind. This discrepancy is closely related to the missing article metadata and more meaningful visually supported explanations of credibility sub-scores. The insights from our studies lead to a better understanding of the amount, sequence and relation of information that needs to be provided in interfaces for credibility assessment. In particular, our integration of software metadata contributes to the more holistic notion of credibility [47, 72] that has become popular in recent years Besides, it paves the way for a more thoroughly informed interaction between humans and machine-generated assessments, anticipating the users' doubts and concerns [39] in early stages of the software design process [37]. Finally, we make suggestions for future research, such as proactively documenting credibility-related metadata for Natural Language Processing and Language Technology services and establishing an explicit hierarchical taxonomy of usability predictors for automatic credibility assessment. © 2022, Springer Nature Switzerland AG.

Intrinsic asthma after SARS-CoV-19-infection

We report the case of a 40-year-old previously lung-healthy male who became infected with SARS-CoV-2 from a named index person while working in charity care. He developed intrinsic bronchial asthma during its course. Discussed are bronchial hyperreactivity following viral infection, occupational accident and occupational disease (number 3101 – German register of occupational diseases). ©2022 Dustri-Verlag Dr. K. Feistle.

Intrinsisches Asthma nach COVID-19-Infektion

Wir berichten über einen 40-jährigen bisher lungengesunden Versicherten, der sich bei seiner Arbeit in der Wohlfahrtspflege bei einer namentlich benannten Index­person mit SARS-CoV-2 infizierte und im Verlauf ein intrinsisches Asthma bronchiale entwickelte. Diskutiert werden eine passagere bronchiale Hyperreaktivität nach Virusinfektion, ein Arbeitsunfall und eine Berufserkrankung nach Listennummer 3101.

Suspicious Sentence Detection and Claim Verification in the COVID-19 Domain

The processing, identification and fact checking of online information has received a lot of attention recently. One of the challenges is that scandalous or "blown up"news tend to become viral, even when coming from unreliable sources. Particularly during a global pandemic, it is crucial to find efficient ways of determining the credibility of information. Fact-checking initiatives such as Snopes, FactCheck.org etc., perform manual claim validation but they are unable to cover all suspicious claims that can be found online - they focus mainly on the ones that have gone viral. Similarly, for the general user it is also impossible to fact-check every single statement on a specific topic. While a lot of research has been carried out in both claim verification and fact-check-worthiness, little work has been done so far on the detection and extraction of dubious claims, combined with fact-checking them using external knowledge bases, especially in the COVID-19 domain. Our approach involves a two-step claim verification procedure consisting of a fake news detection task in the form of binary sequence classification and fact-checking using the Google Fact Check Tools. We primarily work on medium-sized documents in the English language. Our prototype is able to recognize, on a higher level, the nature of fake news, even hidden in a text that seems credible at first glance. This way we can alert the reader that a document contains suspicious statements, even if no already validated similar claims exist. For more popular claims, however, multiple results are found and displayed. We manage to achieve an F1 score of 98.03% and an accuracy of 98.1% in the binary fake news detection task using a fine-tuned DistilBERT model. © 2022 Copyright for this paper by its authors.

Social determinates of health and COVID-19 mortality rates at the county level

Understanding how underlying health conditions and social determinants of health affect the severity of COVID-19 is critical for community response planning. Literature reports that groups at higher risk from COVID-19 include those 65 and older, living in nursing homes and long-Term care facilities, and with severe obesity, diabetes, chronic lung disease, or asthma. In addition, other studies has shown that the disease disproportionately affects individuals with lower socio-economic status. Our research seeks to validate these findings and observe the effects of health measures and social determinants of health on COVID-19 mortality at the county-level. In addition to COVID-19 research from hospital population samples, public health officials can leverage county-level factors for novel disease mitigation. We use the Johns Hopkins University COVID-19 reports of confirmed cases and deaths to measure disease mortality for each county in the United States. Then, we compare mortality to multiple county social determinants of health such as age, obesity, diabetes, and smoking in hypothesis testing. We fit multivariate linear models as well as non-linear models to predict mortality as a function of these county measures. The analysis shows that there is little evidence of a relationship between the county health measures of obesity, diabetes, or smoking and COVID-19 mortality as of the date of this publication. However, the analysis does reveal a positive relationship between the percent of a county population that is 65 or older and COVID-19 mortality. Other factors such as overcrowding, the percent uninsured, and the length of time since the virus has been detected in the county are also correlated with county COVID-19 mortality. Potential reasons for these findings, including data quality, are discussed. We also emphasize the advantage of collecting high quality, detailed health data at the county-level and explain how such data could be used to understand factors affecting the outcomes from novel diseases in real-Time, as a disease is progressing. © 2020 IEEE.