A System for Worldwide COVID -19 Information Aggregation (2020)

Abstract: The global pandemic of COVID-19 has made the public pay close attention to related news, covering various domains, such as sanitation, treatment, and effects on education. Meanwhile, the COVID-19 condition is very different among the countries (e.g., policies and development of the epidemic), and thus citizens would be interested in news in foreign countries. We build a system for worldwide COVID-19 information aggregation containing reliable articles from 10 regions in 7 languages sorted by topics. Our reliable COVID-19 related website dataset collected through crowdsourcing ensures the quality of the articles. A neural machine translation module translates articles in other languages into Japanese and English. A BERT-based topic-classifier trained on our article-topic pair dataset helps users find their interested information efficiently by putting articles into different categories.

## introduction due to the global covid-19 epidemic and the rapid changes in the epidemic, citizens are highly interested in learning about the latest news, which covers various domains, including directly related news such as treatment and sanitation policies and also side effects on education, economy, and so on. meanwhile, citizens would pay extra attention to global related news now, not only because the planet has been brought together by the pandemic, but also because they can learn from the news of other countries to obtain first-hand news. for example, the epidemic outbreak in korea is one month earlier than in japan. japanese citizens the authors are in alphabetical order. 1 https://lotus.kuee.kyoto-u.ac.jp/ nlpforcovid-19/en could prepare better for the epidemic if they had obtained more information from korea. citizens could learn from asian countries about the effectiveness of masks before local official guidance. universities can learn about how to arrange virtual courses from the experience of other countries. thus, a citizen-friendly international news system with topic detection would be helpful. there are three challenges for building such a system compared with systems focusing on one language and one topic @xcite ): the interface and the construction process of the worldwide covid-19 information aggregation system are shown in figure 1 . we first construct a robust multilingual reliable website collection solver via crowdsourcing with native workers for collecting reliable websites. we crawl news articles base on them and filter out the irrelevant. a high-quality machine translation system is then exploited to translate the articles into the local language (i.e., japanese and english). the translated news are grouped into their corresponding topics by a bert-based topic classifier. our classifier achieves 0.84 f-score when classifying whether an article is about covid-19 and substantially outperforms a keyword-based model by a large margin. in the end, all the translated and topic labeled news is demonstrated via a user-friendly web interface. ## results we show the statistics of the reliable website collection , topic classification results, translation quality evaluation and statistical information of the interface in this section. ## conclusion we built a system for worldwide covid-19 information aggregation by combining crowdsourcing, crawling, machine translation, and a topic classifier, which provides reliable, comprehensive and latest information from the world. in the meanwile, we proposed an effective approach to annotate large cross-lingual news topic datasets with high inner-annotation agreement, which potentially benefits the nlp community to enrich the solutions for preventing covid-19. the contextual bert based classification models achieve reasonable performance considering the imbalance of the topic labels. we assume this work could attract future research interests to the covid-19 related tasks.