A COVID -19 news coverage mood map of E urope (2021)

Abstract: We present a COVID-19 news dashboard which visualizes sentiment in pandemic news coverage in different languages across Europe. The dashboard shows analyses for positive/neutral/negative sentiment and moral sentiment for news articles across countries and languages. First we extract news articles from news-crawl. Then we use a pre-trained multilingual BERT model for sentiment analysis of news article headlines and a dictionary and word vectors -based method for moral sentiment analysis of news articles. The resulting dashboard gives a unified overview of news events on COVID-19 news overall sentiment, and the region and language of publication from the period starting from the beginning of January 2020 to the end of January 2021.

## introduction the response to the covid-19 pandemic and its news coverage worldwide has been marked by tension between state-level actions, and those of regional organisations such as the european union, and the essentially borderless nature of the virus itself. this paper presents the covid-19 news coverage mood map of europe which visualizes sentiment in news coverage in different european languages. within the european context, with its many small languages, this multi-lingual approach is vital for working beyond the state-level. in order to evaluate the possibilities of automatic sentiment analysis models in this context, we first created a multilingual covid-19 news corpus from the websites of state broadcasters. we then applied two types of automatic sentiment analysis to the articles. finally, we created a dashboard containing a number of interactive plots based on the analysed corpus 1 . this report details work undertaken at the embeddia hackashop. ## corpus extraction we used the news-please extractor @xcite on news-crawl dumps to obtain a multilingual corpus of european covid-19 news. newscrawl is a web crawl provided by the common crawl organisation which is updated more frequently and contains only data from news websites foot_0 . in order to keep the size of the corpus manageable and the extraction time reasonable, a list of internet domain names of european state broadcasters was first obtained from wikidata, since filtering at the domain level allows for faster processing of common crawl dumps. articles without a language detected by the langdetect language detector foot_1 were discarded. covid-19 keyword filtering was also applied, detailed in section 3.1. the resulting corpus contains 468 thousand articles. it is thus just over a quarter of the size of the comparable aylien coronavirus news dataset foot_2 corpus which has 1 673 thousand news articles. our corpus contains news from a longer period of just over a year versus aylien's, which contains just over a half a year. most importantly, our corpus has at least 20 items in 22 languages, versus aylien's corpus which is english only. the full corpus does not include news from all european countries. ## analyses all analyses were multilingual, and their coverage of different languages is compared in ## visualisation in order to visualise the results of the analyses, we created a dashboard using the dash framework foot_7 . the resulting application makes heavy use of analytical queries which tend to feature range selections and grouping based on dates as well as numerical aggregates such as value summations and counts. to run these queries efficiently across the whole data set of 468 thousand articles, we used the duckdb column database @xcite . the chief dimensions visualised as independent grouping variables in space were date and either the country of production or the country mentioned in articles. for plots in which these variables could not be shown spatially, the user was given the option of filtering using them. the language used in the articles and type of sentiment were also available as filters. the main dependent variables were are either raw article counts or lumped measures. for visualising only time as a visual grouping, a bar chart was used, while for visualising only country, a choropleth map, an example of which is given in figure 1 . finally, an animated choropleth map showing consecutive time slices corresponding to weeks taken monday to sunday groups by both time and country. the lumped measure used for polar sentiment is a simple ratio, with the neutral class included to pull the measure towards zero: positive + 1 2 neutral positive + neutral + negative for moral sentiments, we have already obtained a measure for each document and each moral sentiment. these are simply summed to create a single aggregate bipolar measure of sentiment strength per moral sentiment (see figure 2 for an example). the sentiment estimate is rather noisy, so looking at the absolute values is not recommended. a better way to look at these numbers, is to look at them in relation to other countries or time spans. this tells how different countries differ in the way they represent this information and how is the overall trend progressing over time. ## conclusion we have presented a covid-19 news dashboard for exploration of reporting across time and from and about different countries during the covid-19 epidemic. the dashboard demonstrates the potential of automated multilingual text analysis for understanding reporting on complex phenomena such as the covid-19 crisis beyond the state-level. this type of tool could be integrated into a system used by news agencies to track news trends. beyond covid-19, it could be used to plan coverage of other national or global news events such as elections, international summits, or sports events. the visualizations in the dashboard do seem to line up with the authors' preconceived ideas about sentiments during covid-19 and their evolution. however, all analyses in the dashboard were produced automatically and have not undergone evaluation within this context. since the analyses themselves may not be entirely accurate, the resulting plots may be misleading, and thus should not be used as a basis for decision making. evaluation of the underlying techniques is a clear next step for this work.