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Proceedings of the Text2Story’22 Workshop, Stavanger (Norway), 10-April-2022

[orcid=0000-0003-3228-1129<EMAIL_ADDRESS>]

[orcid=0000-0002-0065-6478<EMAIL_ADDRESS>]

[orcid=0000-0002-8417-3288<EMAIL_ADDRESS>]

[orcid=0000-0003-2782-5478<EMAIL_ADDRESS>]

# Understanding COVID-19 News Coverage using Medical NLP

Ali Emre Varol  Veysel Kocaman  Hasham Ul Haq  David Talby  John Snow Labs
inc. 16192 Coastal Highway, Lewes, DE 19958, USA

(2021)

###### Abstract

Being a global pandemic, the COVID-19 outbreak received global media
attention. In this study, we analyze news publications from CNN and The
Guardian - two of the world’s most influential media organizations. The
dataset includes more than 36,000 articles, analyzed using the clinical and
biomedical Natural Language Processing (NLP) models from the Spark NLP for
Healthcare library, which enables a deeper analysis of medical concepts than
previously achieved. The analysis covers key entities and phrases, observed
biases, and change over time in news coverage by correlating mined medical
symptoms, procedures, drugs, and guidance with commonly mentioned demographic
and occupational groups. Another analysis is of extracted Adverse Drug Events
about drug and vaccine manufacturers, which when reported by major news
outlets has an impact on vaccine hesitancy.

###### keywords:

NLP COVID-19 Spark NLP Natural Language Processing Vaccine Adverse Drug
Effects.

##  1 Introduction

The novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) -
commonly known as COVID-19 - was first reported in December 2019. Due to its
highly contagious nature, it quickly spread through the world, prompting the
World Health Organization to declare the virus outbreak as a global pandemic
on March 11, 2020 [1]. As news media reporting is understood to play a central
role during national security and health emergencies [2], during the pandemic,
media representations of complex, rapidly evolving epidemiological science
shape public understandings of the risks, measures to limit disease spread,
and associated political and policy discourses [3].

In this study we analyse news coverage from two prominent media outlets: CNN
and The Guardian. The dataset includes more than 36,000 articles and is
analyzed using the clinical and biomedical NLP models from the Spark NLP for
Healthcare library [4]. Spark NLP is an open-source and widely deployed
software library, built on top of Apache Spark, that provides production-grade
implementations of recent deep learning and transfer learning NLP algorithms
and models. It enables combining tasks into unified NLP pipelines in Python,
R, Java, or Scala and is the only library that can scale up training and
inference on any Spark cluster. Spark NLP for Healthcare provides healthcare-
specific algorithms and over 400 pre-trained models that have obtained state-
of-the-art accuracy on public academic benchmarks in biomedical named entity
recognition [5], clinical assertion status detection [6], medical relation
extraction [7], and adverse event detection [8].

COVID-19 has been studied extensively - including the impact of its news
coverage. In previous studies, [9] and [10] used topic modelling and sentiment
analysis, while [11] analyzed discourse structures to study bias in media
reporting to influence government policies and global reporting. Cresswell et
al. [12] used social media data to analyse public sentiment towards the
pandemic and determined media reporting played a major role in public
sentiment. This study is unique in applying healthcare-specific deep learning
networks, models, and embeddings - enabling the extraction and correlation of
over 100 different types of medical entities with high accuracy. Following are
the contributions of this study:

  * •

Using fine-tuned NLP models to find most prevalent covid symptoms, prevention
guidelines, research institutions, covid variants, and other entities for
analysis.

  * •

Geographical and demographic analysis of news coverage to understand most
affected countries, population age groups, and professions.

  * •

Putting the entire covid coverage of 2020 & 2021 on a timeline for each
country, to understand temporal variation and correlation of case count and
media coverage.

  * •

Comparing the findings from unstructured text with the statistical data
reported by WHO and analysing coherency.

  * •

Correlating adverse reactions and drug brands by extracting and linking drug
and reaction entities.

##  2 Analysis

The dataset comprises of 36,354 live blogs and key moments about COVID-19,
published in 2020 and 2021. A live blog is a web page where news media outlets
offer daily live coverage about an ongoing event. Each live blog consists of
news stories and key moments. Journalists manually select the key moment
stories from the whole set of news articles. We excluded the key moments from
live blogs; hence, we can appropriately compare live blogs with key moments;
otherwise, there will be some overlapping parts. To scrape the news articles,
we leveraged web scraping algorithms by [13], and updated according to our
use-case. Updated code and Spark NLP pipelines can be accessed [14]. As stated
in [13], first, we collected the live blogs and then applied some pre-
processing steps such as parsing HTML format, converting the dates, and
extracting key moments from live blogs. The data is organized by title, text,
date, URL, and key moment status fields. After data preparation, the executed
NLP pipeline included sentence segmentation, tokenization, calculating
embeddings, multiple named entity recognition steps, and relation extraction.

In the following sections, we’ll report the analysis outcomes that we run over
the news coverage by age demographics, news cycle evolution over time and the
reporting and impact of adverse drug & vaccine events.

###  2.1 General News Analysis

Figure 1: Monthly Distribution of the News from Jan’20 - Dec’21.

We tracked the live blogs of CNN from 22nd January 2020 and The Guardian from
24th January 2020; until the end of 2021. The distribution of news by sites
and years is explained in Figure 1. Upon inspection, it is apparent that the
number of news was high in the periods when COVID-19 first appeared and the
first deaths had been reported in and outside Asia in early 2020. News of CNN
reached its peak point in March 2020 when COVID-19 reached the shores of
United States. Other factors of high news coverage included the massive spread
in Europe (especially Italy) and promulgation of the virus as a pandemic by
the WHO. Conspicuously, CNN COVID-19 reporting gradually decreased after their
peak point and became very seldom in late 2021. With all these, as an overall
evaluation, a monthly average of COVID-19 live blogs and key moments is 807,
and the median is 365.

If we look at the types of news, it primarily consists of live blogs while the
key moments constitute for a minority portion. When we combine them for
analysis, we get more than 5.3M words in the corpus to create a story.

###  2.2 Demographics and Geographical Analysis by Named Entities

Figure 2: Total number of Cases, Deaths, and News (Scaled by Periods)

In order to analyse the news coverage by certain geographical entities, we
used a pre-trained named entity recognition (NER) model called
ner_deid111https://nlp.johnsnowlabs.com/2021/09/03/ner_deid_subentity_augmented_en.html
from Spark NLP for Healthcare library to extract country names mentioned in
the news. Using WHO’s official daily statistics [15], we gathered the number
of actual COVID-19 cases and deaths for every half of 2020 and 2021, and then
compared with the top 12 countries extracted from the news by the ner_deid
model.

Figure 2 shows the normalized metric for each country by dividing its value by
the maximum value in the respective six-month period. As it is seen on the
chart, the most reported cases are seen in the USA, India, and Brazil,
respectively. It is also seen that the number of patients who died is also
among these three countries, although Brazil ranks second on the death list.

The graph can be interpreted in the following way: (1) If the lengths of the
bars are in agreement with each other, it can be deduced that the cases
reported and deaths reported are in agreement with the number of news
broadcasts in CNN and the Guardian. (2) If the blue and red bars are
compatible with each other but not with the green bar, there may be two
reasons for a mismatch with the news number. The first reason could be that
the country is far from the news source while the second reason could be that
the total number of cases and deaths in the country are not considered
remarkable by the news sources. (3) If the all the bars are inconsistent with
each other, it may indicate that the country in question has not published the
case and death numbers transparently. (4) Needless to say, the numbers
regarding the countries having inconsistent figures (e.g. Brazil, Russia etc.)
might also be explained by the fact that the dataset only covers the news
articles from the US (CNN) and UK (Guardian), hence these countries are
reported through the lens of other countries (US and UK editors).

Consequently, we can count United States, United Kingdom, France, Germany and
Australia as the most compatible countries while China and Brazil can be
counted as the most incompatible countries. For Spain, the total number of
cases and deaths is consistent, but the volume of news articles is
inconsistent. This may be explained by Spain being away from the two news
sources.

Figure 3: Top 40 Entities in Corpus

###  2.3 Behavioral and Clinical Analysis by Named Entities

In order to analyse the news with respect to the clinical entities mentioned,
we used a pretrained NER model named
ner_events_clinical222https://nlp.johnsnowlabs.com/2021/03/31/ner_events_clinical_en.html
and ner_jsl333https://nlp.johnsnowlabs.com/2020/04/22/ner_jsl_en.html to
extract major clinical events. These two NER models combined can extract more
than one hundred different types of clinical entities at once, with one line
of code, enabling a deeper analysis than previous studies of this type.

Figure 3 shows the top 40 entities by frequencies in the corpus. Entities
related to coronavirus (deaths, infections, preventive measures, and
geographical locations etc) seem to be the most prevalent, followed by
entities related to research and development of drugs. When we look at the
ranking of countries in this list, the United States holds first place with
the most reported number of cases, while the United Kingdom and Italy are in
the second and third places - aligning with the WHO stats illustrated in
Figure 2. The only university listed is John Hopkins University (JHU),
primarily because of its role in organization and coordination of the
Coronavirus Resource Center [16]. We also see major vaccine manufacturers in
the list due to high coverage of vaccine development and trials.

Figure 4 shows the Top 10 treatments, symptoms, drugs, and procedures in the
order of their frequencies. It is clear that concepts like vaccine, wearing
masks, oxygen support, and ventilators appear often in treatments. These
entities along with isolation, quarantine, social distancing, and self-
isolation, which are at different ranks in the list, have a significant place
in disease control.

On the other hand, the symptoms presented in Figure 4 are widespread. People
who observe any of these symptoms turn to hospitals or health institutions to
find out if they have COVID-19. Coughing, blood clotting, sneezing, fatigue,
shortness of breath, and headache are the prevailing COVID-19 symptoms. It is
worth noting that even a headache alone was enough to arouse the suspicion of
coronavirus during this period. However, very specific symptoms related to
loss of smell and taste are also observed, although their frequency is lower.

Figure 4: Top 10 Treatments, Symptoms, Drugs, and Procedures

When analyzing named entities related to drugs, the companies producing
vaccines stand out. Note that drug names also include drug brand names, drug
manufacturers, and drug ingredients. The data suggests that the manufacturer’s
name is more prominent compared to the drug’s scientific name in news
coverage. Hydroxychloroquine and chloroquine were granted an emergency use
authorization (EUA) on March 28, 2020 for treating COVID-19 cases. However, on
June 15, 2020, FDA revoked its use [17], limiting it only to hospitalized
patients under heart monitoring.

The most commonly mentioned medical procedures feature intubation and
inoculation in the top two places. Intubation has been the most frequently
applied procedure for patients admitted to the intensive care unit during this
period.

Figure 5: Age groups mentioned in news

The pie chart in Figure 5 shows that the most mentioned age group is 60 years
and above, followed by adolescents. This is probably due to the fact that
COVID-19 has higher severity in older people. According to scientific
observations, WHO declared that COVID-19 is often more severe in people who
are older than 60 years or who have health conditions like lung or heart
disease, diabetes, or conditions that affect their immune system [18].

Comparing the most prevalent entities in 2020 versus 2021 - the coronavirus,
which was in the first place in 2020, fell to second place in 2021, ceding the
first place to the COVID-19 vaccine. The word ’reported’ fell from the second
most encountered entity in 2020 to the fourth in 2021. This decline can be
attributed to less willingness to reporting of COVID-19 cases since according
the [19] the trend of reported cases is not declining. On the contrary, there
is an increase in both the number of new cases and the number of deaths.

In addition to healthcare-specific named entity recognition and resolution
models, Spark NLP also provides unsupervised key phrase extraction from free
text. Results indicate that the entity counts follow the general trend where
COVID-19 infection is the most prevalent term, followed by entities related to
death, geographical locations, media outlets, vaccine development, and
vaccination reports.

COVID-19 also highlighted specific professions namely the medical doctors and
nurses. Our NER analysis show that researchers, workers, teachers and
government officials are the most cited professions in the news.
Understandably, the most commonly mentioned people in the news during this
period are healthcare professionals.

###  2.4 Impact of Adverse Drug and Vaccine Events

Adverse drug reactions/events (ADR/ADE) have a major impact on patient
outcomes and healthcare costs. An analysis of extracted Adverse Drug Events
versus drug and vaccine manufacturers reported by major news outlets has an
impact on vaccine hesitancy as well as people’s reactions to medications and
public health measurements dictated by regulatory agencies. In order to
analyse ADEs as well the adverse events of vaccines mentioned in the news, we
used a pre-trained NLP pipeline named
explain_clinical_doc_ade444https://nlp.johnsnowlabs.com/2021/07/15/explain_clinical_doc_ade_en.html
that comes with the Spark NLP for Healthcare library. This NLP pipeline is the
first scalable end-to-end solution for mining ADE’s from unstructured text,
including Document Classification, Named Entity Recognition, and Relation
Extraction Models within a unified NLP pipeline [8].

Figure 7 is a heatmap of correlation between vaccines and adverse reactions.
When we explore the news dataset, we see that some of the vaccines are listed
as their manufacturer’s name, but some are only with generic titles such as
COVID-19 vaccine or mRNA vaccines. Since the heavy use of such general
entities dilutes the results, Figure 7 shows the correlation while eliminating
general vaccine names.

One of the most prominent observation the entanglement of clotting with
AstraZeneca and Johnson & Johnson. This phenomenon was widely reported by the
media, suspending the use of the drugs by these companies [20]. General
allergic reactions are more common for vaccines from Pfizer & BioNTech and
Moderna [21].

Figure 6: ADE of Vaccines

Figure 7: ADE of Vaccines without Generic Titles

##  3 Conclusion

As a global pandemic, COVID-19 has caused millions of deaths and impacted most
people on earth. The media has played a major role in shaping public
awareness, education, and opinion. Due to the growing number of media
channels, the constant barrage of news, the prevalence of social media in
sharing and shaping the news, and frequent shifts in regulations dictated by
governments to combat the pandemic, analysing such a sheer volume of
information objectively in a short amount of time can be one of the most
practical current applications of NLP. In this study, we analyse news coverage
from two prominent media outlets by using the clinical and biomedical NLP
models of Spark NLP for Healthcare.

The analysis shows that the total number of cases and deaths reported by the
news are mostly consistent with the numbers shared by WHO officially. The pre-
trained NLP models perform well on extracting the most relevant terms and
entities that have been widely used in the news. One tangible outcome of this
study can be the automated mining of the adverse reactions to drugs and
vaccines that are used to combat the virus. The analysis shows the
correlations between the prominent vaccine manufacturers and reported adverse
events just by relying on the news articles used for this study. Future
research can broaden this automated analysis to include additional news
sources, news in languages other than English, dealing with fake news, or to
correlating changes in news coverage to resulting changes in public opinion
and behavior.

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