# CovidTracker: A comprehensive Covid-related social media dataset for NLP
tasks

Richard Plant, Amir Hussain

###### Abstract

The Covid-19 pandemic presented an unprecedented global public health
emergency, and concomitantly an unparalleled opportunity to investigate public
responses to adverse social conditions. The widespread ability to post
messages to social media platforms provided an invaluable outlet for such an
outpouring of public sentiment, including not only expressions of social
solidarity, but also the spread of misinformation and misconceptions around
the effect and potential risks of the pandemic. This archive of message
content therefore represents a key resource in understanding public responses
to health crises, analysis of which could help to inform public policy
interventions to better respond to similar events in future. We present a
benchmark database of public social media postings from the United Kingdom
related to the Covid-19 pandemic for academic research purposes, along with
some initial analysis, including a taxonomy of key themes organised by
keyword. This release supports the findings of a research study funded by the
Scottish Government Chief Scientists’ Office that aims to investigate social
sentiment in order to understand the response to public health measures
implemented during the pandemic.

###### Index Terms:

COVID-19, Dataset, NLP, Social networking (online), Twitter, Facebook

##  I Introduction

The SARS-Cov-2 novel coronavirus popularly known as Covid-19 has provoked a
worldwide response since its designation as a worldwide pandemic by the World
Health Organisation in March 2020. As of November 2020, almost 45 million
cases had been confirmed, with more than 1.1 million deaths reported [1].
While the majority of cases involved mild symptoms including cough, fever,
diarrhoea and pain, some affected individuals developed much more acute
symptoms, such as pneumonia or kidney failure. Especially vulnerable groups
including elderly patients and those suffering multiple co-morbidities proved
more susceptible to severe infection, and thus presented a correspondingly
higher risk of hospitalisation [2].

The virus has been transmitted between humans primarily through respiratory
action, with recent studies predicting the rate of transmission between non-
immune carriers at 3.77% [3]. Public health action concentrated on reducing
the spread of the virus through travel travel restrictions, temporary closure
of businesses and schools, social distancing guidelines, and the
implementation of test-and-trace systems. While there were eventually largely
successful efforts to create and distribute vaccine treatments [4, 5, 6, 7],
initial responses to the pandemic involved a great deal of prophylactic
measures, including widespread filter mask wearing and surface disinfection.

Much current research has responded to the pandemic by urgently striving to
understand and communicate aspects of the social impacts to the public as well
as combating pervasive myths [8, 9], a key role for researchers especially
when the public is faced with urgent choices demanding good access to
information and the tools to understand potential trade-offs between public
health, security, and other social goals [10, 11].

Public health measures designed to restrict the spread of the virus like
social distancing—reducing physical contact between people [12, 13]—however
led to negative social outcomes, such as the closure of many workplaces [14],
restrictions on personal mobility [15], and the closure of schools and
universities [16, 17]. These protective measures themselves had profound
effects on many aspects of society, including economic [18, 19], social and
psychological [20, 21, 22], and indeed in wider health terms [23].

Under these circumstances, a great deal of public discourse has moved to
online social networks [24, 25], presenting an excellent opportunity to gauge
the mood and responses of users during a period of exceptional pressure and
stress. While this has enabled a large number of people to share their
experiences and potentially engage positively with additional sources of
social solidarity, we note also the potential for malicious or misinformed
commentary, with the additional concerns of increased spread of conspiracy
theory, hate speech, and trolling [26].

We present a benchmark dataset drawn from online conversations in the United
Kingdom about Covid-19-related topics on social networks, collected during
various stages of the pandemic response, between January 2020 and April 2021.
It is our hope that open access to this data will spur further research into
better informing and understanding public sentiment around the pandemic, as
well as helping public health professionals to make informed decisions when
implementing.

Our key contributions include:

  * •

We make available a large-scale message dataset drawn from both the Twitter
and Facebook social networks during the Covid pandemic, consisting of more
than 9.4 million individual posts. This set is designed for use in natural
language processing applications, and is available through the Edinburgh
Napier University research repository111https://www.napier.ac.uk/research-and-
innovation/research-search/outputs/covid-19-uk-social-media-dataset-for-
public-health-research.

  * •

In consultation with an expert panel of public health policy analysts and
physicians we developed a topic taxonomy to efficiently determine the broad
theme of the textual message content, which we use to annotate each message
with a marker of discussion subject using a keyword frequency analysis system.

  * •

We present an indicative set of statistics including the most prevalent
discussion topics, as well as some key demographics of the user base. This
information could prove useful to public health officials when determining
public policy responses to future adverse social and health events.

##  II Related Work

A number of datasets for natural language processing use extracted from social
media activity around the Covid-19 pandemic have previously been published.
Among those targeting a much wider geographic lens are Chen et al. [27], which
adopts 22 keywords to filter worldwide Twitter messages via the streaming API,
resulting in a large multi-lingual unlabelled dataset. Lamsal [28] used a
similar approach, filtering with a list of 90 keywords and hashtags, as well
as annotating the resultant dataset with sentiment markers. Dimitrov et al.
[29] extended this approach by adopting a 268 keyword list through which they
filtered the existing large-scale TweetsKB dataset [30]. Two sets that we
found particularly of note that were not produced from Twitter are the Weibo-
COV dataset [31], extracted from the social network Weibo, and the Instagram
dataset compiled by Zarei et al.[32].

In addition to this, multiple smaller datasets have been produced from Twitter
encompassing linked niche topics within the broader ambit of Covid-19,
including the effects of Hydroxychloroquine [33], and various sources of
treatment misinformation [34]. The diffusion of fake news through social media
sources in multiple languages was the focus of several dataset releases [35,
36, 37, 38].

Since the widespread easing of lockdown measures, we note that a number of new
or updated datasets have been published by researchers also aiming for our
goal of retrospective analysis. Many of these publications are designed for
sentiment analysis or topic modelling purposes, including the COVID-Senti [39]
and Waheeb et al. [40] datasets. This group also includes some interesting
information not previously widely studied, including Cheng et al.’s Government
Response Event Dataset [41], a collection of public policy announcements by
official bodies which includes a manual coding structure for type, scope, and
targeted population or group. Similar data is presented by Desvars-Larrive et
al. [42]. A comprehensive listing of the textual datasets available, as well
as those including other knowledge modalities, can be found in the review
conducted by Shuja et al. [43].

Studies that utilised key social media Covid-related datasets have tended
towards classical NLP applications, notably sentiment analysis [44], deployed
towards understanding topics such as public reception of contact tracing apps
[45, 46], lockdown procedures [47, 48], and the spread and propagation of both
accurate and inaccurate information [49, 50]. A novel approach that we found
of particular interest involved the differential comparison of sentiment
across national, cultural, and language boundaries [51, 52, 53].

We note the current scholarship around the ethics of releasing the data of
participants in social media research without direct consent, especially [54,
55, 56]. However, we conducted a thorough assessment of the privacy risk to
individuals posed by our research, and along with complying with social
network policies and the relevant sections of the General Data Protection
Regulation (GDPR), we believe our research is in the legitimate public
interest. We have not shared or published direct tweets or posts by
individuals, quotes from individuals or names or locations of users who are
not public organisations/entities. We have also striven to comply with best
practices for user protection [57, 58], as well as ensuring that no non-public
material is included in our dataset.

##  III Methodology

###  III-A Data collection

Our solution demanded that we implement a platform agnostic collection system
capable of integrating with multiple data sources. In order to achieve this,
our primary data store was determined to be a Google Cloud-hosted BigQuery
database, into which we would load all records. We therefore designed our
collection modules for individual platforms as independent parts of the
architecture, capable of being flexibly swapped in and out as required.

When working with Twitter data, we designed a containerised streaming listener
application 222https://github.com/enu-covid-dashboard/gcp-setup that could be
deployed quickly on any available host, which would connect to the network
endpoint, establish a stream connection with a number of possible pre-selected
filters, and consume and log incoming messages constantly. An indicative block
diagram of this application can be found in Figure 1. This application was
connected to the endpoint on the 23rd of June at 09:38 GMT, and so the set
contains messages from this point onwards.

Figure 1: Block diagram of Twitter collection pipeline
1\. Twitter streaming API 2. Containerised stream listener app
3\. Log management 4. Google Pub/Sub subscription
5\. App monitoring and alerting 6. Google Cloud Function
7\. BigQuery database

In this study, we harvested messages within our defined regional boundaries,
and defined our stream filter parameters to harvest all messages tagged with a
geographical location within the United Kingdom 333Exact bounding box
parameters [-6.034129, 49.875160, -0.344459, 61.340529]. The Twitter 1.1 API
specification in use at the time of development 444See
https://developer.twitter.com/en/docs/twitter-api/v1/tweets/filter-
realtime/api-reference/post-statuses-filter allowed the use of bounding box
location filters only, and only returns Tweets that have been tagged with
Place information derived from the ”fine-grained location” permission enabled
by users.

Building on previous work [59, 60], we determined the best solution in order
to build a low-latency message queuing and ingestion system was via Google
Cloud Pub/Sub, through which we sent the extracted post fields to a Cloud
Function written in Python that cleaned and sequenced the data into our
preferred format, before inserting it into a BigQuery database. See Table I
for details of the fields included in the imported data.

Platform | Field Name | Data Type
---|---|---
Twitter | Message ID | Integer
| Date Created | Datetime
| Message Text | String
| Location (nearest Place) | String
Facebook | Crowdtangle Post ID | String
| Created Date | Datetime
| Message Text | String
| Description Text | String
| Engagement Stats (likes, reacts, etc.) | Integer
TABLE I: Input data formats

We gained access to Facebook data through the Crowdtangle platform
555https://www.crowdtangle.com/, a Facebook-owned venture which allows access
to public post and group data through both visual dashboard tools and an API.
In order to harvest Covid-related material, we manually curated lists of
important groups, pages and profiles through the web interface. These lists
were sorted into both Scotland-only and UK-wide.

A scheduled script 666https://github.com/enu-covid-dashboard/cwd-ingest was
set up to connect to the API, retrieve all lists attached to the project
dashboard, retrieve all posts made by lists members during the preceding 24
hours, then directly upload the results to the BigQuery database.
Unfortunately, the lack of a streaming interface precludes near-real-time
updates, but this frequency of updates was judged to be acceptable by the
project team. Since Facebook posts were available for archive retrieval, we
collected posts beginning from the 1st of January 2020 at 00:01 GMT.

We extracted a total of 4,865,020 messages from Twitter between the 23rd of
July 2020 and the 12th of March 2021, and a total of 4,564,753 Facebook posts
between January 1st 2020 and April 27th 2021.

###  III-B Processing

Pre-processing for data was limited to the removal of line ending characters
(\n and \r), as well as annotation with a theme ID determined by keyword
frequency analysis, drawn from a pre-determined list of themes and keywords.
Any messages that could not have a theme label applied to them were tagged
with a valence marker that ensured they would not be entered for further
analysis, and have not been published in the released dataset.

ID | Theme | Tweets | Facebook posts
---|---|---|---
1 | Test & Protect | 30,591 | 40,044
2 | Shielding | 605,709 | 563,038
3 | Care homes | 14,445 | 38,758
4 | Covid survivors | 85,243 | 176,056
5 | Resumption of health services | 24,930 | 84,532
6 | Mental health & loneliness | 469,374 | 351,388
7 | Trust in Scottish Government | 40,531 | 24,513
8 | Routemap to exit lockdown | 810,092 | 455,360
9 | Impact on BAME population | 352,224 | 129,883
10 | Inequalities | 84,940 | 63,522
11 | Community cohesion/solidarity | 431,846 | 205,626
12 | Education | 116,807 | 104,203
13 | Environment | 26,868 | 35,855
14 | Quality of life | 36,126 | 74,171
15 | Social/Family | 417,390 | 522,833
16 | Leisure/Entertainment | 172,973 | 292,409
17 | Travel | 52,149 | 78,028
18 | Business restrictions | 14,276 | 25,317
19 | Work | 145,113 | 128,619
20 | Hygiene | 50,405 | 62,532
21 | Shopping | 84,920 | 311,620
22 | Unemployment | 652,862 | 443,499
23 | Business growth | 36,630 | 221,613
24 | Security | 86,303 | 83,642
25 | Other | 22,273 | 47,692
Total |  | 4,865,020 | 4,564,753
TABLE II: Theme names with associated ID numbers Figure 2: Proportion of each
theme in Twitter messages Figure 3: Proportion of each theme in Facebook posts

A set of priority COVID-19 themes and keywords was developed in close
consultation with public health policy domain experts, led by the Covidtracker
project co-Lead, Prof Sheikh at Edinburgh University (member of the Scottish
Government’s CMO COVID-19 Advisory Group), for potential integration into the
Scottish Government framework for decision making. The latter is a policy
document setting out the key focus of public health and decision-making bodies
when dealing with the pandemic. The COVID-19 themes were also updated in light
of public engagement exercises conducted by the government in order to derive
priority topics of greatest public interest [61]. A full list of the keywords
used to filter for each theme can be found in Table III.

ID |  Keyword/Topic/Idea
---|---
1 |  TTI, isolate, test trace, test protect, contact tracing, covid testing, self isolation, 14 day isolation, r number
2 |  vulnerable, shielding, high risk, elderly, disability, shielded getting out
3 |  care home, old people home, nursing home, residential care, retirement home
4 |  survivor, post-covid lethargy, deaths, long covid, long haulers, recover
5 |  reopen NHS, reopen hospital, reopen GP surgeries, resume dental, reopen dental, non emergency procedure, dentist, health service, routine care, NHS capacity, mammogram, smear test, breast scan, bowel screening, reduction accidents, reduction in viral infections, reduction asthma, blood donations, pregnancy support, non-covid health
6 |  depression, anxiety, mood, mental health, wellbeing, lonely, loneliness, social isolation, suicide, self harm, insomnia
7 |  Scotland, Scottish, Scots, Scot Gov, SNP, SG approach, devolved administrations, Nicola Sturgeon, First Minister, Holyrood
8 |  lockdown, restriction, measures, phase, routemap, mandatory quarantine, guideline, guidance, advice, enforce rules, suppress virus, tackling virus, exit strategy, public compliance, civil liberties, enforcement, freedom, herd immunity, human rights, law, legislation, mass gathering, scientific advice, timeline, circuit breaker
9 |  racism, ethnicity, minority, ethnicity outcomes, BAME, BME, black, non-white, discrimination, prejudice, disparities, bias, religion, Moslem, Muslim, Islam, Sikh, Hindu, Asian, Indian, Pakistani, Bangladeshi, South Asian, Chinese, Caribbean, Mixed, Multiple, South East Asian, Middle Eastern, Arab, African, Black Caribbean, Black African, Jewish, Jews, ethnic minorities, racial inequality, black ethnic minorities, migrant workforce, xenophobia, hate crime
10 |  inequalities, rich, well-off, wealthy, working class, homeless, homelessness, poverty, less fortunate, healthy over 70s, older adults, equality, rural communities, digital equality, universal broadband, broadband access, broadband connectivity, vulnerable children, vulnerable families, vulnerable households, vulnerable communities, fair ethical, gender
11 |  increase xenophobia, crime rate, social stigma, increase racism, community support, increase volunteering, collective solidarity, togetherness, help neighbour, community spirit, rainbows in windows
12 |  education, school, home schooling, teaching, blended learning, remote learning, online learning, student, pupil, teachers, virtual classes, class size, nursery, university, additional needs children, special needs child, exam
13 |  pollution, reduction traffic, green economy, green recovery, waste management, climate impact, environment impact, environment effect, nature impact, nature effect, sustainability, plastic waste, wildlife
14 |  outdoor exercise, family time, quality of life, life balance, new normal, long term impact, social distancing, stay home
15 |  visit household, meet household, extended household, gathering, bubble, family visit, family life, social life, social contact, meet friend, see friend, see loved ones, wedding, marriage ceremony, civil partnership, divorce, childcare, places of worship, child adoption, child fostering, domestic abuse, funeral, pets
16 |  gym, golf, tennis, camping, swimming pool, motorcycling, hill-walking, outdoor activities, indoor activities, running, eating out, exercise, fishing, hospitality, horse riding, personal services, haircut, beauty treatment, facial, massage, cinema
17 |  drive to exercise, car journeys, holiday plans, holiday abroad, caravan site, visit second home, self-catering, camping, hotel, public transport, travel, tourism, border control, border check, plane, airport, bus, tram, train, air bridge
18 |  garden centre, barber, hairdresser, beauty salon, recycling centre, property market, construction work, construction site, pub, business damage, cafe, restaurant, shop, retail, shopping centers, nightclub, music venue, theater, concert, business re-opening, body piercing, dog grooming, soft play centre, small business, business restriction, curfew
19 |  home working, work from home, remote work, office work, key worker, healthcare worker, workplace, employees, early retirement, self employed
20 |  handwashing, sanitiser, mask, face covering, facial covering, PPE, public hygiene, hygiene standards, hand hygiene
21 |  cashless, contactless, online shopping, home delivery, supermarkets, shopping trip, shopping habits, high street shops
22 |  job loss, unemployed, unemployment, made redundant, jobseeker, benefits, furlough, social security, dole, economic uncertainty, basic income, universal credit, deprivation, low income
23 |  economic recovery, business support, economy, economic impact, business recovery, sector, industry
24 |  points system, renew, security risk, service return, statistics, transition, transition arrangements
TABLE III: Theme IDs with list of filter keywords

An indication of how many messages are tagged with each topic ID is displayed
in Table II, and as a proportion in Figures 2 and 3. The ratio of themes is
similar for each platform, with themes 2,15, and 22 (shielding, social life,
unemployment) representing large sections of the discourse, while interesting
themes 8, 9, and 11 (ending lockdown, BAME issues, community solidarity) are
much more prevalent as a proportion of Twitter messages. This may indicate a
differential in the demographic makeup and prevalent concerns of users of the
two platforms [62].

##  IV Dataset Features

###  IV-A Publication and Hydration

In order to comply with network policies for researchers conducting data
collection via the Twitter [63] and Crowdtangle [64] platforms, we are able to
share only the IDs of material that we collected. This precludes us from
sharing the location or text of our collected posts directly.

There are several tools that will enable researchers to rehydrate this data to
return the full content of the post or profile. For Twitter, we note that the
DocNow Hydrator 777https://github.com/DocNow/hydrator and Tweepy Python
library 888https://www.tweepy.org/ can fulfil this function, however the only
option for rehydration of Crowdtangle-provided data is to apply for access to
the platform and gain access to the official API
999https://github.com/CrowdTangle/API/wiki.

Note: there are two separate IDs available for Facebook posts via the
Crowdtangle API, the platform ID used by Facebook itself, and the Crowdtangle
ID, used by the analytics platform. We have provided the Crowdtangle ID in our
dataset, and so when hydrating posts the API endpoint
http://api.crowdtangle.com/ctpost/:id should be used.

Table IV shows the fields included in the published dataset, along with a
short description of the contents and the data type.

Platform | Field Name | Data Type |  Description
---|---|---|---
Twitter | Date Created | String |  Tweet published date
| Message ID | String |  Unique Tweet identifier
| Theme ID | Integer |  ID of theme allocated by keyword analysis
Facebook | Date Created | String |  Post published date
| Crowdtangle Post ID | String |  Unique identifier for post in Crowdtangle system, in pipe-separated format
| Theme ID | Integer |  ID of theme allocated by keyword analysis
TABLE IV: Dataset features with data type declaration and description

###  IV-B Analysis

In this section, we carry out some preliminary data analysis using both the
Twitter and Facebook datasets as an indication of some potential directions
for further research. In conducting this analysis, we randomly sample 10,000
rows without replacement from each set of social network data before
rehydrating the message content.

####  IV-B1 Word Clouds

We present here a set of word cloud visualisations of the message content for
Twitter (Figure 4) and Facebook (Figure 5) posts. Message texts were first
processed to remove stopwords before being ranked by a frequency analysis.

Figure 4: Word cloud for random sampling of Twitter message texts Figure 5:
Word cloud for random sampling of Facebook post texts

Differences in the topics covered in this small random sample of each network
are immediately obvious: Facebook posts appear far more general and less
obviously connected to the pandemic, however the key terms do appear with some
frequency.

####  IV-B2 Topic Modelling

Using a Latent Dirichlet Allocation (LDA) approach, we model the message texts
for each respective platform as the product of a set of unobserved generative
topics. Each ’topic’ is generally understood to consist of a set of
semantically-linked words which taken together define an explanation for the
presence of each word in each message text. We define our maximum number of
topics as five for the purposes of this analysis. The five most determining
words for each topic are presented below in Figures 6 and 7.

Interestingly, while the Twitter topics seem to correlate broadly with Covid-
related terms in general discourse at the time, Facebook results vary more
widely. Indeed, one topic appears to be made up solely of French terms,
perhaps indicating a number of French-speaking ex-patriates or publications
among the entries in the dataset.

Figure 6: Topics derived from Twitter LDA model Figure 7: Topics derived from
Facebook LDA model

##  V Conclusion

In this work, we have presented the outlines of an extensive dataset of social
media messages related to the Covid-19 pandemic gathered from users in the
United Kingdom from the earliest days of awareness of the disease until after
widespread availability of effective vaccines. We have included millions of
postings from the two most popular social networks among British users,
Twitter and Facebook. Along with providing a rich source of data for multi-
modal analyses of public responses to various phases of the crisis, we have
also provided a framework for analysing the topics of discussion through
keyword analysis conducted with physicians and public policy officials. To
facilitate further research, and to promote the goal of further understanding
public responses to help engender a more efficient and effective set of public
health policies in future, we are releasing this information to the community.

##  VI Further Work

As detailed in recent research [65, 66], more attention should be directed
towards social media platforms as important resources for understanding the
dissemination and response to public health policy and messaging. In addition
to helping drive efforts to combat the ’infodemic’ of misinformation that
could contribute to negative personal and social decision-making around health
topics such as vaccine hesistancy [67, 68], leveraging social media could lead
to increased efficacy of health advocacy and involvement of under-represented
populations [69].

Previous research which used parts of this dataset has involved both vaccine
attitude analysis addressing early-stage (March-November 2020) optimism and
concern over vaccine development [70], and characterization of the nature and
frequency of later vaccine mentions in the United Kingdom, in an attempt to
better understand vaccine hesitancy trends [71]. These studies used sentiment-
based approaches to derive useful findings for public health communications,
including that Twitter texts appear to be more likely than Facebook posts to
include a negative bias against vaccine uptake, although the absolute ratio of
sentiments across the vaccine rollout campaign remained highly positive. In
terms of vaccine hesistancy and side effects, the study found that injection-
site and short term reactions (pain, redness, swelling, fever, headache) were
the most often mentioned, while mentions of blood clots appeared to correlate
with press coverage of the AstraZeneca vaccine.

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Richard Plant is a PhD candidate at Edinburgh Napier University in Scotland.
His research interests include statistical privacy tools, ethical machine
learning, and robust natural language processing.
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Amir Hussain received the B.Eng. (highest 1st Class Honours with distinction)
and PhD degrees from the University of Strathclyde in 1992 and 1997,
respectively. Following postdoctoral and academic positions held at the West
of Scotland (1996–1998), Dundee (1998–2000), and Stirling Universities
(2000–2018), he is currently a Professor and Founding Head of the Cognitive
Big Data and Cybersecurity (CogBiD) Research Lab at Edinburgh Napier
University. He has (co)authored more than 350 papers, including over a dozen
books and around 140 journal papers. Dr. Hussain is Founding Editor-in-Chief
of two leading journals: Cognitive Computation (Springer Nature), and BMC Big
Data Analytics (BioMed Central), and of the Springer Book Series on Socio-
Affective Computing, and Cognitive Computation Trends. He is an Associate
Editor for a number of prestigious journals, including Information Fusion
(Elsevier), AI Review (Springer), IEEE TRANSACTIONS ON NEURAL NETWORKS AND
LEARNING SYSTEMS, IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE, and the IEEE
TRANSACTIONS ON EMERGING TOPICS IN COMPUTATIONAL INTELLIGENCE.
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