Climate Action During COVID-19 Recovery and Beyond: A Twitter Text Mining Study

Abstract

The Coronavirus pandemic created a global crisis that prompted immediate large-scale action, including economic shutdowns and mobility restrictions. These actions have had devastating effects on the economy, but some positive effects on the environment. As the world recovers from the pandemic, we ask the following question: What is the public attitude towards climate action during COVID-19 recovery and beyond? We answer this question by analyzing discussions on the Twitter social media platform. We find that most discussions support climate action and point out lessons learned during pandemic response that can shape future climate policy, although skeptics continue to have a presence. Additionally, concerns arise in the context of climate action during the pandemic, such as mitigating the risk of COVID-19 transmission on public transit.

Full text

Climate Action During COVID-19 Recovery and
Beyond: A Twitter Text Mining Study
Mohammad S. Parsa1, Lukasz Golab1, and S. Keshav2
1University of Waterloo, Canada {msparsa,lgolab}@uwaterloo.ca
2University of Cambridge, U.K. sk818@cam.ac.uk
Abstract. The Coronavirus pandemic created a global crisis that prompted
immediate large-scale action, including economic shutdowns and mobility
restrictions. These actions have had devastating effects on the economy,
but some positive effects on the environment. As the world recovers from
the pandemic, we ask the following question: What is the public attitude
towards climate action during COVID-19 recovery and beyond? We an-
swer this question by analyzing discussions on the Twitter social media
platform. We find that most discussions support climate action and point
out lessons learned during pandemic response that can shape future cli-
mate policy, although skeptics continue to have a presence. Additionally,
concerns arise in the context of climate action during the pandemic, such
as mitigating the risk of COVID-19 transmission on public transit.
Keywords: text mining ·social media ·climate change ·COVID-19
1 Introduction
The COVID-19 pandemic has created a global crisis. Controlling the spread of
the virus required immediate large-scale action, including shutdowns and mobil-
ity restrictions. While these actions have had negative effects on the economy,
the corresponding reduction in carbon emissions resulted in some positive im-
pacts on the environment such as improvements in air quality [4] and an increase
in wildlife breeding success [16].
As the world recovers from the pandemic, these positive environmental im-
pacts are at risk of vanishing. It has therefore been suggested that COVID-19
recovery programs should include climate action such as investing in sustainable
infrastructure and technologies [9,22]. Others additionally suggest building on
social changes such as working from home [3,13].
An important aspect of policymaking is an understanding of public opinion,
especially now, given the social and economic sacrifices required to combat the
pandemic. We therefore ask the following question in this paper: What is the
public attitude towards climate action during COVID-19 recovery and beyond?
Online social media platforms such as Twitter have been identified as critical
tools for reflecting and predicting public opinion on a variety of topics. We
therefore answer our question by analyzing messages posted on Twitter during
arXiv:2105.12190v1 [cs.SI] 25 May 2021

2 Mohammad S. Parsa, Lukasz Golab, and S. Keshav
the first wave of the COVID-19 crisis (January to August 2020) that include
keywords related to both the pandemic and climate change.
Our methodology consists of the following steps. First, we apply a topic
modelling algorithm that segments the tweets based on the words used in or-
der to identify topics of discussion. Next, we measure the sentiment of opinions
expressed on each topic as well as the percentage of tweets classified as inflamma-
tory. Finally, we inspect a sample of tweets belonging to each topic to confirm the
nature of the topic and the sentiment of the opinions on this topic. To the best
of our knowledge, our work is the first data-driven study of public commentary
on climate action during the pandemic, as reflected on social media.
The remainder of this paper is organized as follows. Section 2 discusses related
work; Section 3 explains the data and methods used; Section 4 discusses the
results; and, Section 5 concludes with insights and directions for future work.
2 Related Work
There has been previous social media mining work (mainly using Twitter) on
understanding attitudes towards climate science and climate action; see [20]
for a recent survey. The focus has been on discussion topics, spatiotemporal
patterns, communities of activists and skeptics, correlations between Twitter
activity, weather and political events, political polarization, public engagement
in climate discussions, as well as scientific consensus on climate change. However,
these studies were done before the COVID-19 pandemic.
COVID-19 discussions on Twitter and other social media have also been
studied recently. Areas of focus include topic modelling [28], frequently asked
questions [1,11], effects of the pandemic on mental well-being [12], and impacts
of rumors and misinformation [2,7,21,24]. However, climate-related discussions
have not been studied in detail, and we fill this gap in this paper.
3 Data and Methods
This study uses data from the Twitter social networking and microblogging
platform. Twitter users post messages with up to 280 characters, containing
text, images, hyperlinks or hashtags, which are words starting with the symbol
‘#’ and are used to index keywords and topics. Users may follow, i.e., ask to
receive tweets from, other users, and may forward, i.e., retweet, messages written
by other users. There are currently over 330 million active users on Twitter
worldwide, sending approximately 500 million tweets per day.
Recent work has identified over 81 million tweets containing words related
to the COVID-19 pandemic, spanning from January 1, 2020 to July 31, 2020
[6]. This dataset contains only the tweet IDs, and we obtained the full tweets
via the public Twitter download interface. This dataset only contains ‘public’
tweets and omits those marked as ‘private’ and therefore only visible to one’s
followers.

Climate Action During COVID-19 Recovery and Beyond 3
Following the methodology used in prior work on climate change discussions
on Twitter [8,14,15], we identified a subset of these 81 million tweets that contain
at least one of the following terms: climate change, #climatechange, global warm-
ing or #globalwarming. We then removed non-English tweets, leaving 155,716
tweets, and we removed retweeted copies, finally resulting in 39,461 tweets for
analysis. While removing retweeted copies, we labelled each distinct tweet with
the number of times it was retweeted.
Our analysis relies on topic modelling, which is a text mining tool that seg-
ments a collection of documents (in this case, tweets), with each segment con-
taining documents that use similar words and thus are likely to describe the
same topic. To prepare the tweets for topic modelling, we performed standard
text pre-processing, as in related work on social media mining [17,19]. We con-
verted all letters to lower case, and we removed punctuation symbols, hyperlinks
and stopwords (which are words that serve a grammatical purpose but do not
convey any semantic meaning, such as “and”, “the”, etc.). We then lemmatized
the remaining words. Lemmatization groups together the inflected forms of a
word. For example, words such as “plays”, “played” and “playing” are all lem-
matized to “play”. We then vectorized each tweet based on the remaining words.
For each tweet, the ith entry of its vector corresponds to the Term Frequency-
Inverse Document Frequency (TF-IDF) of the ith word in the set of (remaining)
words occurring in the dataset. The TF-IDF score of a given word for a given
tweet was computed by dividing the number of times the word appears in the
tweet (TF) by the logarithm of the fraction of tweets that contain at least one
occurrence of this word (DF). TF-IDF is frequently used when vectorizing text
since it considers both the uniqueness of a word in the dataset and the impor-
tance of the word to the specific document.
We applied the Non-negative Matrix Factorization (NMF) method for topic
modelling [27] to the vectorized tweets. NMF clusters the tweets into topics and
produces a list of representative terms for each topic. We report the following
information for each topic.
–Each representative term is given a score by the NMF algorithm, and we
selected the top-10 highest-scoring representative terms for each topic.
–We extracted the five most frequent word n-grams (for n up to three, i.e.,
sequences of up to three consecutive words) within the tweets assigned to
each topic.
–Following recent work on sentiment analysis of COVID-19 discussions on
social media [23], we calculated the percentage of tweets having a positive
sentiment using the HuggingFace Transformer sentiment analyzer [26].
–We calculated the percentage of comments identified as inflammatory using
the HateSonar hate speech classifier [10], which was shown to work well on
Twitter data.
–To confirm the nature of the topics, we manually examined 100 most fre-
quently retweeted tweets for each topic.
NMF requires the number of topics as input. To select an appropriate number
of topics, we ran NMF to produce between 5 to 90 topics and computed the

4 Mohammad S. Parsa, Lukasz Golab, and S. Keshav
Fig. 1. Coherence scores for different numbers of topics
coherence [18] of each output (higher is better). Coherence measures the extent
to which the top representative terms representing each topic are semantically
related. As shown in Figure 1, coherence was highest at 15 topics.
4 Results
Topic modelling suggests two main themes in Twitter discussions: climate action
during COVID-19 recovery (6 out of 15 topics; summarized in Table 1); and
lessons learned for future climate action (8 out of 15 topics; summarized in
Table 2). These two themes cover all but one topic, containing specific discussions
about China’s role in the pandemic and in the global carbon footprint.
Tables 1 and 2 contain the following information, from left to right: the
topic number, the top-10 representative terms, the frequent n-grams, a topic
description based on manual inspection of the most frequently retweeted tweets,
the number of tweets assigned to the topic, the percentage of tweets with offensive
language (OL), and the percentage of tweets with a positive sentiment (PS).
The topics are sorted by size, i.e., the number of tweets assigned to them. In the
remainder of this section, we discuss the two main themes in more detail.
4.1 Theme 1: Climate action during COVID-19 recovery
Table 1 summarizes the topics related to climate action during the pandemic and
beyond. Over 60 percent of the tweets in our dataset are related to this theme. In
terms of sentiment, topics 1, and 9 have the highest fraction of positive tweets,
encouraging climate action during COVID-19 recovery.
Topic 1 and 2, the most frequent topics, discuss recent improvements in
air quality and express concerns about their temporary nature. We show two
example tweets below.

Climate Action During COVID-19 Recovery and Beyond 5
Table 1. Topics related to climate action during COVID-19 recovery
Representative
terms
Frequent n-grams Topic description # %OL %PS
1 need, time, new,
threat, tackle,
health, action,
recovery, make,
future
earthday 2020,
public health ,
economic recovery,
recovery plan,
existential threat
Economic recovery
plans should include
climate action such as
reducing fossil fuel use
and CO2 emissions.
7901 1.5 31.4
2 human, pollution,
emission, nature,
earth, china, re-
duce, way, air,
carbon
air pollution,
carbon emission,
fossil fuel, human
die, economic
demographic
Economic shutdowns
reduced CO2 emissions
and air pollution, but
more action is needed.
However, activities
such as using public
transit become risky
during the pandemic.
4793 1.7 20.7
7 think, thing, know,
wait, bad, good, re-
ally, cause, hear,
happen
people think, think
bad, bad would,
think virus, think
serious
General discussions
about the pandemic
and long-term effects
of climate change.
2468 4.8 19.4
9 fight, help, lesson,
money, let, use,
warren, elizabeth,
deal, way
help fight, fight
back, elizabeth
warren, deal chi-
nese, chinese must
fight
Supportive and dis-
couraging discussions
around the use of pan-
demic recovery funds
for climate action.
1932 1.8 31.8
12 stop, work, tax, pay,
eat, animal, care,
justice, racial, poor
stop eat, eat ani-
mal, racial justice,
poor racial justice,
stop eat animal
Suggesting a plan-
based diet to protect
the environment and
prevent the spread of
animal-borne diseases.
1423 6.1 18.6
“#COVID19 locking down the whole world can be considered a LARGE
SCALE EXPERIMENT for reduction of emissions. WE all can see the differ-
ence in the BLUE skies and through breathing clean air. Q: What will happen
when #Covid 19 leaves us alone#Enviroment #ClimateChange’
“COVID-19 shutdowns are clearing the air, but pollution will return as economies
reopen — The shutdowns aren’t slowing climate change.”
As a result of these concerns, many tweets expressed the opinion that eco-
nomic recovery plans should target climate action. Suggested actions include re-
ductions in the consumption of fossil fuels (Topic 1), encouraging a sustainable
lifestyle with a plant-based diet (Topic 12), and investments in green transporta-
tion infrastructure such as protected cycle lanes and making public transit safe
to use during the pandemic (Topic 9). We give an example tweet below.
“Please support Clean State’s campaign to develop a budget that supports
sustainable employment and economic recovery package based on addressing a

6 Mohammad S. Parsa, Lukasz Golab, and S. Keshav
threat even greater than COVID-19: #ClimateChange. Sign up to support our
open letter.”
In addition to calling on governments and world leaders to support climate
action, many tweets suggested individual climate actions that can be done during
the pandemic (Topic 1). As an example, the #DarkSelfieChallenge mentioned in
one of the most frequently retweeted messages encouraged people to save energy
by turning off their lights and taking a selfie in the dark:
“Help the planet from home! With Hyundai, I’m taking part in the #Dark-
SelfieChallenge. Turn all the lights off and take a selfie with the flash on. Show
yourself in the dark to shed light on climate change. #DarkSelfieChallenge #Earth-
Day #StayHome #HyundaixBTS #NEXO”
Other examples include encouraging cycling as a sustainable mode of trans-
portation that allows social distancing, as expressed in the following tweet.
“All hail the mighty bicycle: France to pay for cycling lessons and bike re-
pairs to fight both coronavirus and climate change - Bicycles promote physical
distancing - Paris building 750km (466 miles) more bike lanes - Bogota, Berlin,
Brussels, Milan all going big on bicycles too”
In contrast to the supporters of green recovery plans, some tweets argue that
recovery plans should only target economic growth (topic 9), at least for now,
and should suspend climate action for faster economic recovery. According to
these tweets, it would be more dangerous to the environment in the long run
if the carbon tax drives businesses into bankruptcy, as this would lead to a
weakened economy that is unable to fund climate action plans (Topic 7). We
show two example tweets below.
“The only way that would work is when the economies are stable and we doing
it to tackle global warming and climate change. Where we indoors so planet earth
can’t breathe. Otherwise we ain’t doing that **** as a remembrance”
“Are you seriously that dumb to bring climate change and adding additional
carbon tax at a time of this pandemic? The 75% is for small to medium sized
businesses. Rgat wont come free. They will pay double at the other end. Same
for anyone deferring mortgages, car payments etc.”
4.2 Theme 2: Lessons learned during the pandemic to help combat
climate change
The second theme focuses on the lessons learned from the pandemic and dis-
cusses whether these lessons should be applied to mitigate climate change (Table
2). Topic 10 has a significantly high fraction of positive tweets that encourage
applying lessons learned from the COVID-19 crisis to fighting climate change.
Topics 11, 13 and 15, which criticize government policies and express skepticism
towards the virus and climate change, have the lowest fraction of positive tweets.
The fraction of offensive content is generally low, though it appears that skeptics
express their thoughts more negatively and aggressively compared to other users
A frequently mentioned lesson (Topic 10) is that global consensus and collab-
oration were critical during the COVID-19 pandemic and will be critical in future

Climate Action During COVID-19 Recovery and Beyond 7
Table 2. Topics related to lessons learned during the pandemic to combat climate
change
Representative
terms
Frequent n-grams Topic description # %OL %PS
3 say, science, scien-
tist, believe, expert,
listen, response,
pope, deny, ignore
scientist say, be-
lieve science, listen
expert, pope fran-
cis, anti science
Discussions of state-
ments made by scien-
tists, skeptics, and pub-
lic figures.
3835 3.6 18.5
4 world, end, war,
save, leader, react,
happen, post, year,
control
world war, world
leader, end world,
save world, world
economy
The pandemic and cli-
mate change are issues
that must be addressed
by the whole world.
3047 3.4 26.8
5 people, die, care,
believe, young,
make, old, million,
worry, imagine
people die, million
people, old peo-
ple, people believe,
take seriously
The Coronavirus is
dangerous for older
adults; climate change
is dangerous for future
generations.
2718 5.7 17.7
8 like, look, disease,
make, thing, feel, is-
sue, treat, sound,
right
disease like, issue
like, infectious dis-
ease, threat like,
disease threat
Climate change may
become a crisis similar
to or worse than the
COVID-19 pandemic.
2340 5.6 21.4
10 crisis, warn, uk,
avoid, lesson, op-
portunity, tackle,
learn, climatecrisis,
spur
health crisis, eco-
nomic crisis, green
recovery, avoid cri-
sis, tackle crisis
Lessons learned from
pandemic response
should be applied to
climate action.
1853 0.4 31.2
11 trump, blame, war-
ren, elizabeth, dis-
ease, point, presi-
dent, donald, long,
obama
elizabeth warren,
donald trump,
blame trump,
blame disease,
blame disease like
Criticisms of the U.S.
pandemic and climate
policies.
1808 4.5 12.1
13 hoax, chinese,
trump, real, im-
peachment, demo-
crat, russian, fake,
president, russia
call hoax, chi-
nese hoax, virus
hoax, impeach-
ment hoax, hoax
impeachment
Skepticism towards the
Coronavirus and cli-
mate change, e.g., call-
ing the Coronavirus a
hoax.
1059 5.6 10.1
14 kill, year, worry,
million, end, heat,
maybe, thing, right,
want
kill people, kill
million, virus kill,
kill everyone, total
death
Climate change may
be as devastating and
deadly as the Coron-
avirus crisis.
874 7.9 13.3
15 model, wrong, use,
computer, accurate,
predict, base, year,
death, tell
computer model,
model wrong,
model predict,
virus model
Skepticism towards
pandemic and climate
models.
802 1.2 13
climate action. It was observed that some countries implemented pandemic re-

8 Mohammad S. Parsa, Lukasz Golab, and S. Keshav
sponse measures early and were able to control the spread of the virus; similarly,
preparation and preventive measures will be important in the context of climate
action. Additionally, specific pandemic response actions were mentioned as being
helpful in the context of climate action, such as travel restrictions and working
from home (Topic 10). We give three example tweets below.
“Great piece on the lessons that the Coronavirus response already has for cli-
mate change: early, effective and far-reaching action (dont wait for consequences
to unfold) and international collaboration and solidarity.”
“Inslee: ‘we should not be intimidated by people who say you should not use
this COVID crisis to peddle a solution to climate change.’ He’s using this crisis
to push his agenda. He doesn’t care about businesses, life savings, careers, lost.”
“Global inaction on climate change offers grim lessons in the age of coro-
navirus: ‘Only in hindsight will we really understand what we gambled on’ one
climate scientist said.’ ”
Another common opinion reflecting lessons learned was that climate change
crisis may be similar to or worse than the Coronavirus, for example, in terms of
fatality rates (Topic 14). It was argued that while the virus is more dangerous for
the older population, climate change will be dangerous for the next generation
(Topic 5). The following tweet is one of many that express this opinion.
“Corona Virus is an existential threat to old people. Climate Change is an ex-
istential threat to young people. Recipe for a coalition of the many #BernieSurge’’
The third common discussion topic in the context of lessons learned was
the role of science in decision and policymaking (Topic 3), as expressed by the
following tweet.
“Diseases like coronavirus remind us why we need robust institutions and
investments in public health, and a government that is ready to respond at any
moment. That means using science-based policy and confronting climate change,
which will affect how diseases emerge and spread.”
On a related note, there were negative reactions to statements made by pub-
lic figures who underestimated the seriousness of the Coronavirus and climate
change (Topic 11). However, Pope Francis’ statement that the “Coronavirus
pandemic could be nature’s response to the climate crisis” received a mixed re-
sponse. Some tweets agreed with this statement, while others pointed out a lack
of scientific proof (Topic 3).
On the other hand, a small minority of skeptics continued to doubt climate
change and criticized any use of the pandemic to advance climate action (Topic
13), as shown in the following tweet.
“Ms Swedish environmental goddess her team of alarmed activists may try to
link #coronarovirus to #ClimateChange Won’t be surprised if they made a speech
relating #ClimateEmergency to #coronoavirus outbreak lol #climatechangehoax
#ClimateHoax #auspol #auspol2020 #Australia ”
Furthermore, some tweets questioned the accuracy of climate and COVID-19
prediction models. They argued that these models are not reliable at estimating
deaths caused by the virus and deaths that climate change will cause in the
future (Topic 15). We give an example tweet below.

Climate Action During COVID-19 Recovery and Beyond 9
“Why would anyone trust the climate change models when the Covid-19 mod-
els have been off by an incredible (perhaps deliberate) amount?”
5 Discussion and Conclusions
Our study of Twitter discussions on climate change during the COVID-19 pan-
demic revealed two main themes: Climate action during COVID-19 recovery and
lessons learned for the future.
Climate action during COVID-19 recovery is largely supported on Twitter,
with positive opinions on actions such as investing in sustainability and ed-
ucation, as well as social changes such as promoting the use of bicycles and
public transit. However, some tweets express concerns about the higher risk of
contracting the Coronavirus when using public transit. Thus, it appears that
there is at least some willingness to continue making environmentally-friendly
decisions during the pandemic as long as policies are in place to mitigate risks.
This finding should be of interest to local governments wishing to encourage
climate-friendly behaviour during the pandemic.
Similarly, many tweets positively reflect on the lessons learned during the
pandemic that may shape future climate action, such as the importance of pre-
ventive measures, the role of science in public policymaking, and the need for
coordinated global action. In particular, we identified a topic (Topic 8; Table 2)
reflecting the opinion that climate change may become a worse global crisis
than COVID-19, underscoring the need for global action. Additionally, we found
tweets that criticized politicians who ignore both climate change and COVID-
19 safety protocols such as face masks, reinforcing the importance of making
scientifically-sound decisions in the future (Topic 11; Table 2).
On the other hand, as observed in pre-pandemic social media studies [5,25,20],
climate skeptics continue to have a presence on Twitter. We additionally found
that skeptics have incorporated the pandemic and the associated economic cri-
sis into their reasoning for suspending climate policies such as the carbon tax.
Moreover, some tweets use skepticism for one issue to justify skepticism for an-
other, as evidenced by tweets calling both climate change and the Coronavirus
pandemic a hoax, and those that mistrust climate models because COVID-19
models are believed to be inaccurate. An in-depth investigation on how misin-
formation about climate change creates misinformation about the pandemic and
vice versa is an interesting direction for future research.
One limitation of this study is its focus on English language content on the
Twitter platform. Thus, another direction for future work is to compare public
attitudes towards climate action during COVID-19 recovery in Asia, Europe and
North America, and correlate these opinions with pandemic response policies.
References
1. Anderson, A.A.: Effects of social media use on climate change opinion, knowledge,
and behavior. Oxford Research Encyclopedia of Climate Science pp. 1–20 (03 2017)

10 Mohammad S. Parsa, Lukasz Golab, and S. Keshav
2. Apuke, O.D., Omar, B.: Fake news and COVID-19: modelling the predictors of fake
news sharing among social media users. Telematics and Informatics 56, 101475
(2021)
3. Belesova, K., Heymann, D.L., Haines, A.: Integrating climate action for health into
covid-19 recovery plans. BMJ p. m3169 (2020)
4. Berman, J.D., Ebisu, K.: Changes in u.s. air pollution during the COVID-19 pan-
demic. Science of The Total Environment 739, 139864 (2020)
5. Boussalis, C., Coan, T.G.: Text-mining the signals of climate change doubt. Global
Environmental Change 36, 89–100 (2016)
6. Chen, E., Lerman, K., Ferrara, E.: Tracking social media discourse about the
COVID-19 pandemic: Development of a public coronavirus twitter data set. JMIR
Public Health and Surveillance 6(2), e19273 (2020)
7. Cinelli, M., et al.: The COVID-19 social media infodemic. Scientific Reports
10(16598), 1–10 (2020)
8. Cody, E.M., et al.: Climate change sentiment on twitter: An unsolicited public
opinion poll. PLOS ONE 10(8), e0136092 (2015)
9. Cohen, M.J.: Does the COVID-19 outbreak mark the onset of a sustainable con-
sumption transition? Sustainability: Science, Practice and Policy 16(1), 1–3 (2020)
10. Davidson, T., et al.: Automated hate speech detection and the problem of offensive
language. ICWSM 11, 512–515 (2017)
11. Fernandez, M., et al.: Talking climate change via social media. In: WebSci. pp.
85–94 (2016)
12. Gao, J., et al.: Mental health problems and social media exposure during COVID-
19 outbreak. PLOS ONE 15(4), e0231924 (2020)
13. Howarth, C., et al.: Building a social mandate for climate action: Lessons from
COVID-19. Environmental and Resource Economics 76(4), 1107–1115 (2020)
14. Jang, S.M., Hart, P.S.: Polarized frames on “climate change” and “global warming”
across countries and states: Evidence from twitter big data. Global Environmental
Change 32, 11–17 (2015)
15. Kirilenko, A.P., Stepchenkova, S.O.: Public microblogging on climate change: One
year of twitter worldwide. Global Environmental Change 26, 171–182 (2014)
16. Manenti, R., et al.: The good, the bad and the ugly of COVID-19 lockdown effects
on wildlife conservation: Insights from the first european locked down country.
Biological Conservation 249, 108728 (2020)
17. Missier, P., et al.: Tracking dengue epidemics using twitter content classification
and topic modelling. In: Current Trends in Web Engineering, pp. 80–92. Springer
International Publishing (2016)
18. O’Callaghan, D., Greene, D., Carthy, J., Cunningham, P.: An analysis of the coher-
ence of descriptors in topic modeling. Expert Systems with Applications 42(13),
5645–5657 (2015)
19. Ostrowski, D.A.: Using latent dirichlet allocation for topic modelling in twitter.
In: ISCS. pp. 493–497 (2015)
20. Pearce, W., et al.: The social media life of climate change: Platforms, publics, and
future imaginaries. Wiley Interdisciplinary Reviews: Climate Change 10(2), e569
(2019)
21. Pennycook, G., et al.: Fighting COVID-19 misinformation on social media: Exper-
imental evidence for a scalable accuracy-nudge intervention. Psychological Science
31(7), 770–780 (2020)
22. Rosenbloom, D., Markard, J.: A COVID-19 recovery for climate. Science
368(6490), 447–447 (2020)

Climate Action During COVID-19 Recovery and Beyond 11
23. Sanders, A.C., et al.: Unmasking the conversation on masks: Natural language
processing for topical sentiment analysis of COVID-19 twitter discourse. medRxiv
(2020)
24. Tasnim, S., Hossain, M.M., Mazumder, H.: Impact of rumors and misinformation
on COVID-19 in social media. Journal of Preventive Medicine and Public Health
53(3), 171–174 (May 2020)
25. Williams, H.T., et al.: Network analysis reveals open forums and echo chambers
in social media discussions of climate change. Global environmental change 32,
126–138 (2015)
26. Wolf, T., et al.: Transformers: State-of-the-art natural language processing. In:
EMNLP. pp. 38–45 (2020)
27. Xu, W., Liu, X., Gong, Y.: Document clustering based on non-negative matrix
factorization. In: SIGIR. p. 267–273 (2003)
28. Xue, J., et al.: Twitter discussions and emotions about the COVID-19 pandemic:
Machine learning approach. Journal of Medical Internet Research 22(11), e20550
(Nov 2020)