TWEETING THEENVIRONMENT #BREXIT

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TWEETING THEENVIRONMENT #BREXIT

BY

JINGRONG TONGBrunel University London, UK

LANDONG ZUOIT Solution Architect, UK

United Kingdom � North America � JapanIndia � Malaysia � China

Emerald Publishing LimitedHoward House, Wagon Lane, Bingley BD16 1WA, UK

First edition 2018

Copyright r 2018 Jingrong Tong and Landong Zuo.Published under exclusive licence

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To Daniel

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ACKNOWLEDGEMENTS

The book originates from the authors’ paper presented at the

International Environmental Communication Association

(IECA) Annual Conference in Leicester, 2017. The authors

would like to thank Jen McCall of Emerald Publishing for

her enthusiasm and support for this book project. Thanks

also go to Rachel Ward of Emerald Publishing for her assist-

ance. The authors want to thank the anonymous reviewer for

their comments on the book proposal and thank Brian

Keegan of University of Colorado Boulder for his suggestions

on how to present network graphs in greyscale in Gephi.

They also want to thank Sue Sparks for copy editing the

book. The authors are indebted to open-source applications

including Twitter4J, Elasticsearch, Kibana, Gephi and

Inskape and their contributors.

vii

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CONTENTS

List of Figures xiii

List of Tables xv

1. Introduction 1

PART 1

2. The Environment and Politics 13

2.1. The Global Rise of Environmental Awareness,

Movements and Politics 13

2.2. Environmental Awareness and the Politicisation

of the Environment in the UK 17

2.3. What Does the Environment Mean for the UK’s

EU Membership? 23

3. Twitter, the Media Ecology and Environmental

Communication 25

3.1. Twitter Communication Studies � The Current

State of Play 27

3.2. Studies of Environmental Communication on

Twitter 29

PART 2

4. Environmental Discourses on Twitter 35

4.1. Dichotomised Claims in the Pre-referendum

Discourses: Remain for Green versus

Leave for Green 37

ix

4.2. Dominant claims of Remain for Green on the

Referendum Day 49

4.3. A Radical Shift in the Environmental Discourses

after the Referendum Day 54

4.4. Weak Trends of the Discussions on Six

Environmental Topics 63

4.5. Conclusion 64

5. Elite Domination in the Asymmetrical Twitter Space 69

5.1. Asymmetric Structure of Twitter Communication 70

5.2. Decentralisation and Anarchy on Twitter 74

5.3. Attention-driven Prominence of Elites 81

6. Sparse ‘Communities’ and Their Green Bridges in

Twitter Networks 85

6.1. Previous Studies � SNA of Social Media 87

6.2. The Formation of Sparse ‘Communities’ 90

6.2.1. Sparse ‘Communities’ 91

6.2.2. Loosely Connected or Isolated

‘Communities’ 97

6.2.3. Lack of Interactions and One-way

Information Flow 99

6.3. The Prominence of Two Major Green Camps 100

6.4. Isolated State of News Media and Journalists 103

6.5. Exclusion of Established Political Parties and

Isolation of Non-green Politicians 107

6.6. Conclusion 108

7. Influential Social Actors: Competing for Discourses on

Twitter 111

7.1. Political Parties and Politicians 113

7.2. The ENGOs Camp 119

7.3. News Media and Journalists 123

7.4. Seven Other Categories of Influential Social Actors 125

7.5. Conclusion 127

x Contents

PART 3

8. Twitter and Environmental Politics 131

8.1. The Influence of Social Context 132

8.2. The Role of the ‘Technological Affordances’ of

Twitter 135

8.3. Old and New Players on Twitter 139

8.4. Conclusion: The Intertwining of Social Media

Communication and Offline Reality 140

9. Social Media Research: Towards an Inductive Approach 145

9.1. The Methodological Challenges 145

9.2. The Inductive Approach in Action � Let the

Data Tell Us the Story 151

9.2.1. Research Process 151

9.2.2. The Use of Mixed-methods and

Computational Applications to Explore

the Data 155

9.2.3. The Role of the Literature 157

9.3. The Advantages and Limitations of Our

Inductive Approach 158

Bibliography 161

Index 189

xiContents

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L IST OF FIGURES

Chapter 1

Figure 1 Daily Trend of Environmental Tweets OneMonth Before and After the Referendum (x-Axis:Dates; y-Axis: Number of Tweets).. . . . . . . 6

Chapter 4

Figure 2 Types of Themes in the Randomly SelectedSample Before, During and After 23 June2016. . . . . . . . . . . . . . . . . . . . 38

Chapter 5

Figure 3 Types of Users Whose Tweets Were Retweeted(Top 0.4%), Mentioned (Top 2%) and Favoured(Top 0.2%) Most. . . . . . . . . . . . . . . 73

Figure 4 Trends of Tweets by Top 10 ENGOs,Politicians, Political Parties and News Media. . 76

Figure 5 The Core Egonets of 16 Accounts Connected withPolitical Parties before (and on) the ReferendumDay. . . . . . . . . . . . . . . . . . . . . . 80

Chapter 6

Figure 6 The Full (Above) and Core (Below) Egonets ofTop 10 Politicians before (and on) the ReferendumDay. . . . . . . . . . . . . . . . . . . . . 95

xiii

Figure 7 The Core Egonets of Top 10 Online Mediabefore (and on) the Referendum Day. . . . . . 98

Figure 8 The Core Egonets of Top 10 News Mediabefore (and on) the Referendum Day. . . . . . 104

Figure 9 The Core Egonets of Top 10 Journalists before(and on) the Referendum Day. . . . . . . . . 106

xiv List of Figures

L IST OF TABLES

Chapter 4

Table 1 Top 10 Most Popular Users Before theReferendum Day and Their RepresentativeTweets. . . . . . . . . . . . . . . . . . . 41

Table 2 Top 10 Most Popular Users on the Day of theReferendum and Their Tweets. . . . . . . . 50

Table 3 Top Ten Most Popular Users After theReferendum Day and Their RepresentativeTweets. . . . . . . . . . . . . . . . . . . . 56

Chapter 5

Table 4 Statistics of Original Tweets and RetweetedTweets (Decimal = 1). . . . . . . . . . . . 71

Table 5 Top 10 Active and Popular Users.. . . . . . 75Table 6 Top 10 Politicians, ENGOs, Political Parties,

News Media, Online Media, and Journalists. 78Table 7 Top 10 Most Frequently Mentioned Users and

Top 10 Users They Most FrequentlyMentioned. . . . . . . . . . . . . . . . . 82

xv

Chapter 6

Table 8 Categories of 107 Influential Social Actors. . 91Table 9 Statistics for the Networks Surrounding the

Eight Groups of Key Actors before (and on)(above) and after (below) the Referendum Day(Decimal = 3). . . . . . . . . . . . . . . . 92

Chapter 7

Table 10 The Number of Original Tweets Published bythe Twitter Accounts of Four British PoliticalParties and Selected Politicians and theNumber of Their Retweets. . . . . . . . . . 116

xvi List of Tables

CHAPTER 1

INTRODUCTION

The level of politicisation of the environment has been low in

the UK. Environmental concerns, although becoming increas-

ingly prominent in public discourses, are always outweighed by

concerns over economic growth or other materialist matters

such as immigration (Carter, 2015; Hobolt, 2016; Pogorelis,

Maddens, Swenden, & Fabre, 2005; Spoon, 2009). Apart from

the poor environmental1 record of British politics, insufficient

media attention to environmental issues and concerns during

political events such as elections is also part of this tradition

(Boykoff et al., 2018; Brandenburg, 2006; Schmidt,

Ivanova, & Schafer, 2013). Over the recent decade, however,

the world has witnessed a growing tendency for social actors to

express their environmental concerns and demands on social

media platforms. The blossoming of social media communica-

tion complicates the state of environmental communication

and prompts a question � what is the capacity of social media

for helping promote environmental concerns in public

1By ‘environment’ we mean the natural world where humans, animalsand plants live.

1

discourses and increase the level of politicisation of environ-mental issues in the UK?

This question is underexplored in the existing literature onenvironmental politics and social media communication. Mostenvironment politics studies focus on examining the state ofenvironmental issues in government policies, the manifestos ofpolitical parties and political debates as well as the developmentof the Green Party, environmental groups and organisations (seefor example Brandenburg, 2006; Carter, 2007; Carter, 2015;Carter, Ladrech, Little, & Tsagkroni, 2017; O’Riordan &Jordan, 1995; Rüdig & Lowe, 1986; Spoon, 2009).

Studies of environmental communication on social mediatend to explore the way social media is used by ordinaryusers during particular situations such as disasters or environ-mental campaigns, the social media strategies of particularsocial actors such as the Green Party or Environmental Non-government Groups (ENGOs), or social media discourses onspecific environmental topics such as disasters, climate changeand environmental movements or campaigns (see for exampleAutry & Kelly, 2012; Binder, 2012; Hutchins, 2016; Kireyev,Palen, & Anderson, 2009; Li et al., 2016; Takahashi,Tandoc, & Carmichael, 2015).

While existing studies seldom combine the foci of these twofields of research, scarce scholarly attention has been paid tosocial media discussions on environmental issues in relationto political events such as elections or referenda and their impli-cations for environmental politics. The study of this topic,nevertheless, can offer something important. It sheds light onthe role of social media in shaping environmental politics. Itoffers a prism to inspect the level of politicisation of environ-mental issues in these discussions and in related social contexts.

The book addresses the above-mentioned underexaminedquestion through an inductive exploration of Twitter dis-courses and networks about environmental considerations in

2 Tweeting the Environment #Brexit

the discussions about the UK’s European Union (EU) referen-dum in 2016 and its aftermath. In the referendum, Britishvoters voted to leave the EU by a narrow margin of Leave51.9% versus Remain 48.1%. This outcome shocked theworld; and it is producing profound impacts on Britain,Europe and the world. The environment, which is an import-ant aspect of contemporary politics (Carter, 2007), is particu-larly prominent in the relationship between the UK and theEU. The role of the EU in establishing environmental stan-dards as an institutional regulator for its member countrieshas increased public acceptance of European integration(Carrubba & Timpone, 2005; Hix & Marsh, 2007;Hooghe & Marks, 1997; Prosser, 2016). The UK’s member-ship of the EU has led to the integration of domestic and EUenvironmental policy in the UK (Lowe & Ward, 1998). Giventhat environmental issues have been a pivotal justification forthe UK’s EU membership, if the environment was prioritisedover other materialist issues such as the economy and migra-tion in the referendum campaign and in the minds of voterswhen they were considering their votes, environmental aspectsshould have been crucial for the referendum. In spite of theirimportance, during the referendum campaign, environmentalissues were largely missing in the coverage of traditionalmedia, in political debates and in the considerations of votersin the UK (Deacon, Downey, Harmer, Stanyer, & Wring,2016; Hobolt, 2016).

Twitter, one of the most successful social networking sites,offered an alternative media platform for discussing specifictopics during the referendum campaign. On Twitter, userscan send one-to-one and one-to-many short2 messages totheir followers and receive messages from the ones they

2During the study period, the word limit of Twitter was 140 characters.However, it increased to 280 characters in 2017.

3Introduction

follow. They have the freedom to choose who they want tofollow and establish their own circles through following,retweeting and other interactions. A number of interestingquestions arise for this study: What discourses were con-structed in tweets on the environmental aspects of the referen-dum? How were users connected to one another in thisenvironmental communication? What social actors greatlyinfluenced the discourses and networks and how? What dothe discourses, networks and the roles played by social actorssuggest about environmental politics on Twitter and in theUK? The answers to these questions contribute to our under-standing of social media communication as well as environ-mental politics.

The study reported here draws on a computational dis-course and network analysis of 112,298 related tweets col-lected in real time and archived by using the TwitterStreaming Application Programming Interface (API) between24 May and 23 July 2016. The data include the tweets thatwere open to the public and contained any of the seven hash-tags, i.e. ‘#Referendum’, ‘#VoteLeave’, ‘#VoteIn’, ‘#EUref’,‘#VoteOut’, ‘#VoteStay’ and ‘#Brexit’ within the two monthssurrounding the referendum. We consider that the use ofhashtags in tweets indicates a strong intention on the part ofusers to link their tweets to the topic of the referendum, andtherefore, these tweets are most relevant to the studied topic.A total of 31,667,587 tweets were stored in the general data-set. We then followed the literature on environmental dis-courses and used the following keywords to filter the generaldataset: ‘environmental’, ‘environment’, ‘climate’, ‘energy’,‘polution’,3 ‘pollution’, ‘ecological’, ‘ecology’, ‘carbon diox-ide’, ‘global warming’, ‘CO2 emission(s)’, ‘green’, ‘naturalresources’, ‘greenhouse’, ‘gas emissions’, ‘gas emission’,

3This is an intentional misspelling as this word appears in some tweets.

4 Tweeting the Environment #Brexit

‘biodiversity’, ‘natural disasters’, ‘natural hazards’, ‘acid

rain’, ‘resource shortage’, ‘wildlife’, ‘nature reserves’, ‘habi-

tats’ or containing the hashtag: ‘nature’. Only environmental

tweets that contain any of the keywords (hashtag) were

included. Tweets including some keywords such as ‘Brexit

environment’ or ‘political climate’ or ‘Philip Green’, but not

suggesting topics about the environment were removed; the

rest of the tweets were saved in a separate dataset for further

analysis. There are 112,298 environmental tweets sent by

35,761 users in the dataset (referred to as environmental data

or dataset in the remainder of the book). However, these

tweets accounted for a mere 0.35% of all 31,667,587 tweets

about the referendum. Other topics such as immigration or

the economy received much more attention from users in the

general dataset. For example, the number of tweets mention-

ing the topic of immigration4 (891,843 tweets in the general

dataset) is about eight times the number of the total environ-

mental tweets, while that of the economy5 (2,846,045 tweets)

is even more than twenty-five times greater than environmne-

tal tweets. Such a low proportion of environmental tweets

suggests the marginalisation of the environmental aspects of

the referendum in Twitter communication. The trend over

time of the environmental data can be seen in Figure 1, which

shows the discussions reached a peak on 24 June 2016.

4Tweets contain one of the keywords ‘migration’, ‘immigration’, ‘immi-grant(s)’, ‘migrant(s)’, ‘refugee(s)’, ‘freedom of movement’, ‘free move-ment’, ‘right to travel’ and ‘mobility rights’.5Tweets contain any of the keywords ‘economy’, ‘economic’, ‘pound’,‘dow jones’, ‘income(s)’, ‘cost(s)’, ‘money’ , ‘currency’, ‘tourist(s)’, ‘credit(s)’, ‘job(s)’, ‘financial’, ‘finance’, ‘market(s)’, ‘price(s)’, ‘trade(s)’, ‘busi-ness(es)’, ‘worker(s)’, ‘deal(s)’, ‘economist(s)’, ‘wage(s)’, ‘tax(es)’, ‘shop(s)’, ‘fishing’, ‘farming’, ‘TTIP’, ‘bank(s)’, ‘investment(s)’, ‘propert(y/ies)’,and ‘revenue(s)’.

5Introduction

Figure 1. Daily Trend of Environmental Tweets One Month Before and After the Referendum (x-Axis:Dates; y-Axis: Number of Tweets).

6Tw

eetingthe

Environment#Brexit

The research adopted an innovative methodological

approach to inductively analysing ‘big’ social media data6

(see detailed discussions about our methodological approach

in Chapter 9). Using mixed methods (a detailed discussion of

research methods and procedures can be found in Chapter 9;

see Chapter 6 for a discussion of social network analysis), we

analysed not only the content and networks of social media

discussions but also the role played by key social actors in

these discussions and the social networks formed around

them. In so doing, we aimed to develop a ‘thick’ analysis of

social data, borrowing Geertz’s term (Geertz, 1973), so that

we could gain an in-depth insight into the relationship

between Twitter and environmental politics. Here, a ‘thick’

analysis means that we identified quantitative patterns in

social media data on the one hand, and on the other hand,

qualitatively interpreted the meanings of the patterns and

theorised them through establishing connections between the

patterns and social dynamics about environmental politics in

context.We achieved the ‘thick’ analysis in our exploration of the

data, using chosen computational applications, which mix

big data applications such as ElasticSearch and Kibana and

desktop applications such as Gephi and SPSS. The major dif-

ference between the two is the former is designed to operate

on a distributed environment such as clusters of multiple

nodes (machines/computers), while the latter is only run on a

single machine/computer and their scalability and computing

6The term ‘big data’ refers to an unprecedentedly large amount of datathat often come from different sources, but which may be interrelated.This data grows rapidly and are in various formats, as well as beingdynamic and unpredictable (Jin, Wah, Cheng, & Wang, 2015). In thisbook, our data is not typical big data. By ‘big’, we merely mean the bigsize of the social data in the dataset. Therefore, we used single quotationmark around big to indicate the difference.

7Introduction

ability is limited. Therefore, the former is much more scalableand performs better than the latter; and meanwhile they aresuitable and economic for handling big data. Our endeavourto inductively analyse ‘big’ data was made possible by ourstrategy to narrow ‘big’ social data down to ‘small’7 socialdata. Statistically identifying reasoned patterns in ‘big’ socialdata together with a close-reading of identified and extracted‘small’ key data enabled us to gain deep insights into Twittercommunication on this topic and its link to social contextsand implications for environmental politics. The scale of ourdata � 112,298 tweets � and the need to conduct social net-work analysis of the data made it impossible to adopt merelyconventional ways of doing social science research such ascontent analysis. Therefore, we turned to the help of compu-tational applications so as to quantitatively and statisticallyanalyse the ‘big’ data. However, we needed to narrow downthe ‘big’ data to a scale so that we were able to handle thedata manually and gain an inductive understanding of thedata; such data should nevertheless be meaningful, i.e. whichis the key data in the environmental dataset that can be fur-ther developed to tell a significant story and leads to a pro-found understanding of the ‘big’ data. An inductiveunderstanding of the meanings of the ‘small’ but meaningfuldata helped us interpret the quantitative and statistical pat-terns identified in computational analysis.

Along with the story being unfolded in our data explor-ation, we came to understand that the Green Party in theUK,8 ENGOs such as Greenpeace and Friends of the Earth,

7In the specific context, ‘small’ refers to small facets that are extracted,derived and reasoned from repository of ‘big’ data. Such facets can pro-vide meaningful data insights for researchers.8In the remainder of the book, we use the Green Party to refer to theGreen Party and its branches in the UK, unless we specify where it isoperated.

8 Tweeting the Environment #Brexit

their associates, and environmental online media achieved themost prominence in ‘green’ networks on Twitter. The techno-logical affordances of Twitter � the interactions between theinfrastructure of Twitter and the users’ use of Twitter �facilitated these green elite users in gaining discursive capacityon the platform. However, they did not succeed in winningthe referendum (as suggested by the result), the capacity forwhich was partly limited by the state of existing politics andenvironmental politics in the UK and partly limited by thefact that Twitter users may not represent the British electorate(see the discussion in the conclusion in Chapter 4). That is tosay, even if they had mobilised voters (on Twitter) to vote forRemain on the basis of green issues, these voters might havebeen too few in number; at the same time, there might havebeen more voters who cared more about other materialistissues such as the economy and immigration over the envir-onment. Established British political parties such as theConservative Party, the Labour Party and the LiberalDemocratic Party paid limited attention to environmentalconcerns in their tweets about the referendum. Overall, themarginalised environmental discourses in the related tweetspresented a national perspective and materialist concerns,which were based on economic grounds and focused on therole played by the EU in environmental protections and pol-icies in the UK and even in the world. Pure environmentalconcerns, i.e. environmental and ecological dimensions suchas climate change, global warming, the environmental conse-quences of fracking, and pollution were barely mentioned,and where they were mentioned, they were only used to sup-port this focus. In this case, Twitter communication showsasymmetrical information flow and sparse networks; and it isgeographically bound and has close connections to the Britishsocial context, reflecting the extension of British environmen-tal politics onto Twitter. These suggest the influence of social

9Introduction

contexts on, and the importance of, geographical proximity,in Twitter communication.

The book comprises eight chapters that are divided intothree parts. The first part includes two background chapters.Chapter 2 contextualises the study through discussions of therelationship between environmental issues and politics andbetween Brexit and the environment. Chapter 3 outlines theoverall media landscape within which Twitter resides andidentifies key debates in the fields of Twitter communicationand environmental communication on Twitter.

The second part has four chapters that address fouraspects of our findings. Chapter 4 depicts environmental dis-courses in tweets and explains the nature of the environmen-tal considerations in the discussions about the referendum onTwitter. Chapter 5 paints a picture of a divided and asym-metrical space where a fragmented elite club and transientcommunities formed, and where the domination of elitesocial actors in networks was enabled by the attention givento them by ordinary Twitter users. Chapter 6 presents asocial network analysis of the environmental data, in whichwe examined the attention-based ‘retweeting’ networks(egonets)9 surrounding key social actors (egos). Chapter 7discusses the tweeting practices and discursive arguments of15 categories of the 107 influential social actors identified inthe egonets in the social network analysis discussed inChapter 6.

The two chapters in the third part of the book discuss theinsights gained from this study as to the relationship betweensocial media and environmental politics (Chapter 8), as wellas reflections on social media research and our methodo-logical approach (Chapter 9).

9An egonet is a ‘network based on a particular individual’, which is anego (Scott, 2017: 74 and 84).

10 Tweeting the Environment #Brexit