The role of emotion in Dutch online content about
climate mitigation policy support
Mandy Jaclyn Geluk 10552502
Master's Thesis
Graduate School of Communication: Political Communication Dr. L.C.N. Jacobs
Abstract
Prior research has found that affective as well as discrete emotions have a significant influence on public support for climate mitigation policies. Especially the use of negative and positive affect, and the use of anger and fear were proven most important in the context of climate change. This research extends this literature by exploring the use of these four types of emotions in online content of political parties in the Netherlands. Utilising a content
analysis of Facebook and Twitter posts, this study found that Dutch political parties do not make effective use of emotional appeals to gain public support for their policy initiatives. The results contribute to strategic theories of climate change communication and suggest that political parties should use the knowledge about these emotional appeals in their favour.
Introduction
Climate change is one of the most pressing issues facing the world in the 21st century. Unmitigated emissions of greenhouse gases are inclined to have permanent consequences, therefore substantial reductions are needed to minimise these impacts. Multiple studies show that there is a general lack of urgency in the perception of citizens about climate change (Roeser, 2012; Smith & Leiserowitz, 2014). A recurring explanation for this is the lack of personal, emotional involvement with the potential consequences of climate change (Roeser, 2012). Lorenzoni and Pidgeon (2006) argue that citizens feel like their own
contribution would be pointless and rather demand action on a political policy level. Demanding governmental action can be seen as the easy way out for citizens to mitigate climate change. However, it is imperative to keep in mind that his is only possible if the citizens themselves put this on the political agenda and show that they are supportive of mitigation policies that will also require personal sacrifices (Roeser, 2012).
Besides technological progress, the public plays a crucial role in the reduction of global emissions through energy use, social norms, and more importantly their support for climate and energy policies (Smith & Leiserowitz, 2014). According to Feldman & Hart (2018), public
support for regulation is a crucial factor in motivating political officials to act more decisively and try to reduce the risk of climate change by formulating mitigation policies. The role of political leaders and their political parties is equally important. These politicians have to form these mitigation policies and present this to the public in a way that lives up to the
expectations and satisfaction of the public and results in actual support. Even though the understanding of factors that determine the public opinion has become a core research area, Lu & Schuldt (2015) state that the role of emotions has received little consideration in this are of research. An improved recognition of the role of emotions will help us comprehend not only how climate change is perceived by individuals, but also understand interactions around climate change where emotions play an important role (Wang et al., 2018). This is why this research will focus on climate mitigation policies and how emotions are evoked by the way politicians frame these issues.
The extant knowledge about public perceptions regarding climate change has been growing considerably (Wang et al., 2018). The current body of research supports the argument that the public is aware of climate change and is worried. However, simultaneously, the same research also distinguishes a contradiction in risk perceptions and policy preferences. A study of Hagen, Middel and Pijawka (2016), focusing on all 28 Member States of the
European Union at the end of 2013, concludes that in the Netherlands most respondents (41 percent) stated some level of personal responsibility for climate change. Nonetheless,
previous research concludes that the majority of citizens are not willing to support mitigation policies that cause personal hardship, despite the fact that they recognise the effectiveness of these policies. Overall, prior findings show that the public strongly supports policy action on a national and international scale, but resist policies that directly affect themselves. This has been found to be a significant barrier for high levels of public support for climate change policies (Hagen, Middel & Pijawka, 2016). Roeser (2012) states that this barrier can be explained by the lack of personal emotional involvement with the possible effects. Weber (2006) explains that communication strategies about risks like climate change should
explicitly appeal to emotions, but in a discrete way. Meijnders et al. (2001) argue that this communication should appeal more directly to feelings like fear.
Multiple researchers have shown that emotions are important in risk perception and decision making (Sjöberg, 2007; Smith & Leiserowitz, 2014). Roeser (2012) argues that emotions are a fundamental source of reflection and awareness regarding the moral impact of climate change. This emotional engagement leads to a higher degree of motivation to mitigate these risks. She concludes that emotions might be the missing link in effective communication about climate change. Smith & Leiserowitz (2014) add that, besides affective emotions, discrete emotions in particular play a significant role in public support for climate change policy. Knowledge of how text can influence affective and discrete emotions and policy support can help improve communication strategies that try to effectively engage citizens with controversial risks like climate change. All former research on this topic has focused on the effect of evoking these emotions on public support for climate mitigation policy (Roeser, 2012; Smith & Leiserowitz, 2014; Lu & Schuldt, 2015). Less scholarly attention has been paid to the content that politicians and political parties use to persuade the public to vote for climate mitigation policies. Therefore, this study will focus on the online content of these political parties and their leaders in the Netherlands to determine if they evoke certain emotions and use this to their advantage to gain support of these policies.
The overall research question is as follows: “To what extent do Dutch political parties emphasise affective and discrete emotions in online content to engage the public on the issue of climate change to support climate mitigation policies?” By answering this research question, this study will contribute to the existing research on climate communication by providing a comprehensive analysis of the online content about climate mitigation policies of political parties in the Netherlands. This will allow us to formulate recommendations of how these parties might improve this content in the future with the use of the right affective and discrete emotions that will lead towards bigger support for these policies.
Theoretical Framework
Social Media and Politicisation of Climate Communication
Climate change is becoming an increasingly important issue for societies and policy makers worldwide, with many political institutions now campaigning for wide-ranging actions to mitigate the risks that come with this (Smith & Leiserowitz, 2014). Pidgeon (2012) states that on the policy side there is an increasing scientific certainty about the human causes of climate change, but simultaneously there is a growth of uncertainty framings, especially in climate mitigation decision making. He concludes that risk perceptions and political communication about climate change is crucial for future climate policy and decisions. According to Stieglitz and Dang-Xuan (2013), social media like Twitter and Facebook are increasingly used in political context, because they are believed to have the potential to boost political participation and democracy. Hong and Nadler (2011) conclude that politicians in modern democracies around the world have adopted social media to engage their
citizens, create direct dialogues and enable political discussion. Therefore, it is important to research how political parties communicate with the public through social media.
Affective & Discrete Emotions and Attitudes regarding Climate Change
"Affect refers to a person’s good or bad, positive or negative feelings about specific objects, ideas or images”(Leiserowitz, 2006, p. 46). This general feeling that someone assigns to a certain stimulus can have a powerful effect on how individuals react to risk information, because it influences both information processing and the motivation to act (Hart, Stedman & McComas, 2015). Affect and emotions are not mere epiphenomena, but often emerge before cognition and play an essential role in rational thought, risk perception and decision making (Sjöberg, 2007). .
Because climate change has been primarily discussed as a hazard to humans, wildlife and ecosystems (Hart & Feldman 2014), most of the previous research has focused on negative affect associated with the issue in general. However, further research shows that the focus
on climate mitigation projects can evoke both positive and negative associations (Myers et al., 2012; Spence & Pidgeon, 2010). Hart, Stedman & McComas (2015) have concluded that affective responses to climate change as a general problem did not influence individual decision making processes, but the same response to specific climate mitigation projects did. This may be because the issue of climate change is very complex and not easily linked to a single attempt to address and mitigate the issue (Ockwell, Whitmarsh & O'Neill, 2009). As this research focuses on climate mitigation policies, it is important to consider both positive and negative affect to evaluate the impact on decision making and public support for the particular policy.
Various studies suggest that beyond a general affective state, discrete emotions have an influence on how individuals process information and form their judgments like policy preferences (Nabi, 2003; Smith & Leiserowitz, 2014; Lu & Schuldt, 2015). Therefore, it is important to distinguish how discrete emotions differ from emotional affect. Discrete emotions are defined by Forgas as "intense and short lived with a definite cause and clear cognitive content". Previous research shows that discrete emotions are more complex than affect. As stated in the appraisal-tendency framework of Lerner and Keltner (2000, 2001), once an emotion is triggered, it can shape perceptions of events and guide behaviour in line with the central appraisal dimensions that is distinctive to the particular emotion. In this state of mind, when citizens are presented with the same information about the negative impacts and risks of climate change, they may formulate different conclusions regarding the need of support depending on their evoked emotion at the time of judgment. Regarding risk
perception, this framework suggests that fear and anger are particularly relevant, given the different appraisal themes and action tendencies. These discrete emotions evoke the same level of negative affect, but have unique appraisal patterns, motivational functions and behavioural associations (Nabi, 2002; Smith & Leiserowitz, 2014).
Smith & Leiserowitz (2014) conclude that discrete emotions alone are able to explain fifty percent of the variance in public global warming policy support. These types of emotions were the strongest predictors, even while controlling for other relevant factors such as ideology, political party or values. This finding supports the expectation that discrete emotions, besides affective emotions, play a critical role in public processing of risk information and decision making. Today, the big challenge for strategists is how to most effectively cue these powerful affective and discrete emotions to increase public
engagement with climate change policies that mitigate these risks.
Negative Emotions: Anger and Fear
The emotional reactions of individuals on content are relevant because of the potential effects they can have on their opinion about certain policy issues (Feldman & Hart, 2018). Different studies show that negative emotions have an important role in risk perception and decision-making. Schwarz et al. (1991) state that individuals process information more deliberately when they are in a negative affective state. Smith & Leiserowitz (2014) conclude that worry might promote a more productive and sustainable emotional engagement with the issue of climate change. Finucane (2000) adds that negative emotions are an important cause of risk perception because they drive individuals to search and process deeper information and make them more conscious of risks and the policies that can be used to confront them. These negative emotions can motivate a process of problem identification, analysis, option seeking and deliberative decision-making. In turn, this may result in more favourable attitudes towards climate mitigation propositions (Smith & Leiserowitz, 2014). Based on this research, we expect that political parties that try to introduce and create climate mitigation policies, the issue owners, will benefit from using these negative emotions as this will lead to more public support. Therefore, we hypothesise: (H1) In their use of emotional appeals in online content, parties that support climate policies will make more use of negative emotions than parties that do not support these policies. Especially issue owners are expected to make the most use of negative emotions.
Among these negative emotions, fear and anger are the strongest related to bigger support for climate mitigation policies (Smith & Leiserowitz, 2014). Lazarus (1991) defines, following the appraisal-tendency framework, emotions in terms of their own core relational theme. He defines anger as "a demeaning offense against me and mine". He states that anger is thought to increase the tendency to judge and punish the perceived offender of negative events. Smith & Leiserowitz (2014) conclude that anger has been found to be strongly associated with support for policy initiatives that are more vengeful or focused on retribution. Lu & Schuldt (2015) added that when participants were primed to be angry, they had
stronger behavioural intentions that led to punishing others. They concluded that feelings of anger increase the perception that fossil fuel industries are mainly responsible for the climate effects and thus promote support for policies that will hold these industries accountable for their actions. Based on prior results, it is expected that political parties with a focus on industry-targeted climate policies will benefit the most of using anger appeals, but also pro-climate mitigation parties will benefit of the use of these anger appeals. Therefore, we formulate the following hypotheses: (H2) In their use of emotional appeals in online content parties that support climate mitigation policies will make more use of anger than parties that do not support these policies; (H3) In their use of emotional appeals in online content, parties that focus the most on industry-targeted climate policies will make the most use of anger.
On the contrary to anger, we can conclude from multiple studies that fear does not have a unilateral effect on the individual support of climate mitigation policy (Meijnders et al., 2001; Smith & Leiserowitz, 2014). Fear is defined as "confronting an immediate, concrete and overwhelming physical danger” (Lazarus, 1991). Meijnders et al. (2001) research the interaction between emotions and the strength of arguments and conclude that more fear of climate change has a positive influence on information processing about energy-related behaviour of the public and climate mitigation policies. On the contrary, Smith & Leiserowitz (2014) state that fear is not associated with any increased climate policy support. They
conclude that although fear appeals have often been used to scare the public and try to engage them in the issues and create public support for policy change, the results can be counterproductive. This can be explained by the idea that when citizens are frightened by a threat that seems uncontrollable at an individual level, many of them disengage as a form of emotion-focused coping (Smith & Leiserowtiz, 2014). Moser (2007), however, establishes that fear can have a significant positive effect on attitudes regarding risk perception, but this is only the case in situations where individuals feel a personal risk. The belief that individual or collective action would be ineffective or impossible could also contribute to the limited success of fear appeals in climate communication. Many citizens see climate change as a distant threat, which makes it a challenge for communicators to increase the perception of risk (Smith & Leiserowitz, 2014). Because of these mixed results, we expect that political parties, whether they are for or against climate mitigation policy, will make use of fear appeals to gain public support for their point of view. Therefore we propose the following hypothesis: (H4) In their use of emotional appeals in online content, there will be no difference in the use of fear appeals between pro-climate mitigation parties, issue owners and anti-climate mitigation parties.
Positive Emotions
Apart from the influence of negative emotions on policy support, positive emotions can also have a significant positive effect on public support for climate mitigation policies (Smith & Leiserowitz, 2014). Hoijer (2010) states that hope and compassion are frequently used in the social construction of global warming to help individuals comprehend with climate impacts. She concludes that interest and hope can motivate citizens to learn more about certain risks and to make them support mitigation options. Truelove (2012) has found that positive affect and discrete emotions were more associated with support for wind energy than coal or nuclear power. Sjoberg (2007) has concluded that positive emotions like interest, hope and optimism were stronger predictors of attitudes toward nuclear waste repositories than negative emotions. He established that citizens are often motivated to feel hopeful and
interested in options to mitigate the threat even though risks are perceived as threatening. Based on these studies, we can conclude that feeling good about doing the right thing can be an important motivator of behaviour change and policy support. Therefore, we expect that pro-climate mitigation policy parties will make use of these positive appeals to gain support for their proposed policies. We hypothesise: (H5) In their use of emotional appeals in online content, pro-climate mitigation policies, especially issue owners, will make more use of positive emotion than anti-climate mitigation parties.
Data and Methods The Dutch context
For our analysis we will focus on the online content of Dutch political parties regarding climate mitigation policies. According to Vergeer and Hermans (2013, p.39) the Netherlands is an interesting case because it is a "worldwide frontrunner in terms of Twitter adoption”. They state that the country has over 7 million registered Twitter users and the highest ratio of active users in the world, making it more important for political parties to communicate with the public through these kind of channels. The Dutch Parliament involves 13 different political parties: 50Plus, Christen Democraten Appèl (CDA), ChristenUnie (CU), Democraten '66 (D66), DENK, Forum voor Democratie (FvD), GroenLinks (GL), Partij van de Arbeid (PvdA), Partij voor de Dieren (PvdD), Partij voor de Vrijheid (PVV), Staatkundig
Gereformeerde Partij (SGP), Socialistische Partij (SP) en Volkspartij voor Vrijheid en Democratie (VVD). To assess what the position of these parties is regarding climate
mitigation policies, we use a climate label (Figure 1) based on more than one hundred votes in the House of Representatives (Tweede Kamer) in the Netherlands (Klimaatlabel Politiek, 2019). We do have to mention that this label is based on previous votes in the House of Representatives and does not necessarily correspond with the online content about their support for climate mitigation policies that is published by the parties on their own channels. For instance, D66 seems to have low priority regarding the issue of climate change
ambitious climate agreement (2017). Denk, on the other hand, seem to score higher on the climate label, but they almost do not speak about this issue in their online content. By taking both this label and the online content into consideration, we can divide the Dutch political parties in three groups:
1. Issue owners: voters associate the issue of climate change with these parties. These are GL and PvdD.
2. Pro- climate mitigation parties: parties that (to some extent) support climate mitigation policies. These are SP, PvdA, Denk, 50Plus, CDA, D66, CU, SGP and VVD.
3. Anti- climate mitigation parties: parties that do not support climate mitigation policies. These are PVV and FvD.
Figure 1. A Climate Label of the Position of all Political Parties regarding climate mitigation policy.
Data Collection
The dataset for this analysis consists of Twitter and Facebook posts of all Dutch political parties during the period from the Paris Climate Agreement (COP21) on December 12, 2015 up until April 30, 2019, which were collected using the programs Coosto (Twitter) and
Paris Agreement required Dutch policy to become more strict in a brief period of time (Van Vuuren et al., 2017), which has led to more political debates and online statements about climate mitigation policies. The focus of the sample is on online Facebook and Twitter posts of all Dutch political parties that involve climate mitigation policies and which may have an influence on the Dutch citizens. This includes not only national policies, but also EU (European Union) policies. A post is defined as substantively relevant when this is either about the necessity of climate mitigation policies, who is responsible to solve this problem, or both. The latter can be the citizen, the big industries, the government or a combination of these actors.
Following the method of Hosch-Dayican et al. (2016) to collect relevant posts, we used a sampling technique following the logic of the snowball sampling method to gather relevant words. We started with a list of five words on topics of climate change, sustainability and (mitigation) policies. Next, we extracted other relevant words present in the mined posts, at which point we assigned a specific level of importance to each of those words based on the co-appearance with the previous words. When there is a co-occurrence of a word in more than 10 posts that are determined relevant, this tag is added to the list of words to collect posts on and it becomes a part of the set of words used to classify new relevant posts (Appendix A). This process permits to expand the list of words and allows us to recapture alternative words referring to climate mitigation policies as well as identify words that one would not naturally associate with this topic. Tags or posts that were falsely considered relevant, because they contained one of the relevant words but did not focus on the issue of climate change, were manually removed from the list of relevant posts. After this process, we formed a data set containing 880 online posts.
Operationalisation
To measure the use of affective and discrete emotion in the Facebook and Twitter posts we used the National Research Council (NRC) Emotion Lexicon, created by Saïf M. Mohammad and Peter D. Turney. This is a list of English words that was compiled by crowdsourced evaluations which distinguishes a set of affective emotions (positive, negative) as well as different discrete emotions (i.e. anger, fear). Subsequently, these words have been translated into forty different languages and have been proven stable, reliable and valid (Mohammad & Turney, 2013). Therefore, this Emotion Lexicon is applicable to the Dutch online content by political parties about climate mitigation policies that was gathered as part of this study. Although the NRC Emotion Lexicon contains eight discrete emotions (anger, anticipation, disgust, fear, joy, sadness, surprise, trust), for this article only two (anger and fear) were used. This is because most of the theory regarding the use of emotion in political communication about climate mitigation policy considers the two discrete emotions of anger and fear as most relevant (Smith & Leiserowitz, 2014).
Independent Variables
Dutch political parties. The independent variables in our research are the Dutch individual political parties (50Plus, CDA, CU, D66, DENK, FvD, GL, PvdA, PvdD, PVV, SGP, SP and VVD) as well as the three previously mentioned groups of parties:
1. Issue owners: the parties that voters see as best handling the issue on hand and, in this case climate change.
2. Pro-climate mitigation parties: the parties that are supportive of climate mitigation policies, but do not (always) see this issue as the most urgent problem to solve. 3. Anti-climate mitigation parties: the parties that deny that climate change is a problem
Dependent variables
Use of emotional appeals. The dependent variable in our research is the amount of
emotional appeals that the political parties used in their online content. Each relevant post of every individual political party was analysed by counting the number of words that evoke positive affect, negative affect, and fear or anger. Since there is a substantial variation between the number of posts of each individual party, a new variable was created for each emotion. The total number of words devoted to a particular affective or discrete emotion, divided by the total number of words in the post times one hundred results in a more standardised measurement of emotional words per post. These measurements are used to create a ratio. With the use of the ratio of each of these emotions, we can assess the extent to which a political party tried to evoke one of these emotions in their post.
Type of climate mitigation policy. In order to answer our third hypothesis (H3), we extracted and analysed the patterns of climate mitigation policies. We defined four variables, which we identify with a stepwise coding: (1) online posts about climate mitigation policies classifies the posts with respect to presence or absence of policy efforts and (2) type of mitigation policy classifies posts into three groups: those that emphasise the general need of mitigation policy, those that attribute the responsibility of climate change to the industry, and those that attribute the responsibility to the everyday citizen (see Figure 2). In case of combination, the individual post was identified as both attributing responsibility to industry as well as to citizens. Non-policy posts are those that do not belong to either of these categories, such as posts that are solely aimed to make citizens aware of the fact that climate change is an issue in need of political attention like information about the climate march.
Figure 2. An overview of the structure of the variables.
Analysis
First, we will analyse the descriptives to check which individual political parties and which group of parties covered the most online content about climate mitigation policies and which parties used the highest number of negative, angry, fearful and positive appeals.
Subsequently, a series of independent samples t-tests were conducted to check for significant differences between pro- and anti-climate mitigation parties. Finally, we conducted a number of multiple linear regression analyses with the dependent variables being Negative Ratio, Anger Ratio, Fear Ratio and Positive Ratio and the independent variables being the dummies for the three groups of political parties (issue owners, pro- and anti- climate mitigation parties).
Results
Pro- and Anti-Climate Mitigation Parties
Table 1 provides an overview of the 880 online Twitter and Facebook posts of all political parties in the Netherlands that were collected in the period of December 12, 2015 up until April 30, 2019. There are five parties that produced significantly more online content than the other parties: D66, FvD, GL, PvdD and PVV. To assess what their position is regarding
Online posts about climate mitigation
policy
Non-Policy Policy Posts Responsibility of the
industries
Responsibility of the citizens General Need for
climate mitigation policies, we used the climate label introduced in the methods section (Figure 2). The majority of the parties with the most online content are on the outer edges of the spectrum. They are either issue owners and thus very supportive of far-reaching
solutions to the issue of climate change (GL, PvdD), or denying the fact that climate change is a problem and therefore anti-climate mitigation parties (PVV, FvD).
Table 1. Overview of all online posts about climate mitigation policy
Party Facebook Twitter Total Percentage
50Plus 8 15 23 2.61% CDA 12 13 25 2.85% CU 4 7 11 1.25% D66 28 73 101 11.48% Denk 0 4 4 0.45% FvD 39 42 81 9.20% GL 48 169 217 24.66% PvdA 11 17 28 3.18% PvdD 55 185 240 27.27% PVV 26 42 68 7.73% SGP 1 2 3 0.34% SP 25 20 45 5.11% VVD 10 24 34 3.86% Total 267 613 880 100%
For each post, we did not only check how many words were associated with one of the particular emotions, but also made a new ratio variable for it. Subsequently, we calculated the means of each individual party, as shown in table 2. Based on these results, we can find which parties make more use of certain affective or discrete emotions and determine if this corresponds with our hypotheses. Noticeable from the complete analysis below is that all
political parties, except the PVV, use significantly more positive words in their online posts than negative, angry or fearful words.
Table 2. Mean emotion ratio of each individual party
Party Negative Ratio Anger Ratio Fear Ratio Positive Ratio
50Plus 1.65 1.27 1.05 8.39 CDA 2.10 1.24 1.23 6.55 CU 2.88 0.08 0.12 5.26 D66 2.15 1.80 1.44 6.13 Denk 2.94 0.92 0.00 5.13 FvD 2.16 1.36 1.46 5.20 GL 3.13 1.64 1.51 6.48 PvdA 3.23 2.22 2.58 5.28 PvdD 2.73 1.43 1.43 5.63 PVV 5.65 3.29 2.33 4.26 SGP 0.00 0.00 0.00 4.10 SP 3.40 2.43 1.36 7.24 VVD 1.77 1.50 1.41 8.24 Negative Ratio
With a mean score of M=5.65 the PVV scores highest on the Negative Ratio (table 2.). This means that, when analysing with the NRC Emotion Lexicon, the PVV used the most
negative words. This score is followed by the SP (M=3.40) and by the PvdA (M=3.23). To check if the differences in mean scores are statistically significant, we conducted an independent samples t-test. From the results we can conclude that there is no significant difference in the mean score of pro- climate mitigation parties (M=2.72, SD=4.50) and anti- climate mitigation parties (M=3.75, SD=6.89) with conditions t(174,683)=-1.749 and p=.082.
Subsequently, a multiple regression analysis was calculated to predict negative ratio (DV) based on the three different groups of parties (table 3). A significant regression model was found for negative ratio F(2,875)=3.581, p<.05. For this reason, the regression model could be used to predict the negative emotion ratio, but the strength of the prediction is extremely weak: 0.8 percent of the variation in negative emotion can be predicted on the basis of the three different groups of political parties (R2=.008). Only the group of anti- climate mitigation parties, b*=.014, t=2.673, p<.01, 95% CI [.004,.013] has a significant but weak positive association with negative emotion ratio, in comparison with our reference category, the pro-climate mitigation parties. This means that, in contrast to our expectations, the anti- pro-climate mitigation parties devote more words in their online posts that can be associated with negative emotions than the pro- climate mitigation parties. Based on these results we reject our first hypothesis that issue owners and pro-climate mitigation parties use more negative appeals than anti-climate mitigation parties.
Table 3. Regression models to predict Emotion Ratio
Parties (ref category = Pro- Parties) Negative Ratio* Anger Ratio Fear Ratio Positive Ratio* Issue Owners .005 -.002 .001 -.004 Anti- Parties .014** .005 .005 -.016** Constant .024 .017 .014 .064 R2 .008 .005 .003 .009 Adjusted R .006 .003 .001 .007 Note N=880. *p<.05. **p<.01. ***p<.001
Anger Ratio
For the emotion of anger, we anticipated not only that (H2) pro- climate mitigation policies, and especially issues owners, would make more use of anger appeals, but also that (H3) parties that focus the most on industry-targeted climate policies will make the most use of anger. Therefore, it is not only important to see which parties scored the highest mean score on the Anger Ratio, but also check which parties have the most online content with
mitigation policies that hold industries accountable. The results of this analysis can be found in table 4. We can see that the SP (M=86.67) scored significantly higher than VVD (M= 57.8) and 50Plus (M=43.48). Based on our theory we would expect that these parties will score the highest on the anger ratio, because this will have the strongest positive effect on citizens support for their proposed policy.
Table 4. Overview of online posts categorised per type of mitigation policy
Party General Policy Ratio Policy Industries Ratio Policy Citizens Ratio Non-Policy Ratio 50Plus 30,43 43,48 13,04 13,04 CDA 72,00 20,00 28,00 0,00 CU 54,55 9,09 9,09 27,27 D66 55,45 31,68 10,89 10,89 Denk 50,00 25,00 0,00 25,00 FvD 53,09 4,94 28,40 16,05 GL 35,94 41,01 9,68 14,75 PvdA 17,86 35,71 7,14 7,14 PvdD 35,83 42,92 16,67 10,83 PVV 26,47 2,94 50,00 22,06 SGP 100,00 0,0000 0,0000 0,00 SP 4,44 86,67 8,89 0,00
VVD 26,47 57,80 47,06 8,82
To check our expectations a multiple linear regression was conducted to predict anger ratio based on the individual political parties, with the mean party on anger ratio, the PvdD, as reference category (table 5). A significant regression model was found for anger ratio F(12,865)=1.932, p<.05. Therefore the regression model can be used to predict the anger emotion ratio, but the strength of this prediction is weak: 2,6 percent of variation in anger emotion can be predicted on the basis of the individual political parties (R2=.026). The parties that scored the highest on attributing responsibility to industries, SP, b*=-.002, t=-.206, p=.837, 95% CI [.010,.019] and VVD, b*=.001, t=.107, p=.915, 95% CI [-.012,.013] have no significant association with the anger emotion ratio. On the other hand, the PVV b*=.019, t=3.875, p<.001, 95% CI [.009,.028] shows a significant positive association with anger emotion ratio in comparison with our reference category. This shows that, in contrast to our expectation, a party that does not support climate mitigation policies devoted more words that evoke anger in their online posts than parties that support these policies. Therefore, we reject our third hypothesis (H3) that parties that focus most on industry-targeted climate policies will make the most use of anger appeals in their online content.
Table 5. Regression Model to predict Anger Ratio
Parties (ref categorie = PvdD) Anger Ratio*
50Plus -.002 CDA -.002 CU -.014 D66 .004 Denk -.005 FvD -.001
GL .002 PvdA .008 PVV .019*** SGP -.014 SP .010 VVD .001 Constant .014 R2 .026 Adjusted R .013 Note N=880. *p<.05. **<.01. ***p<.001
To test our second hypothesis, we have run an independent samples t-test followed by a multiple regression analysis. After running the independent samples t-test, we can conclude that there is no significant difference with conditions t(169,224)=-1.447 and p=0.150 between con-climate mitigation parties (M=2.24, SD=5.21) and pro-climate mitigation parties (M=1.60, SD=3.05). Subsequently, to predict anger ratio based on the three groups of parties, a multiple regression analysis was calculated (table 3). We found an insignificant regression equation for anger ratio F(2,875)=2.283, p=.103 and a R2 of .005. Also, the results for issue owners b*=-.002, t=-.680, p=.496, CI 95% [-.007,.003] and anti-climate mitigation parties, b*=.005, t=1.467, p=.143, CI 95% [-.002,.012], are very small and insignificant, which means they are negligible. Following these results we reject our second hypothesis (H2) that pro-climate mitigation parties and issue owners use more anger appeals in their online content than anti-climate mitigation parties.
Fear Ratio
In contrast to the former emotions, we expect the Fear Ratio not to be ranked on the basis of pro- and con-climate mitigation parties. This is because fear does not have a unilateral effect on citizens support for climate mitigation policy. Therefore, we did not anticipate a significant
difference between the two different groups of mitigation parties in their use of fear in online content. The results of the Fear Ratio are in line with these expectations, as we can see that the four parties that make the most use of angry words, are fifty percent pro- (PvdA and GL) and 50 percent anti-climate mitigation parties (PVV and FvD). With the use of an
independent sample t-test, we can check if there is a significant difference between these two groups. The results show that there is no significant difference in the mean score of pro-climate mitigation parties (M=1.44, SD=2.56) and con- pro-climate mitigation parties (M=1.86, SD=3.74) with conditions t(177,387)=-1.301 and p=.195.
After calculating the multiple regression analysis to predict Fear ratio based on the three groups of parties, we found an insignificant regression equation F(2,875)=1.432, p=.239 with a R2 of .003 (table 3). The regression model can therefore not be used to predict fear. Both the groups of issue owners b*=.001, t=.329, p=.743, CI 95% [-.004,.005] and anti-climate mitigation parties, b*=.005, t=1.619, p=.106, CI 95% [-.001,.010], have a very small positive effect on fear ratio compared to the pro-climate mitigation parties, but these effects are insignificant, which makes them negligible. Therefore, we accept our fourth hypothesis (H4) that there will be no difference between the three different groups of parties in their use of fear appeals in online content.
Positive Ratio
With a mean score of M=8.39, 50Plus scores highest on the Positive Ratio, closely followed by the VVD (M=8.24) and the SP (M=7.24). These results are in line with our hypothesis that pro-climate mitigation parties score higher in positive appeals than anti-climate mitigation parties. Even though the VVD did not score very high on the climate label, they prove to be an active pro-climate mitigation party in their online content. The results of the independent samples t-test show that there is a significant difference with conditions t(298,224)=3.527 and p<0.001 in the mean score of positive ratio of pro- climate mitigation parties (M=6.17, SD=5.95) and con-climate mitigation parties (M=4.77, SD=4.02).
Subsequently, a multiple regression analysis was calculated to predict positive ratio based on the three groups of parties (table 3). A significant regression equation was found for positive ratio F(2,875)=4.088, p=.017 (table 3), which means that the regression model can be used to predict positive emotion, but the strength of the prediction is weak: 0.9 percent of the variation in positive emotion can be predicted on the basis of the three different groups of political parties (R2=.009). Also, the group of anti-climate mitigation parties, b*=-.016, t=-2.802, p<.01, CI 95% [-.028,-.005], showed a small significant negative effect on our
dependent variable in comparison with the pro-climate mitigation parties. This means that, in line with our expectations, pro-climate mitigation parties make more use of positive appeals than anti-climate mitigation parties. Therefore, we accept our fifth and last hypothesis (H5) that pro-climate mitigation parties will make more use of positive appeals in their online content.
Conclusion and Discussion
This study explored the role of emotion in online content of Dutch political parties regarding climate mitigation policies. Previous research has documented the importance of affective and discrete emotions and the role they play in climate mitigation policy support (Smith & Leiserowitz, 2014; Lu & Schuldt, 2015). This research found that even though the use of these kinds of emotions are proven effective, Dutch political parties do not make effective use of this in their online content. Furthermore, we have seen that parties that are not supportive of climate mitigation policies made the most use of emotions that are proven to create more policy support, and thus go against their intentions. We believe that if these parties use the aforementioned scientific results in their favour, it would help them effectively gain support for their proposed pro- or anti-climate mitigation policies.
For the results of the specific emotions, we have found that there is no significant difference between the two different groups of parties regarding the use of negative emotions and
anger, resulting in a rejection of our first two hypotheses. These hypotheses claimed that pro-climate mitigation parties, and especially issue owners, would make more use of negative and angry emotions because these negative emotions can motivate a process of problem identification, analysis, and deliberative decision making which will lead to more favourable attitudes towards climate mitigation policies (Finucane, 2000; Smith &
Leiserowitz, 2014). In the case of political parties in the Netherlands, we can conclude that, overall, they do not use many negative or angry emotional appeals in their online content. We have seen that social media are becoming an increasingly important instrument because they have the potential to boost political participation and democracy by engaging their citizens and creating political discussion (Hong and Nadler, 2011). For this reason, we expect that when political parties make more use of these affective and discrete emotions in their online content, this will help with engaging the public and create bigger support for climate mitigation policies.
For positive emotion we have found a significant effect and concluded that positive appeals are more used by pro-climate mitigation parties. Based on the expectation that feeling good about doing the right thing can be an important motivator of behaviour change and policy support, we can conclude that the Dutch pro-climate mitigation parties make effective use of positive emotions in their online content. This is in line with our theoretical claim that climate communication should not only focus on negative emotions, but also need take positive emotions into account.
Some limitations need to be acknowledged. First, we have seen in our results that the majority of effects are not significant, which raises doubts about the generalisability of our findings. This can be explained by the limited amount of online content about the issue of climate change for certain political parties in the Netherlands. Second, it is important to note that the context of the online post can be very relevant. There are multiple credible
emotion lexicon, but that they were not meant to be angry at all. Also, we have seen that con- climate mitigation parties like the PVV might use the same words but with the intent to stop citizens from supporting climate mitigation policies. Analysing the context of the online content was beyond the scope of this research, but can be a good starting point for future research. Third, it is essential to discuss the quantity of online content and the differences between the individual political parties. We have seen that the PvdD has a total of 214 posts while the SGP only has 3. This difference makes it more difficult to draw definitive
conclusions about the individual parties. The 3 online posts of the SGP may be analysed as more emotional than the PvdD because their number of neutral tweets evens it out.
Future research should not only seek words in online content that are associated with a particular emotion, but give more attention to the context of the online post. Also, a great number online posts contained references to web pages, pictures and videos. These links may contain more emotional text, but also contain imagery that could have an emotional effect and result in more or less public support for the proposed policies. This could be a good starting point to expand and deepen our current research.
Appendix A. List of Words used for the data collection
The words featured in the initial list were as follows: klimaat, klimaatverandering,
klimaatbeleid, milieu and duurzaamheid. During our data collection the following words were added: klimaatakkoord, klimaatdebat, energie, CO2, opwarming and Parijs
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