Communicating Climate Change: A Comparison Between Tweets and Speeches by German Members of Parliament
Year: 2023

Abstract:
Twitter and parliamentary speeches are very different communication channels, but many members of parliament (MPs) make use of both. Focusing on the topic of climate change, we undertake a comparative analysis of speeches and tweets uttered by MPs in Germany in a recent six-year period. By keyword/hashtag analyses and topic modeling, we find substantial differences along party lines, with left-leaning parties discussing climate change through a crisis frame, while liberal and conservative parties try to address climate change through the lens of climate-friendly technology and practices. Only the AfD denies the need to adopt climate change mitigating measures, demeaning those concerned about a deteriorating climate as climate cult or fanatics. Our analysis reveals that climate change communication does not differ substantially between Twitter and parliamentary speeches, but across the political spectrum.

heading: Future Work
text: g fridays for future instead of #fridaysforfuture. As the Open Discourse data set only covers months until May 2021, we manually collected the relevant speech transcripts of the missing months. Our final corpus consists of 5,351 speeches, including speeches of MPs and of ministers (most of whom are also MPs). First experiments revealed that the transcripts contain substantial noise, i. e. , material that is not related to CC. To improve results, we separated the speeches into paragraphs and filtered them with the same set of keywords. For our analyses, we treat the paragraphs as individual documents (n: 13,794; average number of tokens: 69). While paragraphs and tweets are still of rather different lengths (69 vs. 32 tokens, on average), using paragraphs allows for a more meaningful comparison than using the full speeches. 4 Analyses
For determining the relevance of the CC issue, and the stances toward it, we first analyse our data statistically, looking at speech and tweet volumes, frequencies of hashtags, and use of subjective "climateX" noun compounds (e. g. , Klimapolitik, Klimaschutz, Klimakrise). We are interested in differences between parties, and between tweets and speeches. We present absolute counts and proportions, where proportions in Section 4. 1 are calculated with respect to corpus sizes, while in the other two sections, proportions for hashtags and compounds are calculated with respect to total hashtag/compound counts by party. 4. 1 Volume of CC Communication
Both CC corpora show a rather skewed party distribution (see also Appendix 7). Of the 30,242 tweets on CC, 13,509 (45%) were tweeted by the Greens, followed by the FDP (4,057; 13%) and the SPD (4,023; 13%). The smallest amount of tweets has been contributed by the AfD (2,208; 7%). A different pattern can be observed in the speeches on CC. As the amount of speeches roughly correlates with a party’s representation in parliament, the CDU/CSU, who had most seats in legislative periods 18 and 19, held most speeches (1,446; 28%), with the SPD ranking second (1,180, 22%). Note, however, that the size of the parliamentary group can only serve as a proxy. Other factors such as political orientation have an influence, which is arguably why the Greens have a similar proportion of speeches (20%) as the SPD. The least number of speeches were given by the FDP (416; 8%). 4. 2 Hashtags
Being a social media convention, hashtags are not used in speeches, and thus we entirely focus on the twitter corpus in this section. 3 Results are given in Table 2. While substantial usage of hashtags is indicated by the absolute counts, proportions appear comparatively low. This is caused by the overall large amount of individual hashtags that are being used. All parties except the AfD most often use the tag #climateprotection (#klimaschutz, 1% (AfD) - 10% (SPD)). The Left and, especially, the Greens make use of the term #climatecrisis (#klimakrise, 2% (The Left), 4% (The Greens)), while it is not among the top hashtags of the other parties. In addition, the Left tweets on #climatejustice (#klimagerechtigkeit, 2%), on #transporttransition (#verkehrswende, 3%), and is the only party mentioning the anti-coal activist movement Ende Gelände (#endegelaende, 2%). While mentioning transport as well (2%), the Greens also use #energytransition (#energiewende, 2%) and #coalphaseout (#kohleausstieg, 2%). Being in the government for the whole timeframe, the social democratic SPD frequently uses #climateprotectionlaw (#klimaschutzgesetz, 2%) and #climatechange (#klimawandel, 2%), a term that is rather avoided by the Greens (in favor of crisis). All the
3Note that we will subsequently mention the translated hashtags where appropriate in addition to the original German ones. The Left The Greens SPD Hashtag C | P Hashtag C | P Hashtag C | P
#klimaschutz 351 |. 04 #klimaschutz 2,751 |. 09 #klimaschutz 624 |. 10 #fridaysforfuture 238 |. 03 #klimakrise 1276 |. 04 #energiewende 261 |. 04 #verkehrswende 206 |. 03 #verkehrswende 736 |. 02 #spd 170 |. 03 #klimawandel 168 |. 02 #kohleausstieg 608 |. 02 #klimaschutzgesetz 144 |. 02 #klimagerechtigkeit 148 |. 02 #energiewende 497 |. 02 #klimawandel 116 |. 02 #klimakrise 146 |. 02 #klima 429 |. 01 #fridaysforfuture 89 |. 01
more left-leaning parties mention #fridaysforfuture (1% (The Greens; SPD) – 3% (The Left)). The liberal FDP emphasizes economic interests with #energytransition (#energiewende, 3%), #emissiontrading (#emissionshandel, 2%), #coalphaseout (#kohleausstieg, 2%), and #servicesecurity (#versorgungssicherheit, 1%). The conservative CDU/CSU is the only party mentioning #hydrogen (#wasserstoff, 1%) and #sustainability (#nachhaltigkeit, 1%), in addition to #climate (#klima, 2%) and #climatechange (#klimawandel, 2%). The rightwing AfD uses #afd (9%) as their top hashtag and is the only party using the term #climatehysteria (#klimahysterie, 2%). Neither of the more right-leaning parties has Fridays For Future or any other social movement among their top hashtags. In contrast, they often apply #co2 (2% (CDU/CSU; AfD) – 4% (FDP)). 4. 3 Stance-Conveying Climate Compounds For the climate compound analysis we use a slightly modified version of the "climate change discourse glossary", which consists of 249 polit-
ically charged German noun compounds starting with climate (Klima), i. e. , compounds whose use indicates the author’s stance towards the issue. 4 For matching glossary entries against terms, hyphenated and non-hyphenated variants are treated as the same compound. Results are shown in Table 3. Tweets: We decided to keep the hashtags when matching the tweets, which results in some overlap with the previous analysis. This, however, allows us to show a more complete picture on the usage of climate compounds. 5 In contrast to hashtag use, only a small number of climate compound types are used frequently by the parties, and we concentrate on those here. 4The glossary of noun-noun compounds is available here: http://www. klimadiskurs. info. As one exception to the N-N pattern, we add the N-Adj compound climate-friendly (klimafreundlich) to our study, as it also conveys a stance toward CC. 5Not all climate compounds used as hashtags are represented in the glossary, e. g. , Klimaschutz (‘climate protection’), a term that neutrally describes actions. The Left (Tweet) The Greens (Tweet) SPD (Tweet) Compound C | P Compound C | P Compound C | P
klimagerechtigkeit 235 |. 44 klimakrise 1,951 |. 77 klimafreundlich 109 |. 43 klimakrise 208 |. 39 klimafreundlich 164 |. 06 klimakrise 88 |. 35
klimafreundlich 25 |. 05 klimagerechtigkeit 157 |. 06 klimaleugner 10 |. 04 klimaaktivist 18 |. 03 klimakanzler 54 |. 02 klimakanzler 8 |. 03 klimakanzler 7 |. 01 klimakanzlerin 37 |. 01 klimagerechtigkeit 6 |. 02
FDP (Tweet) CDU/CSU (Tweet) AfD (Tweet) Compound C | P Compound C | P Compound C | P
klimafreundlich 75 |. 43 klimafreundlich 90 |. 52 klimahysterie 193 |. 34 klimakrise 27 |. 16 klimakrise 23 |. 13 klimawahn 70 |. 12
klimaaktivist 13 |. 08 klimakonsens 11 |. 06 klimakrise 30 |. 05 klimakonsens 7 |. 04 klimaaktivist 10 |. 06 klimaaktivist 27 |. 05 klimanationalismus 6 |. 03 klimakompetenz 7 |. 04 klimakult 26 |. 05
The Left (Speech) The Greens (Speech) SPD (Speech) Compound C | P Compound C | P Compound C | P
klimakrise 54 |. 35 klimakrise 403 |. 84 klimafreundlich 68 |. 56 klimagerechtigkeit 31 |. 20 klimafreundlich 31 |. 06 klimakrise 34 |. 28
klimafreundlich 30 |. 19 klimaleugner 7 |. 01 klimakonsens 4 |. 03 klimapäckchen 11 |. 07 klimazerstörung 6 |. 01 klimaleugner 4 |. 03 klimaleugner 7 |. 04 klimapäcken 5 |. 01 klimaaktivist 2 |. 02
FDP (Speech) CDU/CSU (Speech) AfD (Speech) Compound C | P Compound C | P Compound C | P
klimafreundlich 11 |. 28 klimafreundlich 120 |. 79 klimahysterie 35 |. 19 klimakonsens 9 |. 23 klimakrise 17 |. 11 klimareligion 16 |. 09 klimanationalismus 4 |. 10 klimaleugner 4 |. 03 klimarettung 15 |. 08 klimakrise 4 |. 10 klimakonsens 2 |. 01 klimaleugner 14 |. 08
klimakanzler 2 |. 05 klimakompetenz 1 |. 01 klimakrise 13 |. 07
Table 3: Compound Counts (C) and Proportions (P) By Party (Top: Tweet Corpus; Bottom: Speech Corpus). First, climate crisis (Klimakrise) is used by all parties, though with very different frequency (5% (AfD) - 77% (The Greens)), which contrasts with the respective hashtag which is only used by the the Left and the Greens. Climate-friendly (klimafreundlich) is applied by all parties except the AfD (5% (The Left) - 52% (CDU/CSU)). Other frequently used compounds include climate justice (Klimagerechtigkeit by the Left (44%) and the Greens (6%)) and climate activist (Klimaaktivist). Importantly, the AfD exclusively uses a number of compounds that clearly convey negative judgement on the activist movement, including climate hysteria (Klimahysterie, 34%), climate delusion (Klimawahn, 12%), and climate cult (Klimakult, 5%). Speeches: Some of the patterns found in tweets re-emerge in the speeches. First, climate crisis is used by all parties, with proportions ranging from 7% to 84%, the highest of which is associated with the Greens. Second, climate-friendly is mentioned by all parties except the AfD (6% (The Greens) - 79% (CDU/CSU)). Third, climate justice is frequently used by the Left (20%), and the AfD often applies climate hysteria (19%). Contrasting with the tweet data, however, the FDP makes more notable usage of climate consensus (Klimakonsensus, 23%), while the AfD uses climate religion (Klimareligion, 9%), climate rescue (Klimarettung, 8%), and climate denier (Klimaleugner, 8%). 5 Topic Modeling
For topic modeling we use BERTopic (Grootendorst, 2022), a technique based on transformers (Devlin et al. , 2019), clustering and a class-based variation of TF-IDF, i. e. c-TF-IDF. The BERTopic algorithm works as follows:
In a first step, documents are embedded in order to create vector space representations that can be used for semantic clustering. Here, we use Sentence-BERT (SBERT) (Reimers and Gurevych, 2019). Second, the dimensionality of embeddings is reduced using UMAP (McInnes et al. , 2020), after which documents are clustered using HDBSCAN (McInnes et al. , 2017), a hierarchical version of the original DBSCAN algorithm. This allows for soft clustering as unrelated documents are treated as noise. Third, topic representations are created using c-TF-IDF. We use the same hyperparameters for both the tweet and speech datasets. We remove frequent words 1) by applying BM-25 weigthing and 2) by taking the square root of the term frequency after normalizing the frequency matrix instead of taking the default term frequency in the c-TF-IDF algorithm. Both options can be set in BERTopic. We use the SBERT model all-mpnet-base-v2. The
minimum cluster size of HDBSCAN is set to 15. Finally, we apply the Maximal Marginal Relevance algorithm (Carbonell and Goldstein, 1998) to diversify the topic representation, using a mild score of 0. 2 (with 0=no diversity, 1=complete diversity). We train topic models on the full tweet and speech corpora and on individual party subsets, resulting in 14 topic models. In the following, we will describe the topics provided by the trained models. Due to space limitations we will concentrate on the topics that were associated with most documents and were interpretable from a climate (policy) perspective. The topic labels, which were manually derived from the models, can be found in Table 4 for the full tweet and speech corpora, and in Table 5 for individual party subsets. See Appendix C for the actual topic representations. 5. 1 Topics in Tweets
The topic model trained on the full tweet corpus obtains a diverse set of CC-related topics (see Table 4; top). The most prominent topic (T1) focuses on emissions and their trading and reduction, followed by a topic about transport infrastructure (T2). Other topics concentrate on climate crisis and justice (T4), energy transition (T5), climate protection (T7) and innovation and technology (T9). Another one deals with the EU and multilateralism (T6). With respect to the models trained on party subsets, there are several similarities and differences to be observed (see Table 5; top). First, all parties talk about CO2 emissions. However, certain differences can be observed. While the Left mainly discusses emission reduction, the other parties except the AfD also focus on pricing. The liberal FDP mentions emission trading. Second, all parties except the AfD are associated with date topics. These topics are characterized by the mention of several years, which are connected to certain climate goals. Third, the Left, the Greens and the SPD have topics related to transport transition and infrastructure among their most common topics (The Left: T6; The Greens: T2; SPD: T2), while the other parties do not. Fourth, most parties have a topic concentrating on energy transition and renewables6. The AfD is the only party mentioning nuclear energy and having their own topic on wind energy. Fifth, both FDP and CDU/CSU have innovation/technology
6Note that while not among the top topics, the Greens had two topics on energy: 1. energy transition; 2. fossil fuels. topics. Finally, some topics are attached to individual parties. The Greens provide a topic concerning the EU and green(washing). The SPD has a topic mentioning EU and EU council presidency. Industry is a topic exclusive to the CDU/CSU and the AfD is the only party talking about climate hysteria and socialism. 5. 2 Topics in Speeches
The topic model trained on the full speech corpus produces similar topics as the one trained on tweet data, e. g emissions trading and reduction (T1), energy transition (T6) and transport infrastructure (T7) (see Topic 4; bottom). However, new topics include investment and finances (T2) and politics, democracy and human rights (T8). Furthermore, the EU topic has a special focus on security and NATO, while the transport infrastructure topic (T7) is less dominant. The general trend found in the tweet data, can be identified as well in the party subsets of the speech data (see Table 5; bottom, labelled (Speech)). Again, all parties concern themselves with CO2 emissions and/or energy transition, the latter of
which, however, appears now to be a more dominant topic for left-leaning parties. Furthermore, the AfD has speech topics revolving around climate religion and dogma, which are semantically similar to climate hysteria. Still, some minor differences can be found. For instance, transport was represented in the most common tweet topics of the Greens and SPD, which is not the case for the respective speech subsets. Except for the Greens, no topics on years/goals can be found. Finally, the CDU/CSU has stronger focus on e-mobility and transport in general than in the tweet corpus