In this paper, we undertake an attempt to characterize the world view of what are called Wutburger in Germany, that is citizen who are enraged by the current political and social situation. In order to nd out what makes a Wutburger a Wutburger, we analyze Facebook posts on the basis of a lexical resource where nouns, adjectives and verbs are classi ed according to Plutchik's primary emotions. We also introduce new polar roles of verbs that help to identify the writer perspective. This way, we are able to identify targets and the Wutburger's stance towards them. As textual data, we utilize about 100,000 Facebook posts of a German right-wing party whose members are obvious exemplars of the notion of a Wutburger.
In a number of western societies populism has (re)entered the scene and especially rampages in the social media. Hate speech, shit storms etc. are extreme forms of such undemocratic tendencies. In Germany, the notion of a Wutburger has been coined, that is, citizen who are disappointed by the government and the social situation and their (verbal) behavior seem to be driven by rage (German Wut ). A new, right-wing party evolved, the AfD (Alternative fur Deutschland). We have access to about 100,000 Facebook posts of the AfD including reader comments that mostly stem from AfD proponents - who clearly form a subset of German Wutburger. Our research question was: Can we nd out, how a Wutburger perceives the world and what, after all, is the objective of his Wut.
A rst step towards this goal is to measure the emotional ngerprint of the
texts produced by Wutburger and compare it to the ngerprint of a related text
genre. We use the Tubinger (German newspaper) Treebank (TuBa-D/Z) [
The emotional ngerprint does not tell us anything about the world view of a Wutburger: who are his proponents and who are his opponents? We would like to exploit the idea that the identi cation of these targets can be supported and accomplished by a more ne-grained classi cation of lexical items. We not only assign primary emotions to words (verbs, nouns and adjectives), we also identify those words that have an implicit writer perspective and explicate which one it is. For instance, the adjective ine able in a phrase like the ine able chancellor expresses that the writer has a negative attitude towards the referent of the noun and a sentence like Merkel jerks the German citizen around allows to infer that the writer believes that the referent at subject position is an immoral actor, a cheater one might say. Also, the direct object is perceived as a victim of the cheater. Since the writer is against the cheater, he is in favor of the victim. 2
Starting with the freely available lexicons described in [
verb adj noun pos emotion neg emotion pos moral neg moral pos factual neg factual # 49 119 15 71 553 1170 1977 118 182 286 569 1010 1103 3268 91 134 104 436 663 1229 2657
The columns for factual denote words that are positive or negative without reference to either (a particular) emotion or moral. We could say that they are positive or negative on a factual level. For instance to sicken, recover, congratulate are examples of such verbs, whereas mistake, disease, transparency, security, right, wrong are examples for such nouns and adjectives. This is a crucial distinction: such words do not indicate a writer perspective, but the contribute to polarity decisions, nevertheless.
We took the 254 verbs classi ed as either belonging to the emotion or moral
dimension as a basis for further annotations. We identi ed 58 verbs with a very
strong writer perspective either on the actor or the experiencer role or on both
(to cheat, to jerk sb around ). We then coined for the six verb classes derived that
way special role labels. The set of agent roles is: prole, baiter, hater, torturer,
hypocrite, choleric. Experiencer roles are su erer and victim. To give an
example: the verb are up (aufbrausen) bears the emotion anger and the semantic
1 http://bics.sentimental.li/ les/8614/2462/8150/german.lex
2 https://pub.cl.uzh.ch/projects/opinion/lrec data.txt
role of the subject is that of choleric. Our hypothesis is that these roles better
capture the writer perspective, since they express how the writer conceptualizes
these referents. Note that we assign these roles to subcategorization frames, not
to verbs. We speci ed these verbs along the line proposed by [
Our present endeavor is basically one that exploits an existing, but carefully re ned and augmented lexical resource. Especially our new verb classes with a new kind of polar roles are meant to make the writer perspective more nuanced. We are at the very beginning of a sophisticated study. At the moment, however, there is no gold standard and thus no machine learning involved.
We found 9,012 verb types in the Facebook posts, which gave us altogether 419,034 verb tokens in 303,563 sentences (word tokens altogether: 5,249,613). The 1052 verb types of our lexicon found in the data (61 verbs did not occur), amounts to 83,658 verb tokens, which is - taking into account that one sentence might have more than one model verb - about 25% coverage (a model verb in each 4th sentence). Our verb resource seems to have a good coverage, thus. If we just look at moral verbs, we get 11,153 hits, 64 of the 85 moral verb types do occur in the posts. The 168 emotional verbs occur with a frequency of 17,102.
In order to quantify the emotional load of the Facebook posts, we used the Tubinger Treebank (TuBa-D/Z) as a reference corpus. The TuBa-D/Z comprises 95,595 sentences. The coverage of our verb resource is again quite good: we found 930 verb types with 22,679 verb tokens, which is a coverage of 23.79% (again almost each 4th sentence bears a model verb). 4
We use our emotion lexicon in order to diagnose the emotional load of the AfD posts. In Fig. 2, we compare the AfD posts and the newspaper text wrt. to the emotions present. We determined the frequency of words belonging to a particular emotion and normalized by the total number of emotion words (found in the posts) (left hand side), and by the number of sentences (right hand side), respectively.
As we can see from Fig. 2, fear is the most prominent emotion of a Wutburger and not anger (a prestage of rage) while at the same time sadness is not a prevalent emotion of a Wutburger. All other emotions are almost identically distributed in both, AfD posts and newspaper text. Our expectation, namely that the AfD posts would have a higher emotional load than news texts, was not con rmed. We also had a look at the moral dimension. The TuBa-D/Z refers to 7490 nouns and adjectives classi ed as positive (32%) or negative (60%) from a moral perspective, i.e. 7.8% of the sentences refer to that dimension. In the AfD texts, 32167 tokens were found, which is about 10.6% (73% negative, 27% positive). This clearly shows that (negative) moral argumentation is a central attitude of a Wutburger. If we have a look at verbs the picture is similar: 1.6% of the news texts contain a moralizing verb, whereas 2.3% of the AfD posts do so. 5
A polar role is the label for the logical subject (agent) or object (theme, patient, experiencer) that indicates the positive or negative role its ller plays. The inventory of polar roles is not xed, yet. We have de ned a couple of ne-grained polar roles that are meant to indicate a more nuanced writer perspective. These roles are baiter, hater, choleric, hypocrite, prole, torturer and su erer, victim. The de nition of these roles is straightforward: we just had to x the corresponding verbs and determine which semantic role bears which polar role. Take the polar role prole. There is a number of verbs in German (we have identi ed 18) that indicate that the writer implicitly classi es the agent of such a verb as a prole (anlabern (to chat so up), anpobeln (to accost sb)). Thus, the agents of such verbs are negative targets from the point of view of the writer. It turns out that in the AfD texts journalists, do-gooder, politicians, asylum seekers, the print media are, among others, conceptualized as proles.
In order to derive these writer perspectives, we have parsed the AfD posts
with a dependency parser [
An interesting nding concerns the role of the AfD (i.e. Wutburger) itself. If we look at those who are hated, we get: Arabs, strangers, Merkel, Muslims, comrads but also Germany and the AfD. A closer inspections reveals that the Wutburger do not disguise or veil their rage. They use verbs with AfD (or I or we) as agents that indicate that they are haters.
Our verb resource also allows for more sophisticated inferences. We have coined the notion of a violator of morality for the following set of actors: the set of actors of a verb that casts a negative e ect on its object which is - according to the polarity lexicon - positive:
X:(9Verb, Y: subj(Verb,X)^e ect(Verb,obj,neg)^obj(V erb; Y )^polarity(Y; pos))
An example is Merkel destroys the security of Germany where security is positive and destroy casts a negative e ect on the direct object (obj)
If we, however, change polarity(Y,pos) to polarity(Y,neg) than we get a strong proponent of the AfD: to disapprove something negative is positive. 6
One topic of this paper is lexicon-based, document-level emotion detection. For
an overview of similar approaches see e.g. [
The role verbs play in sentiment analysis and stance detection has received
increased attention over the last years, cf. [
We have introduced two new resources for German: a ne-grained verb resource with polar roles that reveal the writer perspective, and an emotion lexicon where words are classi ed as one of eight primary emotions. Also words related to moral are speci ed. We used this in order to nd out whether Wutburger texts do have a clear emotional ngerprint compared to news texts. We found that fear (and not Wut, i.e. rage) is the prevalent emotion and that Wutburger signi cantly more often argue on the basis of moral than a reference newspaper corpus. Another insight is that Wutburger do not hide their rage (in their own sentences they often occupy negative polar roles such as hater). More sophisticated search pattern on the basis of top down and bottom up restrictions give rise to interesting inferences (someone who disapproves something positive is a violator of morality). All this is meant as a rst explorative study: is our lexicon large enough to be useful, is our ne-grained verb resource broadly applicable. A fuller answer to the question raised in the title must await a thorough empirical investigation.