Framing Through Names and Titles in German

Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 4924–4932 Marseille, 11–16 May 2020 c European Language Resources Association (ELRA), licensed under CC-BY-NC Doctor Who? Framing Through Names and Titles in German Esther van den Berg∗z, Katharina Korfhagey, Josef Ruppenhofer∗z, Michael Wiegand∗ and Katja Markert∗y ∗Leibniz ScienceCampus, Heidelberg/Mannheim, Germany yInstitute of Computational Linguistics, Heidelberg University, Germany zInstitute for German Language, Mannheim, Germany fvdberg|korfhage|[email protected] fruppenhofer|[email protected] Abstract Entity framing is the selection of aspects of an entity to promote a particular viewpoint towards that entity. We investigate entity framing of political figures through the use of names and titles in German online discourse, enhancing current research in entity framing through titling and naming that concentrates on English only. We collect tweets that mention prominent German politicians and annotate them for stance. We find that the formality of naming in these tweets correlates positively with their stance. This confirms sociolinguistic observations that naming and titling can have a status-indicating function and suggests that this function is dominant in German tweets mentioning political figures. We also find that this status-indicating function is much weaker in tweets from users that are politically left-leaning than in tweets by right-leaning users. This is in line with observations from moral psychology that left-leaning and right-leaning users assign different importance to maintaining social hierarchies. Keywords: framing, naming, Twitter, German, stance, sentiment, social media 1. Introduction An interesting language to contrast with English in terms of naming and titling is German. Speakers of German (both in To frame a topic is to “to select some aspects of a perceived Germany and Austria) are said to use titles more frequently reality and make them more salient in a communication than non-German speakers, but this may be an outdated text, in such a way as to promote a particular problem defi- stereotype (Besch, 1998). Left-wing student protests in the nition, causal interpretation, moral evaluation, and/or treat- 1960s in Germany destabilised many conventions, includ- ment recommendation” towards that topic (Entman, 1993, ing the formality of pronouns. Instead of using the polite p. 52). For example, a text may discuss the topic of im- pronoun Sie as the default among strangers, an increasing migration primarily in an economic frame that focuses on number of speakers treat informal du as the default, a con- the need for workers, or in a cultural frame that focuses on vention that is also associated with the political left (Gluck¨ issues of diversity and integration. Most work on framing and Sauer, 1997; Clyne et al., 2006; Besch, 1998). Dur- in computational linguistics has focused on the framing of ing the protests, this change in pronoun went hand-in-hand issues and events, rather than entities (Card et al., 2015; with a refusal to use academic titles (Besch, 1998). Fulgoni et al., 2016; Field et al., 2018). In this work, we investigate naming and titling in stance- In previous work (van den Berg et al., 2019), we introduced annotated German tweets that mention politicians with a entity framing as the presentation of an entity that (inten- doctoral degree. We collect and stance-annotate the Ger- tionally or not) promotes a particular viewpoint towards man Titling Twitter Corpus (GTTC)1, ensuring that the ti- that entity. Our goal is to understand how bias and stance tle Dr. is used in half of the tweets to have sufficient data to are expressed in computer-mediated discourse about politi- study titling effects. cal topics, in light of rising concern that discussions about We find that title use and naming formality correlate pos- politics on social media are less civil and objective than dis- itively with stance. We also that this association is much cussions on traditional platforms (Persily, 2017; Ott, 2017; weaker in tweets from users that have a left-wing politi- Dang-Xuan et al., 2013). cal orientation than tweets from likely right-leaning users, One area in which entities can be framed more or less posi- in line with observations by moral psychologists (Graham tively is in the use of names and titles. Sociolinguists iden- et al., 2009) who postulate differences in the importance tify two main functions of naming: the indication of the sta- of hierarchies in left-wing and right-wing value systems. tus of the target, and the indication of solidarity with the tar- To confirm this finding in a corpus with a natural title dis- get (Brown and Ford, 1961; Allerton, 1996; Dickey, 1997). tribution, we additionally construct the GTTC Addendum2 We found that the formality of naming forms correlates pos- without oversampling Dr.-containing tweets. itively with stance in English tweets mentioning politicians Our contributions therefore are: (van den Berg et al., 2019). This points to status-indication • a corpus of stance-annotated German tweets mention- being the more dominant naming function in such tweets. However, the positive correlation between stance and nam- 1Available here: https://doi:org/10:11588/data/ ing formality was found only for English and only for the AOSUY6. In compliance with Twitter usage guidelines, we pro- President professional title . It may be different in a differ- vide tweet ids rather than full tweets with their texts. ent language or for a different kind of title. 2Published along with the main GTTC. 4924 ing politicians with a doctoral degree a doctoral degree and can thus be called by an academic title. To meet the prominence criteria, a politician must be or • evidence for the status-indicating function of naming have been party chairman, Federal President, president of in a language other than English and for an academic the parliament or member of the parliament after 2013. Out degree instead of a professional title of the members of parliament with no additional function, • evidence that the status-indicating function of nam- we selected the top ten with the largest Twitter following. ing and titling is weaker in left-leaning than in right- We constructed queries with political keyword or party leaning discourse name disambiguators that were likely to return on-target results. The party names were: AfD, CDU, Bundnis¨ 2. Related Work 90/Die Grunen¨ (henceforth B90/Grun),¨ Die Linke, SPD and CSU. For the position of these parties on the polit- The framing of entities is a fairly new topic covered in only 3 a handful of papers (Card et al., 2016; Fan et al., 2019). ical spectrum we refer to Table 1. Political keyword The only currently existing dataset for entity framing is the disambiguators were selected by two of the authors who BASIL dataset (Fan et al., 2019), which annotates fram- are native speakers. These were: Bundestag (parliament), ing segments and their polarity towards political entities in MdB (Mitglied des Deutschen Bundestages i.e. member news articles. BASIL is not suitable for studying the im- of the German parliament), Kanzlerin (Chancellor), Bun- pact of naming and titling on entity framing, as journalistic destagsprasident¨ (president of the parliament), Regierung style guides prescribe certain naming conventions to ensure (government), Kabinett (cabinet), Ministerium (ministry) objectivity both in English (Siegal and Connolly, 1999) and and Minister (minister). in German (Raue, 2012). We therefore work with tweets. We did not create queries for mentions of politicians by More common than datasets for entity framing are datasets their first name, as it would be too time-consuming to find annotated for explicit stance. These are typically tagged relevant tweets among irrelevant ones mentioning common for stance towards products and companies (Somasundaran first names like ‘Thomas’ or ‘Peter’. In addition, first name and Wiebe, 2010; Meng et al., 2012; Jiang et al., 2011; Mo- references to politicians are rare in English tweets mention- hammad et al., 2016) where titling might play a lesser role. ing presidents (van den Berg et al., 2019). We expect naming by just the first name to be even more uncommon in the Datasets which do cover person entities typically include 4 them as a subset among other target types such as compa- current setting. nies, institutions and topics (Rosenthal et al., 2017; Amigo´ The four types of queries we used were: et al., 2012; Amigo´ et al., 2013; Amigo´ et al., 2014), with • #FIRST-NAME-LAST-NAME (e.g. #AngelaMerkel) the exception of Taddy (2013) which only has person entity targets. On these datasets, no studies were conducted on • FIRST-NAME LAST-NAME (e.g. Angela Merkel) the use of names and titles. • LAST-NAME PARTY (e.g. Merkel CDU) In previous work, we presented a dataset to examine the use of names and titles in English tweets mentioning pres- • LAST-NAME POLITICAL-KEYWORD (e.g. Merkel idents (van den Berg et al., 2019). We hypothesised that Bundestag) the relation between naming and stance would depend on which naming function was more dominant: (i) marking of We collected all tweets (excluding retweets) that matched relative status (based on e.g. age or professional role) or one of these four query types over 6 weeks in June/July 5 (ii) marking of relative solidarity, also referred to as inti- 2018. Because journalists are instructed not to use the Dr. macy or group membership (Brown and Ford, 1961; Aller- title unless topically relevant (Raue, 2012), we excluded ton, 1996; Dickey, 1997; Clyne et al., 2006). We found a tweets from a manually compiled list of 247 news accounts. positive correlation of 0.32 between stance and formality of We assigned tweets a naming form type, concentrating on naming, which indicates that the status-indicating function our title of interest and frequently occurring naming forms.

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