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Verfasst von:Berg, Esther van den [VerfasserIn]   i
 Korfhage, Katharina [VerfasserIn]   i
 Ruppenhofer, Josef [VerfasserIn]   i
 Wiegand, Michael [VerfasserIn]   i
 Markert, Katja [VerfasserIn]   i
Titel:Doctor Who?
Titelzusatz:Framing through names and titles in German
Verf.angabe:Esther van den Berg, Katharina Korfhage, Josef Ruppenhofer, Michael Wiegand and Katja Markert
Jahr:2020
Umfang:9 S.
Fussnoten:Gesehen am 05.01.2021
Titel Quelle:Enthalten in: International Conference on Language Resources and Evaluation (12. : 2020 : Marseille)LREC 2020 Marseille
Ort Quelle:Paris : The European Language Resources Association (ELRA), 2020
Jahr Quelle:2020
Band/Heft Quelle:(2020), Seite 4924-4932
ISBN Quelle:9791095546344
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.
URL:Bibliographic entry. University members only receive access to full-texts for open access or licensed publications.

Volltext: https://www.aclweb.org/anthology/2020.lrec-1.606
Datenträger:Online-Ressource
Sprache:eng
Bibliogr. Hinweis:Forschungsdaten: Berg, Esther van den: German Twitter Titling Corpus
K10plus-PPN:1743900244
Verknüpfungen:→ Source

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