Abstract
Research from communication science has shown that stereotypical ideas are often reflected in language use. Media coverage of different groups in society influences the perception people have about these groups and even increases distrust and polarization among different groups. Investigating the forms of (especially subtle) stereotyping can raise awareness to journalists and help prevent reinforcing oppositions between groups in society. Conducting large-scale, deep investigations to determine whether we are faced with stereotyping is time-consuming and costly. We propose to tackle this challenges through the means of microportraits: an impression of a target group or individual conveyed in a single text. We introduce the first system implementation for Dutch and show that microportraits allow social scientists to explore various dimensions of stereotyping. We explore the possibilities provided by microportraits by investigating stereotyping of Muslims in the Dutch media. Our (preliminary) results show that microportraits provide more detailed insights into stereotyping compared to more basic models such as word clouds.
Original language | English |
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Title of host publication | Proceedings of the LREC 2018, Eleventh International Conference on Language Resources and Evaluation |
Editors | Hitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga |
Publisher | European Language Resources Association (ELRA) |
Pages | 3734-3741 |
Number of pages | 8 |
ISBN (Electronic) | 9791095546009 |
Publication status | Published - 2019 |
Event | 11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan Duration: 7 May 2018 → 12 May 2018 |
Conference
Conference | 11th International Conference on Language Resources and Evaluation, LREC 2018 |
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Country/Territory | Japan |
City | Miyazaki |
Period | 7/05/18 → 12/05/18 |
Keywords
- Digital social science
- Stereotyping
- Text analysis