Gender discourse on politicians in news writing : a corpus study
Vahtera, Anna (2018-11-29)
Gender discourse on politicians in news writing : a corpus study
(29.11.2018)
Julkaisu on tekijänoikeussäännösten alainen. Teosta voi lukea ja tulostaa henkilökohtaista käyttöä varten. Käyttö kaupallisiin tarkoituksiin on kielletty.
avoin
Julkaisun pysyvä osoite on:
https://urn.fi/URN:NBN:fi-fe2018121450957
https://urn.fi/URN:NBN:fi-fe2018121450957
Tiivistelmä
This study examines gender discourse in newspapers’ coverage on male and female politicians. Newspapers have an important role in creating and maintaining gender discourses, perceptions on gender and gender stereotypes, since they are considered a trustworthy source of information by large audiences even in the current time, where the traditional media are experiencing a decline in their popularity. Therefore, it is important to examine critically the word choices newspapers make when they report on leading political figures, since these choices can hide gender bias and affect the readers’ perceptions.
This study uses Corpus-driven Discourse Analysis in order to examine newspapers’ word choices. The theoretical background comes from Critical Discourse Analysis (CDA), especially from Feminist Critical Discourse Analysis (FCDA) and Feminist Poststructuralist Discourse Analysis (FPDA). These theories combined with Corpus Linguistics enable the critical examination of the selected data. In addition, the thesis will also have a short verb transitivity analysis using Systemic Functional Linguistics. For the study, four politicians were selected as study subjects, two of which were female and two male politicians. The politicians are Angela Merkel, Theresa May, David Cameron and Justin Trudeau. These politicians were used as subjects in collocate searches that were performed using the online-based corpus News on the Web.
The results do not suggest any remarkable gender bias in the news coverage of these four politicians. One of the hypotheses was that there would be more words related to appearance and family when the female politicians were reported on, but this was not the case. The other hypothesis was that the verb choices and their transitivity would reveal gender-based differences, but the differences found were more likely related to the subjects’ individual characters. In future research there could be more study subjects in order to receive more generalizable results, and to find more aspects related to gender. There are also prospects for using multimodal research methods, e.g. multimodal corpora.
This study uses Corpus-driven Discourse Analysis in order to examine newspapers’ word choices. The theoretical background comes from Critical Discourse Analysis (CDA), especially from Feminist Critical Discourse Analysis (FCDA) and Feminist Poststructuralist Discourse Analysis (FPDA). These theories combined with Corpus Linguistics enable the critical examination of the selected data. In addition, the thesis will also have a short verb transitivity analysis using Systemic Functional Linguistics. For the study, four politicians were selected as study subjects, two of which were female and two male politicians. The politicians are Angela Merkel, Theresa May, David Cameron and Justin Trudeau. These politicians were used as subjects in collocate searches that were performed using the online-based corpus News on the Web.
The results do not suggest any remarkable gender bias in the news coverage of these four politicians. One of the hypotheses was that there would be more words related to appearance and family when the female politicians were reported on, but this was not the case. The other hypothesis was that the verb choices and their transitivity would reveal gender-based differences, but the differences found were more likely related to the subjects’ individual characters. In future research there could be more study subjects in order to receive more generalizable results, and to find more aspects related to gender. There are also prospects for using multimodal research methods, e.g. multimodal corpora.