عنوان
پدید آورنده
موضوع
رده
کتابخانه
محل استقرار
TLpq2482660599
انگلیسی
Media Bias:
[Thesis]
Algamde, Amaal
A Corpus-Based Contrastive Study of the Online News Coverage on the Syrian Revolution - A Cxritical Discourse Analysis Perspective
Bangor University (United Kingdom)
2019
343
Ph.D.
Bangor University (United Kingdom)
2019
The Syrian revolution is one of a series of revolutions that erupted in the Middle East in late 2010 under the name of Arab Spring. This incident became a major issue, which attracted the attention of international mass media and led to the involvement of global super powers. This research is premised on the assumption that media discourse is biased and influenced by political, economic and social contexts (Fowler, 1991). Based on this assumption, the study opts to use news agencies as data sources because they are impartial news suppliers that are assumed to hold a non-ideological perspective. The research compares the online media representation of the Syrian revolution in the Iranian Fars news agency to that in UK Reuters news agency. It explores the discursive constructions of anti- and pro-government powers in the Syrian revolution in a 1,000,000-word corpus of online reports released between 2013 and 2015. Despite the body of research on the Arab revolutions, very few linguistic studies have tackled the Syrian revolution. In addition, none of them have considered the array of the fighting armed groups involved in this conflict and their ideological background.
The study adopts a corpus-based critical discourse analysis to investigate the discursive constructions of anti- and pro-government powers across the micro (i.e., lexical choices) and macro linguistic levels (i.e., the broader sense of the linguistic features in relation to their contexts). The analysis, at the micro level, examines three types of lexicosemantic relations: the semantic macrostructures within which the two conflicting sides are framed, the lexical collocations characterising the news discourse and the discourse prosodies they convey about the two sides of the conflict. The study utilises two computer-based approaches, the Sketch engine (Kilgarriff et al, 2004) and Antony's (2014) AntConc software, to minimize the subjectivity of analysis. At the macro level, the study draws upon an eclectic framework from critical discourse analysis. It incorporates van Leeuwen (2008) socio-semantic model, Transitivity model (1994) and Ideological Square (van Dijk, 1998) to investigate the possible bias in the coverage of the chemical attacks. The selection of this incident was guided by the findings of the corpus analysis.
The findings indicate that Fars representation of the Syrian Revolution was biased. Fars excluded the Sunni social actors, suppressed the Islamic faction identity of the rebels and depicted the uprising as a real war against foreign-backed militants. The rebels are stereotyped as non-Syrians, terrorists, ISIL and Takfiri. In contrast, Reuters provides a more balanced representation of the two warring sides. The identification of the anti-government social actors is more critical and less stereotyped as they are categorized into moderate and radical. In addition, the identification of the government SAs were neutral and formal. Their negative racist representation is indexed by their actions at the political and military level not by the reporters' evaluative attributes. The study concluded that Fars news agency is highly influenced by the political stance of the Iranian government, its pan-Islamist ideology, and the shared Islamic orientation with the Syrian government. On the contrary, Reuters' coverage showed no dichotomous ideological representation, reflecting, to some extent, its independence from the effect of the political stance of the UK.
The study adopts a corpus-based critical discourse analysis to investigate the discursive constructions of anti- and pro-government powers across the micro (i.e., lexical choices) and macro linguistic levels (i.e., the broader sense of the linguistic features in relation to their contexts). The analysis, at the micro level, examines three types of lexicosemantic relations: the semantic macrostructures within which the two conflicting sides are framed, the lexical collocations characterising the news discourse and the discourse prosodies they convey about the two sides of the conflict. The study utilises two computer-based approaches, the Sketch engine (Kilgarriff et al, 2004) and Antony's (2014) AntConc software, to minimize the subjectivity of analysis. At the macro level, the study draws upon an eclectic framework from critical discourse analysis. It incorporates van Leeuwen (2008) socio-semantic model, Transitivity model (1994) and Ideological Square (van Dijk, 1998) to investigate the possible bias in the coverage of the chemical attacks. The selection of this incident was guided by the findings of the corpus analysis.
The findings indicate that Fars representation of the Syrian Revolution was biased. Fars excluded the Sunni social actors, suppressed the Islamic faction identity of the rebels and depicted the uprising as a real war against foreign-backed militants. The rebels are stereotyped as non-Syrians, terrorists, ISIL and Takfiri. In contrast, Reuters provides a more balanced representation of the two warring sides. The identification of the anti-government social actors is more critical and less stereotyped as they are categorized into moderate and radical. In addition, the identification of the government SAs were neutral and formal. Their negative racist representation is indexed by their actions at the political and military level not by the reporters' evaluative attributes. The study concluded that Fars news agency is highly influenced by the political stance of the Iranian government, its pan-Islamist ideology, and the shared Islamic orientation with the Syrian government. On the contrary, Reuters' coverage showed no dichotomous ideological representation, reflecting, to some extent, its independence from the effect of the political stance of the UK.
English as a second language--ESL
Language arts
Linguistics
Algamde, Amaal
مطالعه متن کتاب p
[Thesis]
276903
a
Y
