| Political news discourse belongs to the genre of media discourse. In recent yeas, owing to the rapid development of the Internet technology, more and more newspapers tend to use website as a powerful media to publicize their news, enabling their readers to get access to the latest news more conveniently. The words used in political news reports are likely to reflect the stance of their writers. Thus the political news discourse is filled with interpersonal meanings, which reflect the interaction of the various opinions and voices embedded in the texts.Halliday’s Systemic Functional Grammar involves interpersonal meanings, exerting a great influence on the studies and researches on interpersonal meanings. However, it inevitably has certain limitations. Based on SFL(Systemic Functional Linguistics), J.R.Martin and his colleagues put forward Appraisal Theory which advances the research of interpersonal meaning."Appraisal System is a new development of SFL, which is located as an interpersonal system at the level of discourse semantics"(Martin&White.2005). Appraisal Theory has three sub-systems:Attitude, Engagement and Graduation, which can be further divided into several subcategories. Due to the limited time and space, the present thesis only deals with the Engagement resources in English and Chinese political news.A total of60latest news texts are drawn from four websites, all of which belong to the category of political news. Fifteen of which are New York Times news selected from the website:vvww.nytimes.com, fifteen are the Guardian news selected from the website: www.guardian.co.uk, fifteen are Xinhua net news selected from the website: www.xinhuanet..com, and fifteen are People’s Daily news selected from the website: paper.people.com.cn, whose total words is29,938words in English and29,722words in Chinese. The present study uses UAM Corpus Tool to analyze and compare the materials selected, finding Engagement resources prevalent in both news texts, and the occurrence is significantly higher in English than in Chinese. The similarities of the distribution pattern of Engagement recourses in English and Chinese political news are as follows:1) Dialogic Expansion overweighs Dialogic Contraction in both English and Chinese political news texts, with the occurrence of65.7percent in English and53.9percent in Chinese;2) Within Dialogic Contraction, values of Disclaim (27.4percent in English and35.7percent in Chinese) outweigh that of Proclaim (6.9percent in English and10.2percent in Chinese;3) In the subcategory of Proclaim, the most frequently used values are Endorse, accounting for3.6percent in English and7.9percent in Chinese;4) With Dialogic Expansion, Acknowledge takes the most part within the subcategory of Attribute in both English and Chinese political news texts, occupying32.0percent with450instances in English and28.9percent with176instances in Chinese in the whole Engagement system.Meanwhile, the thesis also finds two main differences contained in the distribution pattern of Engagement recourses in English and Chinese political news, which are:1) With Dialogic Contraction, English reporters of political news prefer to use more Counter and Concur while Chinese reporters are inclined to use more Deny and Endorse;2) Under Dialogic Expansion, English reporters of political news prefer to use more Entertain, and Acknowledge while Chinese reporters tend to use more Attribute and Distance in political news writing.This study applies Appraisal Theory into the analysis of political news discourse, focusing on the distribution pattern and ways of realization of Engagement resources and its sub-systems. The author attempts to explore how the news reporters use Engagement resources to engage with their readers, making their reports more objective and convincing. The thesis also compares and analyzes the differences of the Engagement resources in the two languages and tries to provide an explanation. It is hoped to shed some light on the following research and make a contribution to the application of Appraisal Theory in different fields. |
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