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- W2912458336 abstract "ملخصيعتبر البحث دراسة لغوية باستخدام منهج علم المدونات لتحليل مجموعة صغيرة من التغريدات فى تويتر يصل عددها الى 4082 تم مشارکتها خلال اربع وعشرين ساعة من اصدار ترامب قرار منع مسلمى بعض الدول من دخول الولايات المتحدة فى يناير 2017 وتهدف الدراسة الى اظهار کيف يوظف مستخدمى وسائل التواصل الاجتماعى اللغة لخلق توجة عام مما يؤدى الى خلق مجتمع مشارک لنفس الرأى ويتم ذالک عن طريق تطبيق نظرية التقيىم لمارتن ووايت (2005) على المفردات الاکثر تکرارا وتحليل دلالتها ومعدل تاثيرها وقد تم ذالک بمقارنة المادة التحليلة الرئىسية باخرى استدلالية من مجموعة مقالات من صحيفة النيورک تايمز وقد اثبتت نتائج البحث ان المادة التحليلة الرئيسية تزخر بکثير من المفردات السلبية فى التعبير عن الغضب حيال القضية الجدلية فى تويتر وتوضح کيف تطور هذا السلوک فى العالم الافتراضى الى مظاهرات على ارض الواقع. Abstract:This research is a corpus-based analysis of a trending topic marked by the dominating hashtag #MuslimBan to investigate the influential role of evaluative language in social media. It combines the Appraisal theory of Martin and White (2005) as rooted in systemic functional grammar of Halliday & Matthiessen (2004) and corpus linguistics to analyze a mini corpus of 4082 tweets posted on January 28th, 2017 using AntConc for Word List, Keyword List and Concordance tool. The main corpus is tested against a reference corpus of news articles from New York Times newspaper. The aim of the study is to explore how a stance is created on social media discourse at a lexicogrammatical level and how this helps unite the microbloggers. Findings revealed that the main corpus abounds in much negative appreciation and judgment of the trigger (the MuslimBan order). It also showed that most evaluation falls in the Attitude category of Martin and White (2005) as emotional release is usually expressed via frequent use of adjectives, whereas physical release of anger is usually expressed in using action verbs. The lexical analysis also revealed that the interpersonal function is more vigorous in the main corpus and that social media is more influential in spreading slogans and communicating stances than news articles." @default.
- W2912458336 created "2019-02-21" @default.
- W2912458336 creator A5082856791 @default.
- W2912458336 date "2018-12-01" @default.
- W2912458336 modified "2023-09-27" @default.
- W2912458336 title "Evaluation of Attitudinal Lexis in Twitter Political #Hashtags: A Corpus-Based Appraisal Analysis" @default.
- W2912458336 doi "https://doi.org/10.21608/jssa.2018.24476" @default.
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