الفهرس 
A Linguistic Study of selected Tweets Written in English: A Genre Analysis
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Abstract
This study investigated the linguistic and hypertextual features surrounding hashtag campaigning on Twitter through eleven of the top trending hashtags that endorsed social, political, and humanitarian causes during 2016-2018. It customized the traditional genre analysis model by Bhatia (1993), Casañ-Pitarch{u2018}s multi-genre structures model (2017), and Nielsen and Askehave (2005) framework on digital genres to better suit the nature of Twitter hashtag campaigns. Accordingly, the study devised a new eclectic model that can be utilized in analyzing other different digital genres. Through employing the new model, the study adopted a mixed method approach to analyze a corpus of 33,000 tweets written in English in eleven hashtag campaigns. These hashtags included #BlackLivesMatter, #NeverTrump, #TimesUp, #Metoo, #NetNeutrality, #SaveAleppo, #MakeAmericaGreatAgain, #YemenInquiryNow, #FeelTheBern, #InternationalWomensDay, and #NeverHillary. Using manual analysis and methods of corpus linguistics, an extensive genre analysis was conducted in terms of the micro-linguistic and hypertextual features of these hashtags. The analysis showed that the hashtag campaigns varied in the choice and the frequency of the hypertextual and micro-linguistic features used in each campaign. Although the selected hashtag campaigns belonged to one genre and had one communicative purpose which is propagating for a certain cause, this purpose was achieved through reporting only information or through interaction with the readers. Also, there were a number of hypertextual and linguistic similarities and differences between the selected political, social, and humanitarian hashtag campaigns