Thesis (Ph.D.) - Cairo University - Faculty of Arts - Department of English

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

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