Al Moussa, Arwa and Unger, Johann (2024) A Critical Discourse Analysis of The Representation of Migrants on Twitter : The Case of #SaudiArabiaForTheSaudis. PhD thesis, Lancaster University.
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Abstract
This research examines anti-migrant discourse on Twitter. I carry out a qualitative analysis of tweets with the hashtag #SaudiArabiaForTheSaudis للسعوديين_ السعودية# from a discourse-historical perspective (see Reisigl & Wodak, 2016). The main aim of this thesis is to explore the discursive construction of the main social actors involved and to examine topoi as a key part of argumentation schemes employed to justify and downplay the exclusionary discourses within this hashtag. The study sample consisted of 836 tweets with the hashtag #SaudiArabiaForTheSaudis posted between the years 2016 and 2020. A two-level analysis was carried out to investigate the representation and argumentation strategies. The first level focuses on mapping out the content of the tweets, that is the discourse topics within the tweets with the SaudiArabiaForTheSaudis hashtag, and the semiotic resources which are often employed in the data set as forms of legitimation. The second level is an in-depth analysis of the discursive construction of ‘migrants’ in Saudi and their ‘allies’ as social actors where particular attention is paid to nominative and predicative strategies, followed by an analysis of a sample of the most salient argumentative shortcuts (topoi) exploited in the tweets to legitimise the self and delegitimise the other (see Reisigl and Wodak 2001). Given that Twitter provides its users with various meaning-making resources, they rely partially or solely on multimodal resources to construct the other or to advance an argument, the in-depth analysis inevitably draws on multimodal analysis (Kress and Van Leeuwen, 2006) combined with the DHA approach. The first stage of analysis revealed that of the codes related to social processes, those that belong to the category unemployment were the most prevalent codes in the data set.Institutional favouritism was the most frequent code in the data set followed by Saudization issues. On the other hand, thematic hashtags were the most frequent code among the semiotic resources identified in the data set. Taking advantage of people’s frustrations with unemployment and fears of losing their identity has led to the scapegoating of migrants, naturalised Saudis, and those who defend them. In the second stage of analysis which focused on the representational and argumentation strategies present in the data set, Arab migrants were the most negatively constructed group of migrants. They were portrayed as the ungrateful, ill-intentioned other who look down upon Saudi as just an uncivilised desert Bedouin society. Ethnonyms or nationyms are usually used in conjunction with other strategies such as criminalisation and problematisation that evoke stereotyped images of all migrants but particularly Arabs. The presumed lack of belonging of naturalised citizens led to their exclusion, and both naturalised Saudis and other Saudis who defend migrants were portrayed as traitors with ulterior motives. The argumentation schemes that are typical of antimigrant discourse were identified using Reisigl & Wodak's (2001) list of topoi. The topos of threat, which is a common topos in populist discourses was the most dominant topos identified in the data set. It is often employed textually and visually to legitimise the self and delegitimise the other. Migrants were constructed as a threat to Saudi Arabia's culture, national security, but most primarily to Saudis’ employment and wages. Other topoi such as the topos of responsibility, abuse and culture were also present in the data set. This thesis aims to contribute to the body of knowledge about anti-migrant discourse online by examining Saudi Arabia, a largely understudied context. It focuses on how discriminatory ideologies within tweets with the hashtag #SaudiArabiaForTheSaudis are constructed and propagated. The analysis shows that the affordances of Twitter facilitated this negative construction of ‘migrants’ and their ‘allies’ in the data set and thereby paved the way for the legitimation of discriminatory ideologies in relation to migration.