This study uses novel deep learning-based language models to extract meaningful information from vast chunks of textual data from Twitter on the competing narratives of the recent Syrian immigration to Turkey. Our analysis identifies five main topics in the framing of Syrian immigration in Turkish Twittersphere. In this paper, we demonstrate correlational links between the timing of landmark events and change in the percent share of trends in those topics across time. We highlight two important observations: (a) Social benefit demands of natives on Twitter rose sharply with the COVID-19 pandemic, leading to ever more widespread sentiments of welfare chauvinism and (b) Patriotic feelings and the implementation of an interventionist foreign policy agenda in the immigrants’ country of origin created a relatively tolerant yet patronizing attitude towards migrants. As the COVID-19 pandemic and immigration frequently occupy the center stage in politics of immigrant-hosting societies, our research has international appeal beyond its specific geographical context.
AbidR. Z.MananS. A.RahmanZ. A. A. A. (2017). ‘A flood of Syrians has slowed to a trickle’: The use of metaphors in the representation of Syrian refugees in the online media news reports of host and non-host countries. Discourse & Communication, 11(2), 121–140. https://doi.org/10.1177/1750481317691857
BhatiaA.JenksC. J. (2018). Fabricating the American dream in US media portrayals of Syrian refugees: A discourse analytical study. Discourse & Communication, 12(3), 221–239. https://doi.org/10.1177/1750481318757763
6.
BianchiF.TerragniS.HovyD. (2020). Pre-training is a hot topic: Contextualized document embeddings improve topic coherence. ArXiv Preprint ArXiv:2004.03974.
7.
BoyrazC. (2017). Ethnic turkification and homogenization from Ottoman empire to the Turkish republic: Critical investigations into the historiography of non-Muslims in Turkey. Turkish Studies, 18(2), 378–389. https://doi.org/10.1080/14683849.2016.1246944
8.
BozdagC.SmetsK. (2017). Understanding the images of Alan Kurdi with “small data”: A qualitative, comparative analysis of tweets about refugees in Turkey and Flanders (Belgium). International Journal of Communication, 11(0), 24. Retrieved from https://ijoc.org/index.php/ijoc.
9.
BrubakerR. (2017). Between nationalism and civilizationism: The European populist moment in comparative perspective. Ethnic and Racial Studies, 40(8), 1191-1226. https://doi.org/10.1080/01419870.2017.1294700.
10.
CeobanuA. M.EscandellX. (2010). Comparative analyses of public attitudes toward immigrants and immigration using multinational survey data: A review of theories and research. Annual Review of Sociology, 36(1), 309–328. https://doi.org/10.1146/annurev.soc.012809.102651
11.
ConinckD. D.OganC.d’HaenensL. (2020). Can ‘the other’ ever become ‘one of us’? Comparing Turkish and European attitudes toward refugees: A five-country study. International Communication Gazette, 83(3), 217–237. https://doi.org/10.1177/1748048519895376
12.
DağtaşB. (2013). Constructing the “Arab Spring”: News discourses in Turkish newspapers. Global Media Journal: Canadian Edition, 6(2), Retrieved fromhttp://gmj-canadianedition.ca/
13.
DevlinJ.ChangM.-W.LeeK.ToutanovaK. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. ArXiv Preprint ArXiv:1810.04805.
14.
DoğanN.Süleymanoğlu-KürümR.BekaroğluE. A.CinM. (2021). İstanbul göç araştırması 2020. İstanbul Büyükşehir Belediyesi.
15.
DoğanayÜ.KeneşH. Ç. (2016). Yazılı basında suriyeli ‘mülteciler’: Ayrımcı söylemlerin rasyonel ve duygusal gerekçelerinin inşası. Mülkiye Dergisi, 40(1), 143-184.
16.
Efeİ. (2015). Türk basınında Suriyeli sığınmacılar. SETA.
GuidryJ. P.AustinL. L.CarlyleK. E.FrebergK.CacciatoreM.MeganckS.JinY.MessnerM. (2018). Welcome or not: Comparing# refugee posts on Instagram and pinterest. American Behavioral Scientist, 62(4), 512–531. https://doi.org/10.1177/0002764218760369
20.
HouX.ShenL.SunK.QiuG. (2017). Deep feature consistent variational autoencoder. In 2017 IEEE Winter conference on applications of computer vision (WACV), Santa Rosa, CA, 241133–311141March 2017, pp. –.
21.
International Crisis Group. (2016). Turkey’s refugee crisis: The politics of permanence (pp. 241). Europe Report.
22.
KeymanE. F. (2013). Articulating citizenship and identity: The “Kurdish question” in Turkey. In KeymanF.IcduyguA. (Eds.), Citizenship in a global world: European questions and Turkish experiences (p. 267). Routledge.
23.
LafiN. (2014). Early republican Turkish orientalism? In MaksudyanN. (Ed.), Women and the city, women in the city: A gendered perspective on Ottoman urban history (pp. 139-148). Berghahn Books.
24.
MäntyläM. V.GraziotinD.KuutilaM. (2018). The evolution of sentiment analysis—a review of research topics, venues, and top cited papers. Computer Science Review, 27(February), 16-32. https://doi.org/10.1016/j.cosrev.2017.10.002.
Onay-CokerD. (2019). The representation of Syrian refugees in Turkey: A critical discourse analysis of three newspapers. Continuum, 33(3), 369–385. https://doi.org/10.1080/10304312.2019.1587740
29.
ÖzdemirF.Öner-ÖzkanB. (2016). Türkiye’de sosyal medya kullanıcılarının suriyeli mültecilere ilişkin sosyal temsilleri. Nesne Psikolojisi Dergisi, 4(8), 227–245. https://doi.org/10.7816/nesne-04-08-04
30.
ÖztürkN.AyvazS. (2018). Sentiment analysis on twitter: A text mining approach to the Syrian refugee crisis. Telematics and Informatics, 35(1), 136–147. https://doi.org/10.1016/j.tele.2017.10.006
31.
RadfordA.WuJ.ChildR.LuanD.AmodeiD.SutskeverI. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8), 9.
32.
RettbergJ. W.GajjalaR. (2016). Terrorists or cowards: Negative portrayals of male Syrian refugees in social media. Feminist Media Studies, 16(1), 178–181. https://doi.org/10.1080/14680777.2016.1120493
33.
RoserM.RitchieH.Ortiz-OspinaE.HasellJ. (2020). Coronavirus pandemic (COVID-19). Our world in data.
34.
SchweterS. (2020). Berturk-bert models for Turkish.
35.
StoufferS. A.SuchmanE. A.DeVinneyL. C.StarS. A.WilliamsR. M.Jr. (1949). The American soldier: Adjustment during army life. (Studies in social psychology in world war II) (Vol. 1). Princeton University Press.
36.
StrakováJ.StrakaM.HajicJ. (2014). Open-source tools for morphology, lemmatization, POS tagging and named entity recognition. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations,Baltimore, MD, 13–18June 2014. https://doi.org/10.3115/v1/p14-5003
37.
SunS.LuoC.ChenJ. (2017). A review of natural language processing techniques for opinion mining systems. Information Fusion, 36, 10-25. https://doi.org/10.1016/j.inffus.2016.10.004.
38.
VaswaniA.ShazeerN.ParmarN.UszkoreitJ.JonesL.GomezA. N.KaiserL.PolosukhinI. (2017). Attention is all you need. ArXiv Preprint ArXiv:1706.03762.
VollmerB.KarakayaliS. (2018). The volatility of the discourse on refugees in Germany. Journal of Immigrant & Refugee Studies, 16(1–2), 118–139. https://doi.org/10.1080/15562948.2017.1288284
41.
WallaceR. (2018). Contextualizing the crisis: The framing of Syrian refugees in Canadian print media. Canadian Journal of Political Science/Revue Canadienne de Science Politique, 51(2), 207–231. https://doi.org/10.1017/S0008423917001482
WhiteJ. (2014). Muslim nationalism and the new Turks: Updated edition (Vol. 52). Princeton University Press.
44.
YildirimS. (2020). Comparing deep neural networks to traditional models for sentiment analysis in Turkish language. In Deep learning-based approaches for sentiment analysis (pp. 311–319). Springer.
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