Conclusion

1n the analysis of the Twitter agenda covering the period 1-31 May 2020, real-life data were reflected in twitter messages. 1t was observed that the selected keywords were the expressions that occupied the agenda the most and were frequently included in the messages.

According to the keywords determined in this study, the data set obtained from the Twitter platform was analysed using natural language processing and text mining techniques. According to the analysis results, it was observed that sharing about the “YKS” exam was intense at the dates when the data set was created. At the same time, it is seen that the posts about “Coronavirus” epidemic come to the fore according to the keywords specified. Based on these results, it was seen that the university entrance exams came to the fore among the pandemic issue in our country on the dates specified on the Twitter social platform.

The messages of the period reflecting the pandemic period and examined under the research cover the last month of the curfews. Although there are complaints of isolation at home and fear of catching Coronavirus, the messages and tags about the virus also include spreading concern and panic.

Economic concerns and the difficulty of liabilities of the ongoing life are reflected in the content of the message. There are economic difficulties, expectations and social supports included in the topics in the tagging and topics. These issues are also tagged for the president’s own account, the institution, the relevant ministries and the ministers’ own accounts. Even though political parties and their leaders were not determined as keywords, it was observed that they often rank high on the Twitter agenda. The political power, which was moved higher by Twitter users in this process, became the president and relevant ministers.

References

Antenuci D (2020) Using social media to measure labor market flows. https://www.nber.org/pap ers∕w20010. Accessed on 5 Dec 2020

Antonakaki D (2020) A survey of Twitter research: data model, graph structure, sentiment analysis and attacks. Expert Syst Appl 164:114006. https://doi.org/10.1016/j.eswa.2020.114006

Atila S (2020) Sajtltk Bakani Fahrettin Koca’nin Twitter analizi: Takipgi sayιsι 391 binden 5 milyona gikti, yaklagik 4 milyon kez retweet edildi. https://medyascope.tv/2020/04/30/saglik- bakam-fahrettm-kocanin-twitter-analizi-takipci-sayisi-391-binden-5-milyona-cikti-yaklasik-4- milyon-kez-retweet-edildi/. Accessed on 15 Nov 2020

Bicakci B (2019) Post-truth (aginda Halkla iligkiler’in “Hakikat Yoneticiligi” Rolu: Gida ve Beslenme Alanindaki Yalan Haberlere Yonelik Stratejiler. Kurgu 27(4):61-78. https://dergipark. org.tr/tr/pub/kurgu/issue/54877/752382. Accessed on 21 Dec 2020

Bozkurt O (2018) Sosyal Medya ve Kulturel Yansimalari. Gazi Universitesi Sosyal Bilimler Dergisi 5(14):406-417. https://dergipark.org.tr/tr/pub/gusbd/issue/39212/370186

Calic GMG (2020) Big data for social benefits: innovation as a mediator of the relationship between big data and corporate social performance. J Bus Res. https://doi.org/10.1016/j.jbusres.2020. 11.003

Cepeda MP, Bolzmann LGA (2021) Refugee information consumption on Twitter. J Bus Res 123:529-537. https://doi.org/10.1016/j.jbusres.2020.10.029

Ceyhan A (2019) Dijital Iletigim (aginda Siyasetin Dijitallegmesi Uzerine Bir Inceleme: post-truth ve Dijital Siyasetin Sahte Haber Ekseninde Analizi. Kurgu 27(4):1-17. https://dergipark.org.tr/ tr/pub/kurgu/issue/54877/752377

ζ⅛lik N (2020) Belirsizliklerin Dunya Ekonomisine Yonelik Yansimalari: COV1D-19 Salgini Oncesi ve Sonrasi DunyaEkonomisi, Polat M, Aslantag M (eds) Bir Virusun Oggrettikleri, (⅛inde): Nobel Yayincilik, Ankara, pp 315-330

de Melo T, Figueiredo CMS (2020) A first public dataset from Brazilian twitter and news on COV1D-19 in Portuguese. Data Brief 32:106179.

Durahim A, Cogkun O-M (2015) #iamhappybecause: gross national happiness through Twitter analysis and big data. Technol Forecast Social Change 99:92-105. https://doi.org/10.1016/j.tec hfore.2015.06.035

Fernando S (2019) Towards a large-scale twitter observatory for political events. Future Gen Comput Syst September 2020 110:976-983. https://doi.org/10.1016/j.future.2019.10.013

1ndaco A (2020) From twitter to GDP: estimating economic activity from social media. Reg Sci Urban Econ 85:103591. https://doi.org/10.1016/j.regsciurbeco.2020.103591

Koh JX, Liew TM (2020) How loneliness is talked about in social media during COV1D-19 pandemic: text mining of 4,492 Twitter feeds. J Psychiatr Res. https://doi.org/10.1016/j.jpsych ires.2020.11.015

Li L (2020) Characterizing the propagation of situational information in social media during COV1D-19 epidemic: a case study on Weibo. 1EEE Trans Comput Social Syst 7(2):556-562. Article number 9043580

Million M (2020) Clinical efficacy of chloroquine derivatives in COV1D-19 infection: comparative meta-analysis between the big data and the real World. New Microbes New Infect 100709. https:// doi.org/10.1016/j.nmni.2020.100709

NLTK (2020) NLTK

Shamim S (2020) Big data analytics capability and decision making performance in emerging market firms: the role of contractual and relational governance mechanisms. Technol Forecast Social Change 161:120315. https://doi.org/10.1016/j.techfore.2020.120315

Sinnoth RO (2016) Chapter 15—a case study in big data analytics: exploring twitter sentiment analysis and the weather. Big DataPrinciples Paradigms 357-388. https://doi.org/10.1016/B978- 0-12-805394-2.00015-5

Statista (2020) Twitteruser statistics. https://www.statista.com/statistics/242606/number-of-active- twitter-users-in-selected-countries/

Tweepy (2020) https://www.tweepy.org/. Accessed on 29 Nov 2020

Yu Y, Wang X (2015) World cup 2014 in the twitter world: a big data analysis of sentiments in U.S.

Zhang Y (2020) A bibliometric review of a decade of research: Big data in business research—setting a research agenda. J Bus Res. https://doi.org/10.1016/j.jbusres.2020.11.004

Ibrahim Attila Acar CAR has books, articles and papers on public strategic management, public financial management and control law (Law No: 5018), health and education economy, refugees crisis, auditing, financial management and budgeting. He was a member of the Specialization Commission in the Development Plans of 9-10 and 11 and involved in quality, effectiveness, university rankings, European University Association higher education assessment and rating studies in higher education. He worked as a panelist and independent evaluator of TUB1TAK and Development Agencies. He participated in programs in written and visual media. He prepared and presented the Economic Agenda program at TRT for many years. The analyzes are located in its own website, acarhoca.com. He worked at Suleyman Demirel University for 10 years. And he is currently working at the Department of Economics, Faculty of Economics and Administrative Sciences at Izmir Katip Celebi University. He was the Vice Rector and the founding dean at the same university.

Volkan Altintag is a Lecturer in the Computer Programming department at Manisa Celal Bayar University. He got his Ph.D., from Suleyman Demirel University in 2020, M.Sc. and B.Sc. from Suleyman Demirel University in 2012, 2005 respectively. His research focuses on Natural

Language Processing, topic modeling, big data analysis, text mining and machine learning. Recently, his research is on web ontology.