Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis

  • Aprillian Kartino STMIK Amik Riau
  • M. Khairul Anam STMIK Amik Riau
  • Rahmaddeni STMIK Amik Riau
  • Junadhi STMIK Amik Riau
Keywords: Centrality, Covid-19, Follower Rank, Social Network Analysis, Twitter

Abstract

Covid-19 is a disease of the virus that is shaking the world and has been designated by WHO as a pandemic. This case of Covid-19 can be a place of dissemination of disinformation that can be utilized by some parties. The dissemination of information in this day and age has turned to the internet, namely social media, Twitter is one of the social media that is often used by Indonesians and the data can be analyzed. This study uses the social network analysis method, conducted to be able to find nodes that affect the ongoing interaction in the interaction network of information dissemination related to Covid-19 in Indonesia and see if the node is directly proportional to the value of its popularity. As well as to know in identifying the source of Covid-19 information, whether dominated by competent Twitter accounts in their fields. The data examined 19,939 nodes and 12,304 edges were taken from data provided by the web academic.droneemprit.id on the project "Analisis Opini Persebaran Virus Corona di Media Sosial", using the period of December 2019 to December 2020 on social media Twitter. The results showed that the @do_ra_dong account is an influential actor with the highest degree centrality of 860 and the @detikcom account is the actor with the highest popularity value of follower rank of 0.994741605. Thus actors who have a high degree of centrality value do not necessarily have a high follower rank value anyway. The study ignores if there are buzzer accounts on Twitter.

 

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References

World Health Organization, “WHO Director-General’s opening remarks at the media briefing on COVID-19 - 11 March 2020 [Internet],” World Health Organization, 2020. https://www.who.int/dg/speeches/detail/who-director-generals-%0Aopening-remarks-at-the-media-briefing-on-covid-19---11-%0Amarch-2020.

O. M. Bafadhal dan A. D. Santoso, “Memetakan Pesan Hoaks Berita Covid-19 Di Indonesia Lintas Kategori, Sumber, Dan Jenis Disinformasi,” Bricol. J. Magister Ilmu Komun., vol. 6, no. 02, hal. 235, 2020, doi: 10.30813/bricolage.v6i02.2148.

Tim Apjii, “‘APJII Rilis Hasil Survei Pengguna Internet Indonesia Terbaru,’” Apjii, 2020.

S. Kemp, “DIGITAL 2020: GLOBAL DIGITAL OVERVIEW,” Datareportal, 2020. https://datareportal.com/reports/digital-2020-global-digital-overview.

Y. Wu dan Z. Duan, “Social network analysis of international scientific collaboration on psychiatry research,” Int. J. Ment. Health Syst., vol. 9, no. 1, hal. 1–10, 2015, doi: 10.1186/1752-4458-9-2.

A. Rochiyat dan A. Wibowo, “Analisis Aktor Berpengaruh Dan Aktor Popular Dengan Metode Degree Centrality Dan Follower Rank Pada Tagar Twitter ‘#gejayanmemanggil,’” JATISI (Jurnal Tek. Inform. dan Sist. Informasi), vol. 6, no. 2, hal. 130–138, 2019, doi: 10.35957/jatisi.v6i2.187.

E. Mailoa, “Analisis Node dengan Centrality dan Follower Rank pada Twitter,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 4, no. 5, hal. 937–942, 2020, doi: 10.29207/resti.v4i5.2398.

M. K. Anam, T. L. Lestari, L. Latifah, F. M. Bambang, dan S. Fadli, “Analisis Kesiapan Masyarakat Pada Penerapan Smart City di Sosial Media Menggunakan SNA,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 1, no. 10, hal. 9–12, 2021, doi: https://doi.org/10.29207/resti.v5i1.2742.

M. Hanafiah, A. Herdiani, W. Astuti, dan M. Kom, “Klasifikasi Spam Tweet Pada Twitter Menggunakan Metode Naïve Bayes ( Studi Kasus : Pemilihan Presiden 2019 ),” in e-Proceeding of Engineering, 2019, vol. 6, no. 2, hal. 9111–9120.

M. McCord dan M. Chuah, “Spam detection on twitter using traditional classifiers,” Lect. Notes Comput. Sci. (including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics), vol. 6906 LNCS, hal. 175–186, 2011, doi: 10.1007/978-3-642-23496-5_13.

B. Arianto, “Pemanfaatan Aplikasi Drone Emprit Academic dalam Menganalisis Opini Publik di Media Sosial,” JSPG J. Soc. Polit. Gov., vol. 2, hal. 177–191, 2020, doi: https://doi.org/10.24076/jspg.v2i2.415.

S. Anggelia dan A. Syaifudin, “SENTIMEN WARGANET MAHASISWA TERHADAP COVID-19,” LITERASI, vol. 5, hal. 49–57, 2021, doi: http://dx.doi.org/10.25157/literasi.v5i1.5149.

P. Suharso, “Pemanfaatan Drone Emprit dalam Melihat Trend Perkembangan Bacaan Digital melalui Akun Twitter,” Anuva, vol. 3, no. 4, hal. 333–346, 2019, doi: 10.14710/anuva.3.4.333-346.

J. Li, Y. Chen, dan Y. Lin, “Research on traffic layout based on social network analysis,” ICETC 2010 - 2010 2nd Int. Conf. Educ. Technol. Comput., vol. 1, hal. 284–288, 2010, doi: 10.1109/ICETC.2010.5529247.

A. A. Alalwan, N. P. Rana, Y. K. Dwivedi, dan R. Algharabat, “Social media in marketing: A review and analysis of the existing literature,” Telemat. Informatics, vol. 34, no. 7, hal. 1177–1190, 2017, doi: 10.1016/j.tele.2017.05.008.

R. a Hanneman dan M. Riddle, “Introduction to Social Network Methods,” Riverside, CA Univ. California, Riverside. On-line Textb., vol. 46, no. 7, hal. 5128–30, 2005, doi: 10.1016/j.socnet.2006.08.002.

B. Oselio, S. Liu, dan A. Hero, “Multilayer Social Networks,” in Cooperative and Graph Signal Processing, New York: Elsevier, 2018, hal. 679–697.

M. O. Jackson, “Social and Economic Networks,” Soc. Econ. Networks, no. March, hal. 1–504, 2010, doi: 10.1093/acprof:oso/9780199591756.003.0019.

F. Riquelme dan P. González-Cantergiani, “Measuring user influence on Twitter: A survey,” Inf. Process. Manag., vol. 52, no. 5, hal. 949–975, 2016, doi: 10.1016/j.ipm.2016.04.003.

F. Ma, X. Wang, dan P. Wang, “Counterexample: Scale-free networked graphs with invariable diameter and density feature,” arXiv, vol. 022315, hal. 1–8, 2019, doi: 10.1103/PhysRevE.101.022315.

M. Jacomy, T. Venturini, S. Heymann, dan M. Bastian, “ForceAtlas2, a continuous graph layout algorithm for handy network visualization designed for the Gephi software,” PLoS One, vol. 9, no. 6, hal. 1–12, 2014, doi: 10.1371/journal.pone.0098679.

Published
2021-08-20
How to Cite
Kartino, A., M. Khairul Anam, Rahmaddeni, & Junadhi. (2021). Analisis Akun Twitter Berpengaruh terkait Covid-19 menggunakan Social Network Analysis. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 5(4), 697 - 704. https://doi.org/10.29207/resti.v5i4.3160
Section
Artikel Rekayasa Sistem Informasi

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