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Hashtag Analysis of Indonesian COVID-19 Tweets Using Social Network Analysis
https://doi.org/10.22146/ijccs.61626 Muhammad Habibi(1*), Adri Priadana(2), Muhammad Rifqi Ma'arif(3)
(1) Universitas Jenderal Achmad Yani Yogyakarta
(2) Universitas Jenderal Achmad Yani Yogyakarta
(3) Universitas Jenderal Achmad Yani Yogyakarta
(*) Corresponding Author
Abstract
Social media has become more critical for people to communicate about the pandemic of COVID-19. In social media, hashtags are social annotations which often used to denote message content. It serves as an intuitive and flexible tool for making huge collections of posts searchable on Twitter. Through practices of hashtagging, user representations of a given post also become connected. This study aimed to analyze the hashtag of Indonesian COVID-19 Tweets using Social Network Analysis (SNA). We used SNA techniques to visualize network models and measure some centrality to find the most influential hashtag in the network. We collected and analyzed 500.000 public tweets from Twitter based on COVID-19 keywords. Based on the centrality measurement result, the hashtag #corona is a hashtag with the most connection with other hashtags. The hashtag #COVID19 is the hashtag that is most closely related to all other hashtags. The hashtag #corona is the hashtag that most acts as a bridge that can control the flow of information related to COVID-19. The hashtag #coronavirus is the most important of hashtags based on their link. Our study also found that the hashtag #covid19 and #wabah have a substantial relationship with religious-related hashtags based on network visualization.
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[1] Y. Zhao, S. Cheng, X. Yu, and H. Xu, “Chinese public’s attention to the COVID-19 epidemic on social media: Observational descriptive study,” J. Med. Internet Res., vol. 22, no. 5, p. e18825, May 2020.
[2] P. W. Cahyo and M. Habibi, “Entity Profiling to Identify Actor Involvement in Topics of Social Media Content,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 14, no. 4, Oct. 2020.
[3] Y. Yunitasari, A. Musdholifah, and A. K. Sari, “Sarcasm Detection For Sentiment Analysis in Indonesian Tweets,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 1, p. 53, Jan. 2019.
[4] W. Ahmed, J. Vidal-Alaball, J. Downing, and F. L. Seguí, “COVID-19 and the 5G conspiracy theory: Social network analysis of twitter data,” J. Med. Internet Res., vol. 22, no. 5, p. e19458, May 2020.
[5] K. G. Kapanova and S. Fidanova, “Generalized nets: A new approach to model a hashtag linguistic network on Twitter,” in Studies in Computational Intelligence, vol. 793, Springer Verlag, 2019, pp. 211–221.
[6] A. Priadana and M. Habibi, “Face Detection using Haar Cascades to Filter Selfie Face Image on Instagram,” in 2019 International Conference of Artificial Intelligence and Information Technology (ICAIIT), 2019, pp. 6–9.
[7] M. Habibi and P. W. Cahyo, “Clustering User Characteristics Based on the influence of Hashtags on the Instagram Platform,” IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 13, no. 4, pp. 399–408, 2019.
[8] C. Xing, Y. Wang, J. Liu, Y. Huang, and W. Ma, “Hashtag-Based Sub-Event Discovery Using Mutually Generative LDA in Twitter,” undefined, 2016.
[9] Y. Yılmaz and A. O. Hero, “Multimodal Event Detection in Twitter Hashtag Networks,” J. Signal Process. Syst., vol. 90, no. 2, pp. 185–200, Feb. 2018.
[10] T. Highfield and T. Leaver, “A methodology for mapping instagram hashtags,” First Monday, vol. 20, no. 1, 2015.
[11] N. Iswandhani and M. Muhajir, “K-means cluster analysis of tourist destination in special region of Yogyakarta using spatial approach and social network analysis (a case study: Post of @explorejogja instagram account in 2016),” in Journal of Physics: Conference Series, 2018, vol. 974, no. 1, p. 12033.
[12] M. S. Setatama and D. Tricahyono, “Implementasi Social Network Analysis dalam Penyebaran Country Branding ‘Wonderful Indonesia,’” Indones. J. Comput., vol. 2, no. 2, pp. 91–104, 2017.
[13] S. P. Tahalea and A. SN, “Central Actor Identification of Crime Group using Semantic Social Network Analysis,” Indones. J. Inf. Syst., vol. 2, no. 1, p. 24, Aug. 2019.
[14] M. Hung et al., “Social network analysis of COVID-19 sentiments: Application of artificial intelligence,” J. Med. Internet Res., vol. 22, no. 8, p. e22590, Aug. 2020.
[15] S. Prian Tahalea, “Identifikasi Peran Hero DOTA2 Menggunakan Social Network Analysis,” May 2020.
[16] E. Ferrara, P. De Meo, G. Fiumara, and R. Baumgartner, “Web data extraction, applications and techniques: A survey,” Knowledge-Based Syst., vol. 70, pp. 301–323, Nov. 2014.
[17] L. F. Bringmann et al., “What Do Centrality Measures Measure in Psychological Networks?,” J. Abnorm. Psychol., 2019.
[18] A. K. Yadav, R. Johari, and R. Dahiya, “Identification of Centrality Measures in Social Network using Network Science,” in Proceedings - 2019 International Conference on Computing, Communication, and Intelligent Systems, ICCCIS 2019, 2019, vol. 2019-Janua, pp. 229–234.
[19] P. De Meo, K. Musial-Gabrys, D. Rosaci, G. M. L. Sarne, and L. Aroyo, “Using centrality measures to predict helpfulness-based reputation in trust networks,” ACM Trans. Internet Technol., vol. 17, no. 1, pp. 1–20, Feb. 2017.
[20] J. Zhang and Y. Luo, “Degree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network,” 2017, pp. 300–303.
[21] Cai, Zeng, Wang, Li, and Hu, “Community Detection Method Based on Node Density, Degree Centrality, and K-Means Clustering in Complex Network,” Entropy, vol. 21, no. 12, p. 1145, Nov. 2019.
[22] M. S. Setatama and D. Tricahyono, Ir., M.M., Ph.D., “Implementasi Social Network Analysis pada Penyebaran Country Branding ‘Wonderful Indonesia,’” Indones. J. Comput., vol. 2, no. 2, p. 91, Nov. 2017.
[23] C. F. A. Negre et al., “Eigenvector centrality for characterization of protein allosteric pathways,” Proc. Natl. Acad. Sci. U. S. A., vol. 115, no. 52, pp. E12201–E12208, Dec. 2018.
[24] R. Djalante et al., “Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020,” Prog. Disaster Sci., vol. 6, p. 100091, Apr. 2020.
[25] D. L. Hansen, B. Shneiderman, M. A. Smith, and I. Himelboim, “Calculating and visualizing network metrics,” in Analyzing Social Media Networks with NodeXL, Elsevier, 2020, pp. 79–94.
[26] J. Golbeck, “Network Structure and Measures,” in Analyzing the Social Web, Elsevier, 2013, pp. 25–44.
[27] F. C. Tsai, M. C. Hsu, C. T. Chen, and D. Y. Kao, “Exploring drug-related crimes with social network analysis,” in Procedia Computer Science, 2019, vol. 159, pp. 1907–1917.
[28] D. Suárez, J. M. Díaz-Puente, and M. Bettoni, “Risks Identification and Management Related to Rural Innovation Projects through Social Networks Analysis: A Case Study in Spain,” L. 2021, Vol. 10, Page 613, vol. 10, no. 6, p. 613, Jun. 2021.
[29] S. W. Wiiava and I. Handoko, “Examining a Covid-19 Twitter Hashtag Conversation in Indonesia: A Social Network Analysis Approach,” Proc. 2021 15th Int. Conf. Ubiquitous Inf. Manag. Commun. IMCOM 2021, Jan. 2021.
[30] E. Carnia, B. Fermadona, H. Napitupulu, N. Anggriani, and A. K. Supriatna, “Implementation of centrality measures in graph represented information spreads with hashtag #bersatulawancorona in Twitter,” J. Phys. Conf. Ser., vol. 1722, no. 1, p. 012068, Jan. 2021.
[31] A. Priadana and S. P. Tahalea, “Hashtag activism and message frames: social network analysis of Instagram during the COVID-19 pandemic outbreak in Indonesia,” J. Phys. Conf. Ser., vol. 1836, no. 1, p. 012031, Mar. 2021.
DOI: https://doi.org/10.22146/ijccs.61626
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