Analisis jejaring sosial gempa Cianjur di Twitter sebagai mitigasi dampak bencana
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Abstract
Indonesia is a country that has the potential for volcanic and tectonic earthquakes. One of the actions that can be taken to minimise the impact of disasters is to mitigate natural disasters through social media, such as Twitter. The #PrayForCianjur hashtag is one of the efforts to expand information by utilising Twitter to minimise the impact of the disaster in Cianjur as well as provide prompt action from related parties. This research aims to analyse the social network hashtag #PrayForCianjur, which became a topic of public discourse on Twitter after the Cianjur earthquake occurred. The study results show that the information centre actors are non-institutional actors such as @marchfoward, @aqfiazfan, @tanyakanrl, and @convomf. Meanwhile, institutional actors such as @nctzenhumanity, @detik.com, and @info_bmkg There are interesting findings in this research: actors who should be actively involved in disaster mitigation are not popular in the network. This study will operate as a foundation for providing the crisis management and mitigation teams with helpful information that they can use to prepare for and plan an efficient disaster response and to support the creation of automated crisis management systems in the future.
References
Alhajj, R., & Rokne, J. (2014). Encyclopedia of Social Network Analysis and Mining. Springer Science+Business Media Publisher.
APJII. (2020). Penetrasi Pengguna Internet 2019-2020. Buletin APJII.
Bakry, G. N. (2020). Struktur Jaringan Pengguna Twitter dengan Tagar #Bandunglawancovid19. Jurnal Komunikasi Global, 9(2), 209–229. https://doi.org/10.24815/jkg.v9i2.17478
Bakry, G. N., & Kusmayadi, I. M. (2021). Peran Pers Sebagai Aktor Gerakan Digital Tagar #SolidaritasUntukNTT di Twitter. Kajian Jurnalisme, 05(01), 98–114. https://doi.org/10.24198/jkj.v5i1.33458
Bala, M. M., Navya, K., & Shruthilaya, P. (2017). TEXT MINING ON REAL TIME TWITTER DATA FOR DISASTER RESPONSE. International Journal of Civil Engineering and Technology (IJCIET), 8(8), 20–29. http://www.iaeme.com/ijciet/index.asp
Barisione, M., & Ceron, A. (2017). A Digital Movement of Opinion? Contesting Austerity Through Social Media. Social Media and European Politics, 77–104. https://doi.org/https://doi.org/10.1057/978-1-137-59890-5_4
Block, P., Hoffman, M., Raabe, I. J., Dowd, J. B., Rahal, C., Kashyap, R., & Mills, M. C. (2020). Social network-based distancing strategies to flatten the COVID-19 curve in a post-lockdown world. Nature Human Behaviour, 4(6), 588–596. https://doi.org/10.1038/s41562-020-0898-6
BNPB. (2021, April 27). Mengenal Jenis Bahaya Letusan Gunung Api di Indonesia. BNPB. https://bnpb.go.id/berita/mengenal-jenis-bahaya-letusan-gunung-api-di-indonesia
Borgatti, S. P., Everett, M. G., & Johnson, J. C. (2013). Analyzing Social Networks (J. Seaman (ed.); 1st ed.). Sage Publications Ltd.
Carnia, E., Fermadona, B., Napitupulu, H., Anggriani, N., & Supriatna, A. K. (2021). Implementation of centrality measures in graph represented information spreads with hashtag #bersatulawancorona in Twitter. Journal of Physics: Conference Series, 1722(1). https://doi.org/10.1088/1742-6596/1722/1/012068
Carrington, J. S. and P. J. (2011). Scott, Carrington_The SAGE handbook of social network analysis. In SAGE Publications Ltd.
Chatfield, A. T., & Brajawidagda, U. (2012). Twitter tsunami early warning network: A social network analysis of twitter information flows. ACIS 2012 : Proceedings of the 23rd Australasian Conference on Information Systems.
Cheng, I. K., Heyl, J., Lad, N., Facini, G., & Grout, Z. (2021). Evaluation of Twitter data for an emerging crisis: an application to the first wave of COVID-19 in the UK. Scientific Reports, 11(1), 1–13. https://doi.org/10.1038/s41598-021-98396-9
Eriyanto. (2014). Analisis Jaringan Komunikasi: Strategi Baru dalam Penelitian Ilmu Komunikasi dan Ilmu Sosial Lainnya. Prenada Media Group.
Eriyanto. (2019). Hashtags and Digital Movement of Opinion Mobilization : A Social Network Analysis / SNA Study on # BubarkanKPAI vs # KamiBersamaKPAI Hashtags. Jurnal Komunikasi Indonesia, VIII(3).
ESDM. (2023). Kejadian Gempa Bumi Merusak Di Indonesia Tahun 2022. 02 Januari 2023. https://vsi.esdm.go.id/index.php/kegiatan-pvmbg/kegiatan-diseminasi-informasi/4041-kejadian-gempa-bumi-merusak-di-indonesia-tahun-2022
Fahriyani, S., & Harmaningsih, D. (2019). Penggunaan Media Sosial Twitter Untuk Mitigasi Bencana Di Indonesia. Journal Sosial Dan Humaira, 4(2), 56–65. https://journals.upi-yai.ac.id/index.php/ikraith-humaniora/article/download/556/408/
Gunawibawa, E. Y., & Oktiani, H. (2020). POLITIK & BENCANA BANJIR JAKARTA 2020 : ANALISIS PETA PERCAKAPAN #JakartaBanjir. Expose: Jurnal Ilmu Komunikasi, 3(1), 60. https://doi.org/10.33021/exp.v3i1.989
Hamid, N. (2020). Kesiapsiagaan Masyarakat dalam Menghadapi Gempa Bumi (Mengenang 14 Tahun Silam Gempa Bumi Bantul, Yogyakarta). Altruis: Journal of Community Services, 1(2), 81. https://doi.org/10.22219/altruis.v1i2.12184
Imran, M., Castillo, C., Lucas, J., Meier, P., & Vieweg, S. (2014). AIDR: Artificial intelligence for disaster response. WWW 2014 Companion - Proceedings of the 23rd International Conference on World Wide Web, 159–162. https://doi.org/10.1145/2567948.2577034
Jacomy, M., Heymann, S., Venturini, T., & Bastian, M. (2012). ForceAtlas2, A Continuous Graph Layout Algorithm for Handy Network Visualization. Medialab.Sciences-Po.Fr, 1–22. http://medialab.sciences-po.fr/publications/Jacomy_Heymann_Venturini-Force_Atlas2.pdf
Kaur, A. (2019). Analyzing Twitter Feeds to Facilitate Crises Informatics and Analyzing Twitter Feeds to Facilitate Crises Informatics and Disaster Response During Mass Emergencies Disaster Response During Mass Emergencies [Technological University Dublin]. https://arrow.tudublin.ie/scschcomdis
Khairunnisa, Y., Aulia, T., Ompusunggu, E. J., & Charisma, T. (2022). Peran aktor Twitter pada gerakan tagar # unsubscribepodcastcorbuzier. 6(November), 1041–1057. https://doi.org/10.25139/jsk.v6i3.4993
Kim, J., & Hastak, M. (2018). Social network analysis: Characteristics of online social networks after a disaster. International Journal of Information Management, 38(1), 86–96. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2017.08.003
Kostkova, P., Szomszor, M., & St. Louis, C. (2014). #Swineflu : The Use of Twitter as an Early Warning and Risk Communication Tool in the 2009 Swine Flu Pandemic. ACM Transactions on Management Information Systems, 5(2), 1–25. https://doi.org/10.1145/2597892
Landwehr, P. M., Wei, W., Kowalchuck, M., & Carley, K. M. (2016). Using tweets to support disaster planning, warning and response. Safety Science, 90, 33–47. https://doi.org/https://doi.org/10.1016/j.ssci.2016.04.012
Lestari, P., & Rinasti, H. A. (2020). Menyingkap Perspektif Tunggal Dalam Komunikasi Bencana Erupsi Gunung Sinabung Melalui Detik.Com. Bricolage : Jurnal Magister Ilmu Komunikasi, 6(01), 117. https://doi.org/10.30813/bricolage.v6i01.2071
Mbaru, E. K., & Barnes, M. L. (2017). Identify Critical Injection Points. Biological Conservation, 210(November 2016), 222–232. https://doi.org/10.1016/j.biocon.2017.03.031
Mulyani, H. S., Bakry, G. N., & Kusmayadi, I. M. (2022). STORYTELLING WITH NETWORK DATA VISUALIZATION HASHTAG #PRAYFORTURKEY ON TWITTER. Journal of New Zealand Studies NS34, 34, 366–381. https://doi.org/https://doi.org/ 10.5281/zenodo.7306374
Nandiwardhana, B. G., Pratama, B. I., & Tamitiadini, D. (2019). Study of Communication Network Travel-Related eWOM on Instagram Social Media. Journal Pekommas, 4(1), 97. https://doi.org/10.30818/jpkm.2019.2040110
Priadi, R., Wijaya, A., Pasaribu, M. A., Yulinda, R., Tinggi, S., Klimatologi, M., & Geofisika, D. (2019). Analysis of the Donggala-Palu Tsunami Characteristics based on Rupture Duration (T dur ) and Active Fault Orientation using the HC-plot Method Analisa karakteristik tsunami Donggala-Palu berdasarkan rupture duration (T dur ) dan orientasi sesar aktif meng. 17(01), 16–20.
Priyanto, & Farida, N. (2021). Jaringan Sosial Komunikasi Pemasaran Traveloka Di Twitter. Mediakom : Jurnal Ilmu Komunikasi, 5(2), 123–137. https://doi.org/10.35760/mkm.2021.v5i2.2402
Ridho Fariz, T., Suhardono, S., & Verdiana, S. (2021). Pemanfaatan Data Twitter Dalam Penanggulangan Bencana Banjir dan Longsor Use of Twitter Data in Flood and Landslide Disaster Management. Cogito Smart Journal |, 7(1), 135–147.
Rosenbaum, J. E., & Bouvier, G. (2020). Twitter, social movements and the logic of connective action: Activism in the 21st century – an introduction. Participation: Journal of Audience & Reception Studies, 17(1), 120–125.
Rousydiy, M. (2020). Patterns of Organizational Communication To Effect the Distribution of Information. Sarwah, 18(1), 49–70.
Safitri, Y., Angeline, M., & Wibowo, D. (2021). Tweeps and their tweeting behavior during natural disaster. IOP Conference Series: Earth and Environmental Science, 729(1). https://doi.org/10.1088/1755-1315/729/1/012083
Sari, M. R., & Dwiyanti, K. T. (2018). Teori Graf Dalam Analisis Jejaring Sosial: Hubungan Aktor Utama Dengan Pengguna Internal Laporan Keuangan. Jurnal Akuntansi Dan Keuangan Indonesia, 15(1), 21–35. https://doi.org/10.21002/jaki.2018.02
Selamet, I. (2022). Bupati Cianjur: Total Korban Meninggal Akibat Gempa Sekitar 600 Orang. Detik.Com. https://news.detik.com/berita/d-6457252/bupati-cianjur-total-korban-meninggal-akibat-gempa-sekitar-600-orang
Soumokil, O. V., Manongga, D., & Hendry. (2013). Pengaruh Sentralitas Aktor dalam Jaringan Sosial Game Online Massive Multiplayer Online Role Playing Game Menggunakan Social Network Analysis. Jurnal Teknologi Informasi Dan Komunikasi, 2013(Sentika). https://fti.uajy.ac.id/sentika/publikasi/makalah/2013/2013_18.pdf
Stowe, K., Paul, M., Palmer, M., Palen, L., & Anderson, K. (2016). Identifying and Categorizing Disaster-Related Tweets. EMNLP 2016 - Conference on Empirical Methods in Natural Language Processing, Proceedings of the 4th International Workshop on Natural Language Processing for Social Media, SocialNLP 2016, 1–6. https://doi.org/10.18653/v1/w16-6201
Wang, B., & Zhuang, J. (2017). Crisis information distribution on Twitter: a contentanalysis of tweets during Hurricane Sandy. Natural Hazards, 89. https://doi.org/DOI 10.1007/s11069-017-2960-x
Zhang, J., & Luo, Y. (2017). Degree Centrality, Betweenness Centrality, and Closeness Centrality in Social Network. Proceedings of the 2017 2nd International Conference on Modelling, Simulation and Applied Mathematics (MSAM2017), 300–303. https://doi.org/https://doi.org/10.2991/msam-17.2017.68
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