#PrayForKanjuruhan On Twitter: Public Response to the Kanjuruhan Stadium Disaster

Gusti Naufal Rizky Perdana; Bambang Irawan; Paisal Akbar

doi:10.31000/nyimak.v7i1.7209

#PrayForKanjuruhan On Twitter: Public Response to the Kanjuruhan Stadium Disaster

Gusti Naufal Rizky Perdana, Bambang Irawan, Paisal Akbar

Abstract

Social media platforms, such as Twitter, serve as vital channels for disseminating information and expressing opinions on various issues. This research focuses on analyzing the hashtag #PrayForKanjuruhan on Twitter, which emerged in response to the tragic riot at Kanjuruhan Stadium that resulted in numerous fatalities. The study aims to assess the intensity of the conversations associated with this hashtag and to examine the key issues and criticisms expressed within these discussions. Employing a qualitative methodology with the Qualitative Data Analysis (QDA) Miner approach and utilizing Nvivo 12 Plus software, this research conducts network, content, and cloud analyses to understand the discourse surrounding #PrayForKanjuruhan. The results reveal a high level of conversation intensity, reflecting diverse responses from various groups. The predominant focus in the hashtag discussions is on expressing concern for the victims of the tragedy, rather than criticizing the individuals or entities involved in the incident. The findings underscore Twitter’s effectiveness as a platform for users to voice their reactions and engage in discourse related to the Kanjuruhan Stadium disaster. By providing a space for both emotional support and critical discussion, Twitter plays a significant role in shaping public response to such crises.

Keywords: Public response, social media, Kanjuruhan Stadium disaster

ABSTRAK

Platform media sosial, seperti Twitter, berfungsi sebagai saluran penting untuk menyebarkan informasi dan mengekspresikan pendapat tentang berbagai isu. Penelitian ini berfokus pada analisis tagar #PrayForKanjuruhan di Twitter, yang muncul sebagai respons terhadap kerusuhan tragis di Stadion Kanjuruhan yang mengakibatkan banyak korban jiwa. Studi ini bertujuan untuk menilai intensitas percakapan yang terkait dengan tagar tersebut serta mengkaji isu-isu kunci dan kritik yang diungkapkan dalam diskusi tersebut. Dengan menggunakan metodologi kualitatif melalui pendekatan Qualitative Data Analysis (QDA) Miner dan memanfaatkan perangkat lunak Nvivo 12 Plus, penelitian ini melakukan analisis jaringan, konten, dan cloud untuk memahami wacana yang berkembang di sekitar #PrayForKanjuruhan. Hasil penelitian mengungkapkan tingginya intensitas percakapan, mencerminkan beragam respons dari berbagai kelompok. Fokus utama dalam diskusi tagar ini adalah menyampaikan kepedulian terhadap para korban tragedi, bukan mengkritik individu atau pihak yang terlibat dalam insiden tersebut. Temuan ini menegaskan efektivitas Twitter sebagai platform bagi pengguna untuk menyuarakan reaksi mereka dan terlibat dalam wacana terkait bencana di Stadion Kanjuruhan. Dengan menyediakan ruang untuk dukungan emosional dan diskusi kritis, Twitter memainkan peran penting dalam membentuk respons publik terhadap krisis semacam ini.

Kata Kunci: Respon public, media social, bencana Stadion Kanjuruhan

Full Text:

PDF

References

Aisyah, I., & Hasfi, N. (2022). Opini Publik Dalam Gerakan Tagar #percumalaporpolisi Di Media Sosial Twitter. Interaksi Online, 10(3), 605-618.

Alexandre Huang, Z., & Wang, R. (2019). Building a Network to "Tell China Stories Well": Chinese Diplomatic Communication Strategies on Twitter. International Journal of Communication, 13, 2984-3007.

Anson, S., Watson, H., Wadhwa, K., & Metz, K. (2017). Analyzing social media data for disaster preparedness: Understanding the opportunities and barriers faced by humanitarian actors. International Journal of Disaster Risk Reduction, 21, 131-139. https://doi.org/10.1016/j.ijdrr.2016.11.014

Azmi, N. A., Fathani, A. T., Sadayi, D. P., & Fitriani, I. (2021). Social Media Network Analysis ( SNA )/ : Identifikasi Komunikasi dan Penyebaran Informasi Melalui Media Sosial Twitter. Jurnal Media Informatika Budidarma, 5(4), 1422-1430. https://doi.org/10.30865/mib.v5i4.3257

Baharuddin, T., Salahudin, S., Sairin, S., Qodir, Z., & Jubba, H. (2021). Kampanye Antikorupsi Kaum Muda melalui Media Sosial Twitter. Jurnal Ilmu Komunikasi, 19(1), 58. https://doi.org/10.31315/jik.v19i1.3827

Bogen, K. W., Bleiweiss, K. K., Leach, N. R., & Orchowski, L. M. (2021). #MeToo: Disclosure and Response to Sexual Victimization on Twitter. Journal of Interpersonal Violence, 36(17-18), 8257-8288. https://doi.org/10.1177/0886260519851211

Bogen, K. W., Mulla, M. M. M., Haikalis, M., & Orchowski, L. M. (2022). Sexual Victimization Among Men: A Qualitative Analysis of the Twitter Hashtag #UsToo. Journal of Interpersonal Violence, 37(9-10), 1-25. https://doi.org/10.1177/0886260520967167

Brandão, C. (2015). P. Bazeley and K. Jackson, Qualitative Data Analysis with NVivo (2nd ed.) . Qualitative Research in Psychology, 12(4), 492-494. https://doi.org/10.1080/14780887.2014.992750

Buntoro, K., Suswanta, S., Nurmandi, A., Setiawan, A., & Saputra, H. A. (2021). Twitter Media Activities: Virtual Activism Related To China Uighur Muslim Problems. Jurnal Tarbiyatuna, 12(1), 1-18. https://doi.org/10.31603/tarbiyatuna.v12i1.4115

CNN Indonesia. (2022). Warganet Berbelasungkawa, Tagar Pray For Kanjuruhan Menggema. https://www.cnnindonesia.com/teknologi/20221003094156-192-855554/warganet-berbelasungkawa-tagar-pray-for-kanjuruhan-menggema

Darwis, D., Siskawati, N., & Abidin, Z. (2021). Penerapan Algoritma Naive Bayes Untuk Analisis Sentimen Review Data Twitter Bmkg Nasional. Jurnal Tekno Kompak, 15(1), 131. https://doi.org/10.33365/jtk.v15i1.744

Detik News. (2022). Korban Tewas Tragedi Kanjuruhan Bertambah Jadi 132 Orang. https://news.detik.com/berita/d-6341986/korban-tewas-tragedi-kanjuruhan-bertambah-jadi-132-orang

Diba, F., Ichsan, I., Muhsin, M., Marthoenis, M., Sofyan, H., Andalas, M., Monfared, I., Richert, K., Kaplan, L., Rogge, L., Doria, S., Samadi, S., & Vollmer, S. (2019). Healthcare providers' perception of the referral system in maternal care facilities in Aceh, Indonesia: A cross-sectional study. BMJ Open, 9(12), 1-8. https://doi.org/10.1136/bmjopen-2019-031484

Fang, J., Hu, J., Shi, X., & Zhao, L. (2019). Assessing disaster impacts and response using social media data in China: A case study of 2016 Wuhan rainstorm. International Journal of Disaster Risk Reduction, 34(December 2018), 275-282. https://doi.org/10.1016/j.ijdrr.2018.11.027

Fiqri, U. P. (2020). Dialog Humor Antar Agama Dan Politik Pada Akun Twitter @Nugarislucu @Eko_kuntadhi. Jurnal Riset Mahasiswa Dakwah Dan Komunikasi, 2(1), 15-25. https://ejournal.uin-suska.ac.id/index.php/jrmdk/article/view/8815/6106

Fitriani, L., Pawito, P., & Utari, P. (2022). The analysis of the hashtag #Jokowi404NotFound on Twitter in protesting mural removal in the public sphere. Informasi, 51(2), 281-304. https://doi.org/10.21831/informasi.v51i2.45068

García-Ramírez, G. M., Bogen, K. W., Rodríguez-Guzmán, V. M., Nugent, N., & Orchowski, L. M. (2021). #4645Boricuas: Twitter reactions to the estimates of deaths by Hurricane María in Puerto Rico. Journal of Community Psychology, 49(3), 768-790. https://doi.org/10.1002/jcop.22295

Gargiulo, F., Cafiero, F., Guille-Escuret, P., Seror, V., & Ward, J. K. (2020). Asymmetric participation of defenders and critics of vaccines to debates on French-speaking Twitter. Scientific Reports, 10(1), 1-12. https://doi.org/10.1038/s41598-020-62880-5

George Efthimion, P., Payne, S., Proferes, N., Efthimion, P. G., & Proferes, N. (2018). Supervised Machine Learning Bot Detection Techniques to Identify Social Twitter Bots. SMU Data Science Review, 1(2).

Glowacki, E. M., Lazard, A. J., Wilcox, G. B., Mackert, M., & Bernhardt, J. M. (2016). Identifying the public's concerns and the Centers for Disease Control and Prevention's reactions during a health crisis: An analysis of a Zika live Twitter chat. American Journal of Infection Control, 44(12), 1709-1711. https://doi.org/10.1016/j.ajic.2016.05.025

Gorodnichenko, Y., Pham, T., & Talavera, O. (2021). Social media , sentiment and public opinions/ : Evidence from # Brexit and # USElection. European Economic Review, 136, 103772. https://doi.org/10.1016/j.euroecorev.2021.103772

Hermida, A., Lewis, S. C., & Zamith, R. (2014). Sourcing the Arab spring: A case study of Andy Carvin's sources on twitter during the Tunisian and Egyptian revolutions. Journal of Computer-Mediated Communication, 19(3), 479-499. https://doi.org/10.1111/jcc4.12074

Himelboim, I., Smith, M., & Shneiderman, B. (2013). Tweeting Apart: Applying Network Analysis to Detect Selective Exposure Clusters in Twitter. Communication Methods and Measures, 7(3), 169-197. https://doi.org/10.1080/19312458.2013.813922

Irawan, A. W., Yusufianto, A., Agustina, D., & Dean, R. (2020). Laporan survei internet APJII 2019 - 2020 (Q20. Asosiasi Penyelenggara Jasa Internet Indonesia, 2020. https://apjii.or.id/survei

Irawan, B. (2022). Policies for controlling the covid-19 pandemic through social media communications by the East Kalimantan provincial government. International Journal of Communication and Society, 4(1), 125-136. http://pubs2.ascee.org/index.php/IJCSIJCS@ascee.org

Isa, D., & Himelboim, I. (2018). A Social Networks Approach to Online Social Movement: Social Mediators and Mediated Content in #FreeAJStaff Twitter Network. Social Media and Society, 4(1). https://doi.org/10.1177/2056305118760807

Jamil, A. (2018). Social Movements in Framing Perspectives: A Study on Corruption Case Issues in Indonesia. Jurnal Komunikasi Indonesia, 7(2). https://doi.org/10.7454/jki.v7i2.9989

Klingeren, M. Van, Trilling, D., & Möller, J. (2020). Public opinion on Twitter/ ? How vote choice and arguments on Twitter comply with patterns in survey data , evidence from the 2016 Ukraine referendum in the Netherlands. Acta Politica, 56, 436-455. https://doi.org/10.1057/s41269-020-00160-w

Loilatu, M. J., Irawan, B., Salahudin, S., & Sihidi, I. T. (2021). Analysis of Twitter's Function as a Media communication of Public Transportation. Jurnal Komunikasi, 13(1), 54. https://doi.org/10.24912/jk.v13i1.8707

Makmun, S., & Rohim. (2021). Advokasi Kelompok Disabilitas Melalui Media Sosial. Majalah Ilmiah Dian Ilmu, 20(2), 165-181. https://doi.org/10.37849/midi.v20i2.228

Maulana, A., Kusumasari, B., & Giyarsih, S. R. (2021). Komunikasi Bencana di Twitter: Studi Kasus Bencana Banjir Perkotaan di Daerah Khusus Ibukota (DKI) Jakarta'. Jurnal Kawistara, 11(2), 129. https://doi.org/10.22146/kawistara.v11i2.58123

Mueller, A., Wood-Doughty, Z., Amir, S., Dredze, M., & Nobles, A. L. (2021). Demographic Representation and Collective Storytelling in the Me Too Twitter Hashtag Activism Movement. Proceedings of the ACM on Human-Computer Interaction, 5(CSCW1), 1-28. https://doi.org/10.1145/3449181

Nofrima, S., Nurmandi, A., Kusuma Dewi, D., & Salahudin, S. (2020). Cyber-activism on the dissemination of #Gejayanmemanggil: Yogyakarta's student movement. Jurnal Studi Komunikasi (Indonesian Journal of Communications Studies), 4(1), 103. https://doi.org/10.25139/jsk.v4i1.2091

Orminski, J., Jr, E. C. T., Detenber, B. H., & Jr, E. C. T. (2021). # sustainablefashion - A Conceptual Framework for Sustainable Fashion Discourse on Twitter. Environmental Communication, 15(1), 115-132. https://doi.org/10.1080/17524032.2020.1802321

Puspita, R. S. D., & Gumelar, G. (2014). Pengaruh Empati Terhadap Perilaku Prososial Dalam Sosial Di Jejaring Sosial Twitter. Jurnal Penelitian Dan Pengukuran Psikologi, 3(1), 1-7.

Rachman, F. F., Nooraeni, R., & Yuliana, L. (2021). Public Opinion of Transportation integrated ( Jak Lingko ) in DKI Jakarta Indonesia. Procedia Computer Science, 179, 696-703. https://doi.org/10.1016/j.procs.2021.01.057

Rahmawati, D. (2014). Media Sosial Dan Demokrasi Di Era Informasi. Jurnal Vokasi Indonesia, 2(2). http://dx.doi.org/10.7454/jvi.v2i2.40

Rakhman, F. R., Ramadhani, R. W., & Fathoni, A. (2021). Digital Movement of Opinion# IndonesiaTerserah on Social Media Twitter in The Covid-19 Pandemic. Jurnal Penelitian Komunikasi, 24(1), 29-44. https://doi.org/10.20422/jpk.v24i1.752

Saini, S., Punhani, R., Bathla, R., & Shukla, V. K. (2019). Sentiment Analysis on Twitter Data using R. 2019 International Conference on Automation, Computational and Technology Management, ICACTM 2019, 68-72. https://doi.org/10.1109/ICACTM.2019.8776685

Scarborough, W. J. (2018). Feminist Twitter and Gender Attitudes/ : Opportunities and Limitations to Using Twitter in the Study of Public Opinion. Socius, 4, 1-16. https://doi.org/10.1177/2378023118780760

Shapiro, G. K., Surian, D., Dunn, A. G., Perry, R., & Kelaher, M. (2017). Comparing human papillomavirus vaccine concerns on Twitter: A cross-sectional study of users in Australia, Canada and the UK. BMJ Open, 7(10), 1-10. https://doi.org/10.1136/bmjopen-2017-016869

Shen, F., Xia, C., & Skoric, M. (2020). Examining the roles of social media and alternative media in social movement participation: A study of Hong Kong's Umbrella Movement. Telematics and Informatics, 47, 101303. https://doi.org/10.1016/j.tele.2019.101303

Signorini, A., Segre, A. M., & Polgreen, P. M. (2011). The use of Twitter to track levels of disease activity and public concern in the U.S. during the influenza A H1N1 pandemic. PLoS ONE, 6(5). https://doi.org/10.1371/journal.pone.0019467

Silver, C., & Lewins, A. (2007). QDA Miner 3 . 2 ( with WordStat & Simstat ) Distinguishing features and functions. Database, 2.

Sotiriadou, P., Brouwers, J., & Le, T. A. (2014). Choosing a qualitative data analysis tool: A comparison of NVivo and Leximancer. Annals of Leisure Research, 17(2), 218-234. https://doi.org/10.1080/11745398.2014.902292

Sukarno, M., & Nur, U. (2022). Public Response On Social Media Narration (Case Study: #PercumaLaporPolisi). International Conference on Government Education Management and Tourism, 1, 1-5.

Suratnoaji, C., & Arianto, I. D. (2020). Public Opinion on Lockdown ( PSBB ) Policy in Overcoming COVID-19 Pandemic in Indonesia/ : Analysis Based on Big Data Twitter. Asian Journal For Public Opinion Research, 8(3), 393-406. http://dx.doi.org/10.15206/ajpor.2020.8.3.393

Takahashi, B., Tandoc, E. C., & Carmichael, C. (2015). Communicating on Twitter during a disaster: An analysis of tweets during Typhoon Haiyan in the Philippines. Computers in Human Behavior, 50, 392-398. https://doi.org/10.1016/j.chb.2015.04.020

Thelwall, M., & Kousha, K. (2021). Researchers' attitudes towards the h-index on Twitter 2007-2020: criticism and acceptance. Scientometrics, 126(6), 5361-5368. https://doi.org/10.1007/s11192-021-03961-8

Tribun News. (2022). Kronologi Kasus Tragedi Stadion Kanjuruhan versi Polisi. https://www.tribunnews.com/nasional/2022/10/07/kronologi-kasus-tragedi-stadion-kanjuruhan-versi-polisi

Vaccari, C., & Valeriani, A. (2018). Digital Political Talk and Political Participation/ : Comparing Established and Third Wave Democracies. Sage Open, 8(2), 1-14. https://doi.org/10.1177/2158244018784986

Wang, R., Liu, W., & Gao, S. (2016). Hashtags and information virality in networked social movement: Examining hashtag co-occurrence patterns. Online Information Review, 40(7), 850-866. https://doi.org/10.1108/OIR-12-2015-0378

Weller, K., Bruns, A., Burgess, J., Mahrt, M., & Puschmann, C. (2014). Twitter and Society. The Journal of Media Innovations, 1(1), 134-137. https://doi.org/10.5617/jmi.v1i1.825

Woolf, N. H., & Silver, C. (2017). Qualitative analysis using MAXQDA: The five-level QDA® method. In Qualitative Analysis Using MAXQDA: The Five-Level QDA Method. Routledge. https://doi.org/10.4324/9781315268569

Xiong, Y., Cho, M., & Boatwright, B. (2019). Hashtag activism and message frames among social movement organizations: Semantic network analysis and thematic analysis of Twitter during the #MeToo movement. Public Relations Review, 45(1), 10-23. https://doi.org/10.1016/j.pubrev.2018.10.014

Zappavigna, M., & Martin, J. R. (2018). #Communing affiliation: Social tagging as a resource for aligning around values in social media. Discourse, Context and Media, 22, 4-12. https://doi.org/10.1016/j.dcm.2017.08.001

Zou, L., Lam, N. S. N., Cai, H., & Qiang, Y. (2018). Mining Twitter Data for Improved Understanding of Disaster Resilience. Annals of the American Association of Geographers, 108(5), 1422-1441. https://doi.org/10.1080/24694452.2017.1421897

DOI: http://dx.doi.org/10.31000/nyimak.v7i1.7209