Article Sidebar

Download
PDF
Statistic
Read Counter : 212 Download : 161

Main Article Content

Abstract

The presence of threads has enhanced users' ability to share stories, information and views in greater detail and effectively in the online world. Social media platforms allow users to interact, share content, and connect online. Therefore, the purpose of this research is to create a Social Network Analysis (SNA) network visualization and categorize the data to contribute to the understanding of user interaction on this platform. The research method in this study is the Social Network Analysis (SNA) approach to analyze the relationship between individuals in social networks. The results show that user participation in the Threads platform has a significant positive impact. The data shows a high level of participation, providing an understanding of user engagement in various topics. Social network analysis revealed characteristics of user interactions, such as the number of nodes, average connectedness, and relationship complexity. In addition, word categories and network visualizations provide insights into relationship patterns and topics in conversations.

Keywords

threads twitter media social social network analysis

Article Details

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

By exercising the Licensed Rights, You accept and agree to be bound by the terms and conditions of this Creative Commons Attribution-NonCommercial 4.0 International Public License.

How to Cite
Lestari, D. W., & Ambarwati, R. (2024). Social Network Analysis: Understanding User Behavior in Threads. Kontigensi : Jurnal Ilmiah Manajemen, 12(1), 115-124. https://doi.org/10.56457/jimk.v12i1.505

References

  1. Akbar, M. A., Masniarara Aziza Balfas Amril, Raiza Syahira, Fahrein Rachel Latisha, & Noor Jihan. (2022). Analisis Struktur Jaringan Komunikasi #Seagames2022 Di Twitter Menggunakan Pendekatan Social Network Analysis (Sna). Jurnal Studi Komunikasi Dan Media, 26(1), 1–16. https://doi.org/10.17933/jskm.2022.4780
  2. Alisya Putri Rabbani, Andry Alamsyah, & Sri Widiyanesti. (2020). Analisa Interaksi User Di Media Sosial Mengenai Industri Fintech Menggunakan Social Network Analysis (Studi Kasus: Gopay, Ovo Dan Linkaja). Jurnal Mitra Manajemen, 4(3), 341–351. https://doi.org/10.52160/ejmm.v4i3.352
  3. Anjani, S. P., & Ambarwati, R. (2023). Analysis Of The Credibility Of Utilizing Brand Ambassador To Compete Between Shopee And Tokopedia Using Social Network Analysis On Twitter. Jurnal Manajemen, 14(3), 391–404. https://doi.org/10.32832/jm-uika.v14i3.13777
  4. Ariyanti, Y. D. P. (2022). Analisis Centrality Aktor pada Penyebaran Informasi Kuliner di Media Sosial dengan menggunakan Social Network Analysis. Journal of System, Information Technology and Electronics Engineering (J-SITEE), 2(1), 23–31.
  5. Azmi, N. A., Fathani, A. T., Sadayi, D. P., Fitriani, I., & Adiyaksa, M. R. (2021). Social Media Network Analysis (SNA): Identifikasi Komunikasi dan Penyebaran Informasi Melalui Media Sosial Twitter. Jurnal Media Informatika Budidarma, 5(4), 1422. https://doi.org/10.30865/mib.v5i4.3257
  6. Bratawisnu, M. K., & Alamsyah, A. (2019). Social Network Analysis Untuk Analisa Interaksi User Di Media Sosial Mengenai Bisnis E-Commerce. Sosiohumanitas, 21(1), 63–69. https://doi.org/10.36555/sosiohumanitas.v21i1.1000
  7. Bratawisnu, M. K., Putra, M. R. D., & Ignatio, W. (2018). Penentuan Top Brand Menggunakan Social Network Analysis pada e-commerce Tokopedia dan Bukalapak. Journal of Information Engineering and Educational Technology, 2(1), 1. https://doi.org/10.26740/jieet.v2n1.p1-5
  8. Chamila, A. N., & Sukmono, R. A. (n.d.). Analysis Of Twitter User Interactions Using Social Networks At Payday Sale Events On The Marketplace [ Analisis Interaksi Pengguna Twitter Menggunakan Social Network Pada Event Gajian Sale Di Marketplace ]. Universitas Muhammadiyah Sidoarjo, 1–12.
  9. Digital, P. P., Kualitas, P., Citra, D. A. N., Terhadap, M., Pembelian, K., & Lokal, S. (2022). Journal of Applied Business Administration. September, 90–100.
  10. Hadi, S. W., Julianto, M. F., Rahmatullah, S., & Gata, W. (2020). Analisa Cluster Aplikasi Pada App Store Dengan Menggunakan Metode K-Means. Bianglala Informatika, 8(2), 86–90. https://doi.org/10.31294/bi.v8i2.8191
  11. Hadna, N. M. S., Santosa, P. I., & Winarno, W. W. (2016). Studi Literatur Tentang Perbandingan Metode Untuk Proses Analisis Sentimen Di Twitter. Seminar Nasional Teknologi Informasi Dan Komunikasi, 2016(March), 57–64.
  12. Herlinawati, N., Yuliani, Y., Faizah, S., Gata, W., & Samudi, S. (2020). Analisis Sentimen Zoom Cloud Meetings di Play Store Menggunakan Naïve Bayes dan Support Vector Machine. CESS (Journal of Computer Engineering, System and Science), 5(2), 293. https://doi.org/10.24114/cess.v5i2.18186
  13. Hukum, K., Kampung, M., Memiliki, M. U., Atas, S., Ulayat, H., Metodologi, B. I., Pendekatan, P. A., & Penelitian, M. (2013). Metodelogi Penelitian. 66–79.
  14. Mutiara, Z., Antonius, B., & Leviane, J. H. L. (2020). Twitter Sebagai Media Mengungkapkan Diri Pada Kalangan Milenial. Fakultas Ilmu Sosial Dan Politik, Universitas Sam Ratulangi, 1–8. https://ejournal.unsrat.ac.id/index.php/actadiurnakomunikasi/article/view/31979/30381
  15. Navisha, A. N., Ambarwati, R., & Hariasih, M. (2023). Twitter Social Network Interaction As Customer Engagement In Competition For E-Commerce E-Health Performance In Indonesia. Jurnal Manajerial, 10(02), 303. https://doi.org/10.30587/jurnalmanajerial.v10i02.5279
  16. Nishfi, D., Huda, I., & Prianto, C. (2023). Analisis Sentimen Layanan Jasa Pengiriman Pada Ulasan Play Store : Systematic Literature Review. 04(02), 87–98.
  17. Puspitarini, D. S., & Nuraeni, R. (2019). Pemanfaatan Media Sosial Sebagai Media Promosi. Jurnal Common, 3(1), 71–80. https://doi.org/10.34010/common.v3i1.1950
  18. Putri, F. A., Ambarwati, R., & Kusuma, K. A. (2023). Marketing Strategy of Fast-Food Restaurants in Indonesia - Sosial Network Analysis on Twitter. Jurnal Manajerial, 10(3), 561–576. https://doi.org/10.21070/ups.2461
  19. Ragam, E., Digital, K., & Rosadi, N. (n.d.). The Existence of Inlis Variety in Digital Communication in Threads : an Instagram App.
  20. Rhein Rahmahsya Reshany, & Santi Indra Astuti. (2023). Media Sosial Twitter sebagai Sarana Mendiskusikan Kasus Kekerasan Seksual. Bandung Conference Series: Journalism, 3(1), 34–43. https://doi.org/10.29313/bcsj.v3i1.6224
  21. Rofidah, L. (2021). Analisis Komunikasi Konflik Netizen dalam Pemaknaan Feminisme pada “Thread Bekal Buat Suami.” Jurnal Interaksi : Jurnal Ilmu Komunikasi, 5(2), 133–149. https://doi.org/10.30596/interaksi.v5i2.5899
  22. Samrin, N., & Akbar, M. N. (2023). Analisis Sentimen Komentar Pengguna Aplikasi Threads Pada Google Play Store Menggunakan Algoritma Multinomial Naïve Bayes. 3(2), 1–9.
  23. Sandag, G. A. (2020). Prediksi Rating Aplikasi App Store Menggunakan Algoritma Random Forest. CogITo Smart Journal, 6(2), 167–178. https://doi.org/10.31154/cogito.v6i2.270.167-178
  24. Susanto, B., Lina, H., & Chrismanto, A. R. (2012). Penerapan Social Network Analysis dalam Penentuan Centrality Studi Kasus Social Network Twitter. Jurnal Informatika, 8(1). https://doi.org/10.21460/inf.2012.81.111
  25. Watie, E. D. S. (2016). Komunikasi dan Media Sosial (Communications and Social Media). Jurnal The Messenger, 3(2), 69. https://doi.org/10.26623/themessenger.v3i2.270