Identifikasi Pengaruh Pandemi Covid-19 terhadap Perilaku Pengguna Twitter dengan Pendekatan Social Network Analysis

Penulis

Diana Purwitasari, Apriantoni Apriantoni, Agus Budi Raharjo

Abstrak

Pandemi COVID-19 yang berlangsung lama telah berdampak masif pada berbagai aktivitas publik, misalnya perilaku pengguna di media sosial. Twitter, media sosial yang fleksibel untuk berdiskusi dan bertukar pendapat, menjadi salah satu media populer dalam menyebarluaskan informasi COVID-19 secara dinamis dan up-to-date. Hal ini menjadikan twitter relevan sebagai media ekstraksi pengetahuan dalam mengidentifikasi perubahan perilaku pengguna. Kontribusi penelitian ini adalah menemukan perubahan perilaku pengguna twitter melalui analisis profil pengguna pada periode sebelum dan setelah COVID-19. Data yang digunakan adalah data tweet berbahasa Indonesia. Penelitian ini menggunakan pendekatan Social Network Analysis (SNA) sebagai ekstraksi informasi dalam menentukan aktor utama dan aktor populer. Kemudian, profil pengguna aktif dianalisis untuk mengidentifikasi perubahan perilaku melalui intensitas tweet, popularitas pengguna, dan representasi topik pembahasan. Popularitas pengguna dianalisis dengan pendekatan follower rank, sedangkan representasi topik pembahasan diekstraksi dengan metode Latent Dirichlet Allocation untuk mendapatkan dominan topik yang dibahas oleh setiap pengguna aktif. Tujuannya adalah untuk mempermudah  identifikasi pengaruh pandemi COVID-19 terhadap perubahan perilaku pengguna twitter. Berdasarkan hasil SNA, penelitian ini menemukan tiga aktor  kunci yang aktif pada periode sebelum dan setelah COVID-19. Selanjutnya, hasil analisis dari ketiga aktor tersebut menunjukkan adanya pengaruh pandemi COVID-19 terhadap perubahan perilaku pengguna twitter, yaitu kenaikan intensitas tweet sebesar 58% pada jam kerja, aktor utama yang didominasi oleh 60% pengguna dengan follower rendah, dan topik pembicaraan pengguna twitter yang dominan membahas COVID-19, hobi dan aktivitas di dalam rumah.

 

Abstract


The long-lasting COVID-19 pandemic had a massive impact on public activities, such as user behavior on social media. Twitter, a flexible social media for discussing and exchanging opinions, has become popular in disseminating COVID-19  dynamic and up-to-date information. It makes twitter relevant as a medium of knowledge extraction in identifying user behavior changes. The contribution of this research is to find behavior changes of Twitter users through user profiles analysis in the before and after COVID-19 period. This data used is Indonesian-language tweets. This research used a Social Network Analysis (SNA) to determine the main actors and famous actors. Then, active user profiles were analyzed to identify behavior changes through tweet intensity, user popularity, and representation of the topic of discussion. User popularity was analyzed using a follower rank approach. At the same time, the representation of discussion topics was extracted using the Latent Dirichlet Allocation method to obtain dominant topics which each active user discusses. It aims to make it easier to identify the impact of the COVID-19 pandemic on Twitter user behavior changes. Based on the results of the SNA, this research found three key actors who were active in the before and after COVID-19 period. Then, the results of the analysis of these three user profiles shows that an influence of the COVID-19 pandemic on Twitter user behavior changes: an increase in tweet intensity by 58% during working hours, the leading actor was dominated by 60% of users with low followers, and the topic of Twitter users' conversation that it dominantly discuss COVID-19 issues, hobbies, and activities at home.


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Referensi


ABD-ALRAZAQ, A., ALHUWAIL, D., HOUSEH, M., HAI, M., & SHAH, Z. (2020). Top concerns of tweeters during the COVID-19 pandemic: A surveillance study. Journal of Medical Internet Research, 22(4), 1–9. https://doi.org/10.2196/19016

ABDELSADEK, Y., CHELGHOUM, K., HERRMANN, F., KACEM, I., & OTJACQUES, B. (2018). Community extraction and visualization in social networks applied to Twitter. Information Sciences, 424, 204–223. https://doi.org/10.1016/j.ins.2017.09.022

AHMED, M. S., AURPA, T. T., & ANWAR, M. M. (2021). Detecting sentiment dynamics and clusters of Twitter users for trending topics in COVID-19 pandemic. PLoS ONE.

https://doi.org/10.1371/journal.pone.0253300

AHMED, W., VIDAL-ALABALL, J., SEGUI, F. L., & MORENO-SÁNCHEZ, P. A. (2020). A social network analysis of tweets related to masks during the covid-19 pandemic. International Journal of Environmental Research and Public Health, 17(21), 1–9. https://doi.org/10.3390/ijerph17218235

AL-SHARGABI, A. A., & SELMI, A. (2021). Social Network Analysis and Visualization of Arabic Tweets during the COVID-19 Pandemic. IEEE. https://doi.org/10.1109/ACCESS.2021.3091537

BASIRI, M. E., NEMATI, S., ABDAR, M., ASADI, S., & ACHARRYA, U. R. (2021). A novel fusion-based deep learning model for sentiment analysis of COVID-19 tweets. Knowledge-Based Systems, 228, 107242. https://doi.org/10.1016/j.knosys.2021.107242

COSTA, G., & ORTALE, R. (2021). Jointly modeling and simultaneously discovering topics and clusters in text corpora using word vectors. Information Sciences, 563, 226–240. https://doi.org/10.1016/j.ins.2021.01.019

HAUPT, M. R., JINICH-DIAMANT, A., LI, J., NALI, M., & MACKEY, T. K. (2021). Characterizing twitter user topics and communication network dynamics of the “Liberate” movement during COVID-19 using unsupervised machine learning and social network analysis. Online Social Networks and Media, 21(December 2020). https://doi.org/10.1016/j.osnem.2020.100114

HUNG, M., LAUREN, E., HON, E. S., BIRMINGHAM, W. C., XU, J., SU, S., … LIPSKY, M. S. (2020). Social network analysis of COVID-19 sentiments: Application of artificial intelligence. Journal of Medical Internet Research, 22(8), 1–13. https://doi.org/10.2196/22590

LOSSIO-VENTURA, J. A., GONZALES, S., MORZAN, J., ALATRISTA-SALAS, H., HERNANDEZ-BOUSSARD, T., & BIAN, J. (2021). Evaluation of clustering and topic modeling methods over health-related tweets and emails. Artificial Intelligence in Medicine, 117(March), 102096. https://doi.org/10.1016/j.artmed.2021.102096

MAILOA, E. (2020). Analisis Node dengan Centrality dan Follower Rank pada Twitter. Jurnal RESTI (Rekayasa Sistem Dan Teknologi Informasi), 4(5), 937–942. https://doi.org/10.29207/resti.v4i5.2398

MARTIN, C., & NIEMEYER, P. (2020). On the impact of network size and average degree on the robustness of centrality measures.

Network Science, (2020), 1–22. https://doi.org/10.1017/nws.2020.37

MITTAL, D., SUTHAR, P., PATIL, M., PRANAYA, P. G. S., RANA, D. P., & TIDKE, B. (2020). Social Network Influencer Rank Recommender Using Diverse Features from Topical Graph. Procedia Computer Science, 167(2019), 1861–1871.

https://doi.org/10.1016/j.procs.2020.03.205

PASCUAL-FERRÁ, P., ALPERSTEIN, N., & BARNETT, D. J. (2020). Social Network Analysis of COVID-19 Public Discourse on Twitter: Implications for Risk Communication. Disaster Medicine and Public Health Preparedness, 1–9. https://doi.org/10.1017/dmp.2020.347

STOLZ, S., & SCHLERETH, C. (2021). Predicting Tie Strength with Ego Network Structures. Journal of Interactive Marketing, 54, 40–52. https://doi.org/10.1016/j.intmar.2020.10.001

TOMASOA, L., IRIANI, A., & SEMBIRING, I. (2019). Ekstraksi Knowledge tentang Penyebaran #Ratnamiliksiapa pada Jejaring Sosial (Twitter) menggunakan Social Network Analysis (SNA). Jurnal Teknologi Informasi Dan Ilmu Komputer, 6(6), 677. https://doi.org/10.25126/jtiik.2019661710

VALDEZ, D., TEN THIJ, M., BATHINA, K., RUTTER, L. A., & BOLLEN, J. (2020). Social media insights into US mental health during the COVID-19 pandemic: Longitudinal analysis of twitter data. Journal of Medical Internet Research, 22(12). https://doi.org/10.2196/21418

YAO, Q., YI, R., LI, M., SONG, L., & CRABBE, M. J. C. (2021). Safety knowledge sharing on Twitter : A social network analysis. Safety Science, 143(April), 105411. https://doi.org/10.1016/j.ssci.2021.105411

YIP, W. S., & TO, S. (2021). Identification of stakeholder related barriers in sustainable manufacturing using Social Network Analysis. Sustainable Production and Consumption, 27, 1903–1917. https://doi.org/10.1016/j.spc.2021.04.018

ZHAO, G., LOU, P., QIAN, X., & HOU, X. (2020). Personalized location recommendation by fusing sentimental and spatial context. Knowledge-Based Systems, 196, 105849. https://doi.org/10.1016/j.knosys.2020.105849




DOI: http://dx.doi.org/10.25126/jtiik.2021865213