Jaringan Komunitas Berbasis Similaritas Topik Bahasan dan Emosi untuk Mengidentifikasi Perilaku Pengguna Twitter
DOI:
https://doi.org/10.25126/jtiik.20231016317Abstrak
Pandemi COVID-19 menyebabkan situasi krisis yang berdampak pada perubahan perilaku pengguna Twitter terkait pengalaman distres publik. Perubahan perilaku positif bisa berdampak positif. Namun, perubahan perilaku negatif bisa menjadi masalah jika terjadi secara masif, seperti meningkatnya kecemasan pengguna. Oleh karena itu, mengeksplorasi hubungan antara perilaku dan jaringan komunitas pengguna sangat penting untuk menemukan implikasi pandemi COVID-19 terhadap perubahan perilaku pengguna Twitter. Penelitian ini berkontribusi dalam mengidentifikasi perubahan perilaku pengguna berdasarkan model ekstraksi perilaku kolektif pada aktivitas tweet temporal. Mekanisme ini menggunakan topik bahasan dan emosi sebagai variabel ekstraksi untuk menghasilkan jaringan perilaku pengguna. Kemudian, jaringan perilaku tersebut dimodelkan dengan algoritma DeepWalk Network Embeddings untuk memetakan hubungan kedekatan perilaku antar pengguna dan Density Peak Clustering Algorithm untuk mengelompokkan komunitas pengguna berdasarkan kesamaan perilaku yang kuat. Dari analisis 121 pengguna aktif, periode sebelum COVID-19 memiliki 98 pengguna representatif yang didominasi oleh 33% perilaku komunitas terkait aktivitas pribadi dengan emosi senang. Di sisi lain, periode setelah COVID-19 memiliki 54 pengguna representatif yang didominasi oleh 65% perilaku komunitas terkait kesehatan dengan emosi marah. Perubahan perilaku kedua periode tersebut dipengaruhi oleh transisi pola jaringan terdistribusi ke pola jaringan clique graph, sehingga sentralisasi penyebaran informasi mempengaruhi potensi peningkatan perubahan perilaku pengguna pada jaringan komunitas. Hasil ini dapat digunakan untuk mengurangi potensi penyebaran perilaku negatif dengan memanfaatkan komunitas yang memiliki pengaruh perilaku positif dikalangan pengguna Twitter.
Abstract
The COVID-19 pandemic caused a crisis that impacted behavior changes of Twitter users related to public distress experiences. Positive behavior changes could have a positive impact. However, negative behavior changes could have problems if it occur massively, such as increased user anxiety. Therefore, exploring the relationship between behavior and user community in the social networks is very important to find the implication of the COVID-19 pandemic on behavior changes of Twitter users. This study contributes to identify user behavior changes based on the collective behavior extraction model on temporal tweet activities. This mechanism used discussion topics and emotions as extraction variables to generate user behavior network. Then, the behavioral network was modeled by the DeepWalk Network Embeddings algorithm to map the behavioral closeness relationship between users and the Density Peak Clustering Algorithm to group user communities with strong behavioral similarities. Based on the analysis of 121 active users, before the COVID-19 period had 98 representative users, who were dominated by 33% of community behavior related to personal activities with happy emotions. On the other hand, after the COVID-19 period, 54 representative users were dominated by 65% of community behavior related to health with anger. Behavior changes in both periods are influenced by the transition from a distributed network pattern to a clique graph network pattern, so the centralization of information dissemination could affect the potential for increasing user behavioral changes in the community network. These findings could be used to reduce the potential for spreading negative behavior by leveraging communities with positive behavior influence among Twitter users.
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