Abstract
Post-COVID-19 pandemic has significantly impacted the global economy, resulting in a surge of job losses and layoffs across various industries, including the technology sector. The pandemic has led to changes in consumer behavior, supply chain disruptions, and an overall decrease in demand, all of which have contributed to the current economic situation. With the rise of social media platforms, individuals have been using Twitter to express their thoughts and opinions on the impact of the pandemic on the technology industry, including the increase in job losses and layoffs. In this study, we analyze the characteristics of Twitter users and their text and hashtag usage in the context of the pandemic's impact on the technology industry. We employ topic modeling and k-means clustering to a preprocessed dataset of tweets related to tech layoffs to identify common themes or topics in Twitter users' responses to tech winter layoffs in Indonesia. The analysis revealed a high number of negative tweets expressing anger and sadness. The use of predetermined keywords did not provide a comprehensive understanding of the phenomenon as other topics such as politics, religion, news, and advertisements were prevalent.
Bahasa Abstract
Pasca pandemi COVID-19 telah berdampak signifikan pada ekonomi global, mengakibatkan peningkatan pemutusan hubungan kerja di berbagai industri, termasuk sektor teknologi. Pandemi ini telah menyebabkan perubahan perilaku konsumen, gangguan rantai pasokan, dan penurunan permintaan secara keseluruhan, yang semuanya telah berkontribusi pada situasi ekonomi saat ini. Dengan munculnya platform media sosial, individu menggunakan Twitter untuk mengungkapkan pemikiran dan pendapat mereka tentang dampak pandemi pada industri teknologi, termasuk peningkatan pemutusan hubungan kerja. Dalam studi ini, peneliti menganalisis karakteristik pengguna Twitter dan penggunaan teks dan hashtag mereka dalam konteks dampak pandemi terhadap industri teknologi. Peneliti menggunakan topic modeling dan k-means clustering untuk kumpulan data tweet yang telah diproses terkait pemutusan hubungan kerja untuk mengidentifikasi tema atau topik umum dalam tanggapan pengguna Twitter terhadap tech winter layoff di Indonesia. Analisis ini mengungkapkan jumlah tweet negatif yang tinggi yang menyuarakan kemarahan dan kesedihan. Penggunaan kata kunci yang telah ditentukan sebelumnya tidak memberikan pemahaman yang komprehensif tentang fenomena tersebut karena topik lain seperti politik, agama, berita, dan iklan lebih mendominasi.
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Recommended Citation
F., F., & Widianto, S. (2023). Examining Characteristics on Twitter Users’ Text and Hashtag Utilization During Tech Winter Layoff Post-COVID-19 Using LDA and K-Means Clustering Approach. Makara Human Behavior Studies in Asia, 27(2). https://doi.org/10.7454/hubs.asia.1191223