Examining Characteristics on Twitter Users’ Text and Hashtag Utilization During Tech Winter Layoff Post-COVID-19 Using LDA and K-Means Clustering Approach - F., Fery; Widianto, Sunu (UI Scholars Hub, 2023)
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.