https://jds.mbicore.com/index.php/JDS/issue/feed 2025-03-08T00:00:00+07:00 Arif Mu'amar Wahid, M.Kom. arif@amikompurwokerto.ac.id Open Journal Systems https://jds.mbicore.com/index.php/JDS/article/view/1 2024-12-15T15:55:38+07:00 Iwona Chomiak-Orsa chomiak-orsa@gmai.com Ibrahiem M. M. El Emary omary57@hotmail.com Elwira Gross-Gołacka elwira@gmail.com Andrzej Greńczuk andrzej@gmai.com Kamila Łuczak kamila@gmai.com

The rapid emergence of artificial intelligence (AI) technologies has ignited global discussions, particularly around ChatGPT, an AI tool designed to transform how humans interact with digital systems. This study explores public sentiment and emotional reactions towards ChatGPT during its initial launch period, analyzing a dataset of tweets sourced from Kaggle. Leveraging the VADER sentiment analysis algorithm, the research categorizes user reactions into positive, negative, and neutral sentiments, while also identifying key emotional tones such as joy, fear, and skepticism. The findings reveal that positive sentiment prevailed, reflecting excitement about ChatGPT’s innovative capabilities, while concerns regarding ethics and job displacement gradually surfaced, underscoring the dual nature of public opinion. Through visualizations such as bar charts, time-based sentiment trends, and word clouds, the study highlights the dynamic engagement of users with ChatGPT and its broader implications for society. Key insights suggest that public perceptions of AI are influenced by its perceived utility, accessibility, and ethical considerations. While the study demonstrates the efficacy of VADER in capturing sentiment trends, it also acknowledges limitations, including the inability to detect sarcasm or nuanced emotional expressions. The implications of this research extend to AI developers, policymakers, and researchers, emphasizing the importance of public engagement strategies that address ethical concerns and build trust. Additionally, the study contributes to the growing body of knowledge on digital society, offering a framework for understanding how emerging technologies shape public discourse. Future research could focus on comparative analyses across different social media platforms or delve deeper into the evolution of public sentiment over time. By unraveling these complexities, this study aims to guide the responsible development and deployment of AI technologies in an increasingly interconnected world.

2025-03-08T00:00:00+07:00 Copyright (c) 2025 Journal of Digital Society https://jds.mbicore.com/index.php/JDS/article/view/2 2024-12-15T15:59:27+07:00 Min-Tsai Lai lai1962@mail.stust.edu.tw Taqwa Hariguna taqwa@amikompurwokerto.ac.id

This study explores the relationship between company characteristics and recruitment trends by analyzing a dataset obtained from Simplify.jobs, which contains detailed profiles of companies and their job postings. The research focuses on how organizational attributes such as funding stage, customer type, and company size influence recruitment strategies and job posting behaviors. Using clustering techniques, the study identifies three distinct clusters of companies based on these attributes, revealing that early-stage companies prioritize technical hires while later-stage companies offer a more diverse array of benefits. The study also employs association rule mining to uncover frequent patterns between job attributes, such as the tendency for tech companies to offer remote work options. These findings highlight how companies adapt their recruitment strategies as they grow, with early-stage companies leveraging financial incentives like stock options to attract talent, while later-stage companies emphasize employee retention through comprehensive benefits packages. The results offer valuable insights for HR professionals and recruiters, enabling them to tailor their strategies according to company profiles. This research contributes to the broader field of data-driven recruitment analysis by providing a nuanced understanding of how company characteristics shape hiring practices and job posting trends. The study also paves the way for future research into the evolution of recruitment strategies over time and the application of similar methodologies to other industries.

2025-03-08T00:00:00+07:00 Copyright (c) 2024 Journal of Digital Society https://jds.mbicore.com/index.php/JDS/article/view/3 2024-12-15T16:04:17+07:00 Minh Luan Doan AMA3124@e.ntu.edu.sg

This study explores the dynamics of public sentiment toward SpaceX, focusing on how it fluctuates in response to key events, including successful missions and technical setbacks. Using sentiment analysis on a dataset of SpaceX-related tweets, this research captures the emotional reactions of the public, classifying them into positive, neutral, and negative categories. The analysis reveals distinct patterns: positive sentiment predominates during major achievements, such as rocket launches and new technological advancements, while negative sentiment spikes following failures or delays. The results demonstrate how public perception of SpaceX is intricately tied to the company’s performance, reflecting both excitement for its successes and frustration for its setbacks. By examining these sentiment trends, this research offers insights into how companies in the space exploration sector can manage their public relations efforts and strategically engage with audiences on social media platforms. The study employs the TextBlob sentiment analysis tool, which classifies tweet polarity and subjectivity, to categorize public sentiment in a straightforward yet effective manner. Through time-series visualizations, the study tracks how sentiment evolves over time, highlighting key fluctuations tied to SpaceX’s milestones. Additionally, the research integrates visualizations like word clouds and bar charts to identify frequent keywords associated with both positive and negative sentiments, providing a deeper understanding of public discourse surrounding the company. The study underscores the role of Twitter as a significant tool for shaping public perception, particularly in high-visibility industries like space exploration, where real-time feedback can influence both public opinion and corporate strategies. This research contributes to the broader field of sentiment analysis by focusing on the tech and space industries, where public sentiment plays a pivotal role in shaping business success and technological innovation. By examining SpaceX’s public image through sentiment trends, this study highlights the importance of real-time sentiment monitoring in shaping company strategies. Future studies could extend this analysis to include other companies in the space sector or incorporate more sophisticated machine learning models for deeper sentiment classification.

2025-03-08T00:00:00+07:00 Copyright (c) 2024 Journal of Digital Society https://jds.mbicore.com/index.php/JDS/article/view/4 2024-12-15T16:07:50+07:00 B Herawan Hayadi b.herawan.hayadi@binabangsa.ac.id Ika Maulita ika.maulita@unsoed.ac.id

The growing influence of social media platforms like Twitter has transformed the landscape of public discourse, particularly on critical societal issues such as education. This study investigates public sentiment on education in Indonesia by analyzing tweets collected between January and June 2024, using sentiment analysis techniques powered by the Support Vector Machine (SVM) algorithm. By leveraging a dataset of 484 tweets, the analysis classified sentiments into positive, neutral, and negative categories, uncovering dominant patterns and their correlation with significant events in the education sector. The findings revealed a strong prevalence of neutral sentiments, emphasizing Twitter’s role as an informational hub. Positive sentiments were linked to public approval of equity-focused reforms, such as increased funding for rural schools, while negative sentiments reflected dissatisfaction with contentious policies, particularly standardized testing reforms. The study also examined temporal trends, identifying spikes in sentiment coinciding with major policy announcements, such as the significant surge in neutral sentiments in May 2024 following the government’s testing policy changes. These patterns illustrate the dynamic nature of public engagement on Twitter, shaped by real-time events and discussions. A comparative analysis with existing literature confirmed the value of social media as a barometer for public opinion, while also highlighting the unique context of Indonesian education. This research contributes to the growing field of digital society by demonstrating how sentiment analysis can provide actionable insights for policymakers and educators. It underscores the transformative potential of social media analytics in fostering inclusive and responsive governance. However, limitations such as dataset biases and classification challenges suggest avenues for future research, including multi-platform analysis and advanced natural language processing techniques. These findings serve as a foundation for leveraging sentiment analysis to enhance educational strategies and public communication in Indonesia's increasingly digitalized landscape.

2025-03-08T00:00:00+07:00 Copyright (c) 2025 Journal of Digital Society https://jds.mbicore.com/index.php/JDS/article/view/5 2024-12-15T16:11:07+07:00 Deshinta Arrova Dewi deshinta.ad@newinti.edu.my Tri Basuki Kurniawan t.basuki@gmai.com

Social media platforms, particularly Twitter, have emerged as influential arenas for financial discourse, shaping and reflecting market sentiment in real time. This study explores the thematic structure of financial discussions on Twitter, employing Latent Dirichlet Allocation (LDA) to identify key topics and their temporal dynamics. A dataset of 11,932 finance-related tweets was analyzed, revealing five distinct topics encompassing corporate earnings, macroeconomic policies, geopolitical trade issues, and market trends. By correlating tweet volumes and topic prevalence with significant financial events, the study demonstrates the utility of social media as a barometer for market activity. Unlike traditional sentiment analysis, which predominantly classifies tweets into sentiment categories such as bullish, bearish, or neutral, the application of LDA enabled the extraction of latent themes that underpin these sentiments. This nuanced approach provided deeper insights into the narratives driving market discussions, offering a more comprehensive understanding of how thematic shifts in financial discourse align with market movements. Visualization techniques, including topic-term matrices and word clouds, further elucidated the structure of these conversations, enhancing interpretability and accessibility. The findings contribute to the growing body of research on social media analytics in finance, highlighting the potential of unsupervised learning techniques for financial trend analysis. By bridging the gap between thematic exploration and temporal analysis, this study offers a methodological framework for leveraging social media data to uncover actionable insights. The implications extend beyond academic research, providing practical tools for investors, financial analysts, and policymakers to navigate the dynamic relationship between social media narratives and market behavior. Future research could expand on these insights by integrating more advanced modeling techniques, such as transformer-based models, and exploring domain-specific patterns across asset classes like stocks, commodities, and cryptocurrencies. By examining the intersection of social media, financial events, and market dynamics, this study lays the groundwork for a deeper understanding of digital narratives in financial ecosystems.

2025-03-08T00:00:00+07:00 Copyright (c) 2025 Journal of Digital Society