Examining Digital Leadership, Digital Transformation, and Employee Engagement in Public Sector Discourse on X (Twitter): A Social Network Analysis and Sentiment Mining Approach
Abstract
Purpose – This study maps and analyzes public-sector discourse on digital leadership, digital transformation, and employee engagement on X (Twitter). It aims to identify the dominant framing of the conversation, its sentiment orientation, its thematic composition, and the actors who shape its diffusion.
Design/methods/approach – The study adopts a quantitative, exploratory computational social science design. A corpus of 288 English-language posts was retrieved from X (Twitter) between 27 January and 28 July 2026 using Boolean keyword queries on the SocialX analytics platform. Three methods were combined: sentiment mining using a hybrid lexicon and machine-learning classifier, zero-shot thematic classification, and social network analysis (SNA) examining centrality and community structure.
Findings – "Government" is the dominant topic of discussion, with sentiment that is predominantly negative (73%), followed by neutral (23%) and positive (4%), indicating a critical and politically charged discourse. Zero-shot classification shows that most posts address government subjects (66%), followed by technology (28%) and human resources (6%). The interaction network is low-density and highly centralized with a hub-and-spoke structure, suggesting influence-driven rather than community-driven diffusion, while temporal analysis reveals an event-driven pattern with an early peak and subsequent decline.
Research implications/limitations – The study provides evidence-based recommendations for digital communication and reputation management in public-sector organizations through an integrated framework that connects sentiment polarity, theme orientation, and network position. Its limitations include a small sample size, a limited time frame, and the absence of independent domain-specific validation of the classifier. Future research could employ cross-platform designs, larger longitudinal corpora, and manually validated sentiment models.
Originality/value – The study integrates affective (sentiment), thematic (zero-shot), and relational (network) analyses into a single framework for examining public-sector digital discourse, linking what the public feels, which topics dominate, and who controls the flow of information.
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DOI: https://doi.org/10.17509/tjr.v8i2.104781
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