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All Journal JOURNAL OF APPLIED INFORMATICS AND COMPUTING The Indonesian Journal of Computer Science
Revesai, Zvinodashe
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Information Cascades in Professional Networks: A Graph-Based Study of LinkedIn Post Engagement Revesai, Zvinodashe; Mutanga, Murimo Bethel; Chani, Tarirai
Journal of Applied Informatics and Computing Vol. 9 No. 4 (2025): August 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i4.10212

Abstract

Information cascades in professional networks represent a critical mechanism for knowledge transfer and career development, yet their dynamics remain poorly understood. This study presents a comprehensive empirical analysis of information cascades in LinkedIn professional networks, focusing on computer science professionals and academic-industry knowledge transfer. We analysed 50,000 CS professionals, 500,000 connections, and 100,000 technical posts over 12 months using a Modified Independent Cascade Model that incorporates professional context factors. Our analysis reveals that hybrid professionals, representing only 25% of the network, account for 52% of inter-cluster connections and achieve 2.8× higher cross-domain transfer rates. Educational content demonstrates superior cross-domain appeal (0.47) compared to research papers (0.23), with optimal posting windows between 10 AM-12 PM achieving 23% higher cross-domain engagement. Bridge users in academic-industry transitions show significantly higher transfer effectiveness (Cohen's d = 1.47, p < 0.001). These findings provide evidence-based strategies for optimising professional networking and knowledge dissemination across academic and industry domains
Why Generative AI Will Not Replace University Lecturers: A Human-Centred Perspective Murimo Bethel Mutanga; Revesai, Zvinodashe; Samuel Chikasha; Tarirai Chani
The Indonesian Journal of Computer Science Vol. 14 No. 6 (2025): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v14i6.5037

Abstract

The integration of artificial intelligence (AI) into higher education has prompted widespread speculation about the potential obsolescence of university lecturers. While AI systems demonstrate impressive capabilities in content delivery, assessment, and personalisation, this research critically examines the assumption that they can replace human educators. This issue is particularly complex, given that effective higher education involves not only the transmission of information but also the development of cognitive, emotional, ethical, and social aspects. Despite advances in AI technologies, current discourse often neglects the irreplaceable human functions that underpin transformative education. Addressing this gap, the study adopts a human-centred framework to investigate essential lecturer capabilities, limitations of AI systems, and the design of optimal human-AI collaboration. Using qualitative methods, including stakeholder interviews and comparative institutional analysis, the findings reveal ten educational domains where human capabilities remain indispensable, from emotional support and ethical mentorship to adaptive teaching and research integration. AI excels in routine, scalable tasks, yet lacks empathy, moral agency, and contextual understanding. Consequently, this research proposes a collaborative model in which AI enhances rather than replaces lecturers, thereby supporting educational quality and student development. The findings have significant implications for institutional policy, faculty development, and the ethical integration of AI in education, affirming the enduring and transformative role of human educators in the digital age.
Co-Authors Chani, Tarirai Murimo Bethel Mutanga Mutanga, Murimo Bethel Samuel Chikasha Tarirai Chani