Md. Monoarul Islam Bhuiyan
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Graduate Research Assistant
Graduate Student Researcher
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Biography and Research Information
OverviewAI-generated summary
Md. Monoarul Islam Bhuiyan's research investigates the dynamics of online recommendation systems and social media engagement. His work examines how structural properties within recommendation networks influence user attraction and retention, employing methods such as uniform and degree-biased random walks. Bhuiyan has explored the impact of semiotics and symbolic signals on user engagement, emotion, and trust within information campaigns on platforms like YouTube and Instagram. He also studies the detection of algorithmic homophily in recommendation graphs through weighted topic distribution and has developed metrics like TrapIntensity to quantify structural entrapment. Bhuiyan collaborates with researchers at the University of Arkansas at Little Rock, including Nitin Agarwal, Mayor Inna Gurung, Ahmed Al‐Taweel, and Diwash Poudel, with whom he has co-authored multiple publications.
Metrics
- h-index: 3
- Publications: 8
- Citations: 19
Selected Publications
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Narrative Shifts in YouTube Recommendation Networks: A Depth-Based Analysis of the Indonesian Protest (2026)Journal of the Association for Information Systems OpenAlex
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Quantifying Algorithmic Entrapment in YouTube Recommendation Network: A Composite Measure of Structure and Persuasion (2026)Journal of the Association for Information Systems OpenAlex
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Decoding persuasive content traps: a comprehensive analysis of persuasive cues, topical uniformity, and engagement on YouTube’s recommendation networks (2026)
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Attraction and retention dynamics in recommendation graphs: a cross-dataset analysis using uniform and degree-biased random walks (2026)
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TrapIntensity: Quantifying Structural Entrapment via Hop-Aware Attraction and Retention (2026)
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Persuasive Pathways Into Content Traps: The Role of Persuasive Features in Structuring Algorithmic Content Cycles (2026)
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How Far Is Too Far? Modeling User Attraction Pathways in Recommendation Networks via Random Walk Variants (2026)
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Evaluating Structural Attractors and Retainers in YouTube Recommendation Networks (2026)
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Detecting Algorithmic Homophily in Recommendation Graphs via Weighted Topic Distribution (2025)
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Structure, Semantics, and Attraction: Analyzing Homophily in Recommender Networks (2025)
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Symbolic signals on Instagram: how visual media shapes engagement, emotion, trust, and diffusion (2025)
Collaboration Network
Top Collaborators
- Symbolic signals on Instagram: how visual media shapes engagement, emotion, trust, and diffusion
- Structure, Semantics, and Attraction: Analyzing Homophily in Recommender Networks
- Detecting Algorithmic Homophily in Recommendation Graphs via Weighted Topic Distribution
- How Far Is Too Far? Modeling User Attraction Pathways in Recommendation Networks via Random Walk Variants
- Persuasive Pathways Into Content Traps: The Role of Persuasive Features in Structuring Algorithmic Content Cycles
- TrapIntensity: Quantifying Structural Entrapment via Hop-Aware Attraction and Retention
- Symbolic signals on Instagram: how visual media shapes engagement, emotion, trust, and diffusion
- Symbolic signals on Instagram: how visual media shapes engagement, emotion, trust, and diffusion
- Evaluating Structural Attractors and Retainers in YouTube Recommendation Networks
- Attraction and retention dynamics in recommendation graphs: a cross-dataset analysis using uniform and degree-biased random walks
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