Mayor Inna Gurung
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Graduate Research Assistant
Graduate Student Researcher
Research Areas
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Biography and Research Information
OverviewAI-generated summary
Mayor Inna Gurung's research focuses on the diffusion and influence of narratives within digital social networks. Her work investigates how information, particularly in the form of narratives, spreads through platforms like YouTube, Instagram, and TikTok. Gurung employs computational modeling, drawing on epidemiological theories, to understand the dynamics of narrative contagion and polarization. She has explored the role of semiotics and visual media in shaping user engagement, trust, and emotional responses in information campaigns. Her recent publications model narrative diffusion in social topologies and analyze competing narratives in political elections, such as Taiwan's 2024 election on TikTok. Gurung collaborates with researchers at the University of Arkansas at Little Rock, including Nitin Agarwal and Md. Monoarul Islam Bhuiyan, on several shared publications.
Metrics
- h-index: 5
- Publications: 12
- Citations: 52
Selected Publications
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Narrative Diffusion in Social Topologies: A Comparative Study of LLM-Driven Dynamics (2026)
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Competing Narratives on TikTok: Modeling Taiwan’s 2024 Election Dynamics (2026)
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How Do Competing Narratives Spread? A Stance-Based Epidemiological Approach (2026)
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Narrative diffusion in social networks: a survey (2025)
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Examining the role of semiotics in social media-driven information campaigns (2025)
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Modeling polarized information diffusion with SEI(A)I(D)Z: a stance-based epidemiological approach (2025)
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Symbolic signals on Instagram: how visual media shapes engagement, emotion, trust, and diffusion (2025)
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Developing a Stance-induced Epidemiological Model to Examine Polarized Information Contagion (2025)
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Are Narratives Contagious? Modeling Narrative Diffusion Using Epidemiological Theories (2025)
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Exploring Online Video Narratives and Networks Using VTracker (2023)
Collaboration Network
Top Collaborators
- Are Narratives Contagious? Modeling Narrative Diffusion Using Epidemiological Theories
- Modeling polarized information diffusion with SEI(A)I(D)Z: a stance-based epidemiological approach
- Symbolic signals on Instagram: how visual media shapes engagement, emotion, trust, and diffusion
- Narrative diffusion in social networks: a survey
- Exploring Online Video Narratives and Networks Using VTracker
Showing 5 of 7 shared publications
- Modeling polarized information diffusion with SEI(A)I(D)Z: a stance-based epidemiological approach
- Developing a Stance-induced Epidemiological Model to Examine Polarized Information Contagion
- Competing Narratives on TikTok: Modeling Taiwan’s 2024 Election Dynamics
- Narrative Diffusion in Social Topologies: A Comparative Study of LLM-Driven Dynamics
- Exploring Online Video Narratives and Networks Using VTracker
- Exploring Online Video Narratives and Networks Using VTracker
- Exploring Online Video Narratives and Networks Using VTracker
- Are Narratives Contagious? Modeling Narrative Diffusion Using Epidemiological Theories
- 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
- How Do Competing Narratives Spread? A Stance-Based Epidemiological Approach
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