Nitin Agarwal
Sourced from institutional research profiles (UAMS TRI or ARA).
COSMOS Research Center - Social Media Analytics and Socio-Cognitive Security
Also affiliated: Creighton University (2023); Dr. A.P.J. Abdul Kalam Technical University (2025); University of Engineering & Management (2013); International Computer Science Institute (2025); University of Waterloo (2014); National Institute of Technology Durgapur (2014–2022); University of Illinois Urbana-Champaign (2007–2010); University College of Medical Sciences (2014); Vilnius University (2019); University of California, Irvine (2018); University of Arkansas Medical Center (2025); University of California System (2016); Indian Institute of Management Indore (2005–2012); Arkansas Department of Agriculture (2022–2025); Cosmos Corporation (United States) (2021–2024); Arizona State University (2005–2010); Indian Institute of Technology Hyderabad (2014–2015); Carnegie Mellon University (2011); University of California, Berkeley (2025)
Faculty Researcher
Research Areas
Biomedical Subjects
Links
Biography and Research Information
OverviewAI-generated summary
Nitin Agarwal directs the Collaboratorium for Social Media and Online Behavioral Studies (COSMOS) at the University of Arkansas at Little Rock, where he also holds the Jerry L. Maulden-Entergy Endowed Chair and Distinguished Professor of Information Science position. He is a faculty fellow at the International Computer Science Institute at the University of California - Berkeley. His research focuses on understanding digital and cyber social behaviors that emerge and evolve on contemporary communication platforms.
Under his leadership, COSMOS has secured over $25 million in funding from U.S. federal agencies, including the Department of Defense, DARPA, the Department of State, and the National Science Foundation. Agarwal has played a key role in the partnership between UA Little Rock and the Department of Homeland Security. He developed publicly available social media analysis tools, Blogtracker and YouTubeTracker, which have been utilized by organizations such as NATO Strategic Communications and Public Affairs, European Defense agencies, the Australian Defense Science and Technology Agency, and the Arkansas Attorney General’s office.
Agarwal's scholarly output includes 314 publications, with an h-index of 30 and over 4,300 citations. His recent work has explored topics such as social bots, toxicity propagation in online discourse, and the accuracy of AI chatbot responses for medical procedures. He is a member of the ARA Academy as an ARA Scholar in Cyber Social Computing and is recognized as a highly cited researcher.
Metrics
- h-index: 30
- Publications: 313
- Citations: 4,294
Selected Publications
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Narrative Shifts in YouTube Recommendation Networks: A Depth-Based Analysis of the Indonesian Protest (2026)
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Toxic Unity: Behavioral Homogenization and the Inverse Toxicity-Cohesion Mechanism in Protest Movements (2026)
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Focal Collective Actors as Narrative Structures: Cross-Platform Brokerage in Digital Protest (2026)
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Studying Emotional and Trust-building Effects of Symbolic Communication on YouTube (2026)
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Beyond Algorithmic Detection: How Community Expertise Moderates Toxic Algospeak via Dual Cognitive Mechanisms (2026)
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Quantifying Algorithmic Entrapment in YouTube Recommendation Network: A Composite Measure of Structure and Persuasion (2026)
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ViMET-R: Auditing Activity-Level Bias in YouTube Shorts Recommendations (2026)
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Uncovering Channel-Level Behaviors via Multimodal Characterization in YouTube Content (2026)
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Toxicity-Driven Behavioral Homogenization in Multilayer Political Networks: Cross-Dimensional Coupling During Russia-Ukraine Conflict (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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Weighted Focal Structure Analysis for Coordinated Toxicity Propagation in Social Networks (2026)
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CI-FSA: Toward Scalable Discovery of Influential Groups in Social Networks (2026)
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Narrative Diffusion in Social Topologies: A Comparative Study of LLM-Driven Dynamics (2026)
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The Network Effect of Shared Grievances: Measuring Collective Concern of Tariff Policy (2026)
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Modeling the Propagation Dynamics of Visual Elements with Epidemiological Frameworks (2026)
ARA Academy 2018 ARA Scholar
Dr. Agarwal directs the Collaboratorium for Social Media and Online Behavioral Studies (COSMOS). His work centers on cyber social behaviors in modern information platforms with applications spanning defense, security, health, business, marketing, finance, and education. His specific areas include cyber information campaigns, social computing, deviant behavior modeling, group dynamics, social-cyber forensics, and privacy.
Policy Impact
Directs the COSMOS research center, attracting defense and security funding for social media analytics research that addresses national cybersecurity priorities from Arkansas.
Growth Areas
['Supply Chain Retail & Consumer Analytics']
Collaboration Network
Top Collaborators
- Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube
- Emotion Assessment of YouTube Videos using Color Theory
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- The bias beneath: analyzing drift in YouTube’s algorithmic recommendations
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
Showing 5 of 11 shared publications
- Applying diffusion of innovations theory to social networks to understand the stages of adoption in connective action campaigns
- Adopting Parallel Processing for Rapid Generation of Transcripts in Multimedia-rich Online Information Environment
- Analyzing Bias in Recommender Systems: A Comprehensive Evaluation of YouTube's Recommendation Algorithm
- Investigating Bias in YouTube Recommendations: Emotion, Morality, and Network Dynamics in China-Uyghur Content
- Examining Toxicity’s Impact on Reddit Conversations
Showing 5 of 10 shared publications
- Characterizing Suspicious Commenter Behaviors
- Contextualizing focal structure analysis in social networks
- Comprehensive decomposition optimization method for locating key sets of commenters spreading conspiracy theory in complex social networks
- Using Computational Social Science Techniques to Identify Coordinated Cyber Threats to Smart City Networks
- A Systematic Approach for Contextualizing Focal Structure Analysis in Social Networks
Showing 5 of 8 shared publications
- Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube
- Applying diffusion of innovations theory to social networks to understand the stages of adoption in connective action campaigns
- Applying an Epidemiological Model to Evaluate the Propagation of Misinformation and Legitimate COVID-19-Related Information on Twitter
- Assessing Bias in YouTube’s Video Recommendation Algorithm in a Cross-lingual and Cross-topical Context
- Assessing the influence and reach of digital activity amongst far-right actors: A comparative evaluation of mainstream and ‘free speech’ social media platforms
Showing 5 of 8 shared publications
- Evaluating Structural Attractors and Retainers in YouTube Recommendation Networks
- 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
- Attraction and retention dynamics in recommendation graphs: a cross-dataset analysis using uniform and degree-biased random walks
Showing 5 of 7 shared publications
- Telegram: Data Collection, Opportunities and Challenges
- Assessing Bias in YouTube’s Video Recommendation Algorithm in a Cross-lingual and Cross-topical Context
- Comprehensive decomposition optimization method for locating key sets of commenters spreading conspiracy theory in complex social networks
- Developing an agent-based model to minimize spreading of malicious information in dynamic social networks
- Analyzing Cyber Influence Campaigns on YouTube Using YouTubeTracker
- Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube
- Assessing the influence and reach of digital activity amongst far-right actors: A comparative evaluation of mainstream and ‘free speech’ social media platforms
- A public online resource to track COVID-19 misinfodemic
- Analyzing Cyber Influence Campaigns on YouTube Using YouTubeTracker
- Characterizing video-based online information environment using VTracker
- Examining Multimodel Emotion Assessment and Resonance with Audience on YouTube
- Examining Toxicity’s Impact on Reddit Conversations
- Evaluating Role of Instagram’s Multimedia in Connective Action Leveraging Diffusion of Innovation and Cognitive Mobilization Theories: Brazilian and Peruvian Social Unrest Case Studies
- Developing Epidemiological Models with Differentiated Infected Intensity
- Characterizing Multimedia Information Environment Through Multi-modal Clustering of YouTube Videos
- Modeling cross-platform narrative templates: a temporal knowledge graph approach
- A comparative evaluation of social network analysis tools: performance and community engagement perspectives
- KG-CFSA: a comprehensive approach for analyzing multi-source heterogeneous social network knowledge graph
- Detecting and Measuring Anomalous Behaviors on YouTube
- Focal Collective Actors as Narrative Structures: Cross-Platform Brokerage in Digital Protest
- Emotion Assessment of YouTube Videos using Color Theory
- Telegram: Data Collection, Opportunities and Challenges
- Evaluating Role of Instagram’s Multimedia in Connective Action Leveraging Diffusion of Innovation and Cognitive Mobilization Theories: Brazilian and Peruvian Social Unrest Case Studies
- Characterizing Multimedia Information Environment Through Multi-modal Clustering of YouTube Videos
- Characterizing Suspicious Commenter Behaviors
- Developing a network-centric approach for anomalous behavior detection on youtube
- Detecting and Measuring Anomalous Behaviors on YouTube
- Developing a Commenter Behavior-Based Framework for Characterizing YouTube Channels
- Unveiling Bias in YouTube Shorts: Analyzing Thumbnail Recommendations and Topic Dynamics
- Beyond the Click: How YouTube Thumbnails Shape User Interaction and Algorithmic Recommendations
- Investigating Algorithmic Bias in YouTube Shorts
- ViMET-R: Auditing Activity-Level Bias in YouTube Shorts Recommendations
- Are Narratives Contagious? Modeling Narrative Diffusion Using Epidemiological Theories
- How Do Competing Narratives Spread? A Stance-Based Epidemiological Approach
- Competing Narratives on TikTok: Modeling Taiwan’s 2024 Election Dynamics
- Narrative Diffusion in Social Topologies: A Comparative Study of LLM-Driven Dynamics
- Flash mob: a multidisciplinary review
- Using Computational Social Science Techniques to Identify Coordinated Cyber Threats to Smart City Networks
- Studying the Role of Social Bots During Cyber Flash Mobs
- Social Bots and Their Coordination During Online Campaigns: A Survey
- Developing a socio-computational approach to examine toxicity propagation and regulation in COVID-19 discourse on YouTube
- Telegram: Data Collection, Opportunities and Challenges
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