nitin agarwal

What role do you play at COSMOS?

At COSMOS, I work as a Graduate Assistant, helping Prof. Agarwal with projects focused on understanding social media behaviors. My work involves helping with conducting experiments to evaluate hypotheses. Additionally, I contribute to the center’s communication efforts.

Please share a bit about your professional background and experience.

I have a multidisciplinary background in social sciences and data science, with experience in program evaluation, data analysis, and monitoring and evaluation. I hold a Master of Public Service and am currently pursuing a Master of Science in Information Science at UA Little Rock. My professional experience includes policy research with the Arkansas Department of Education, data analysis with nonprofit organizations, and supporting UNICEF and World Bank social protection initiatives through data-driven monitoring and evaluation. 

What attracted you to join the COSMOS Research Center? What aspects of COSMOS’s vision, mission, and culture stood out to you and why?

What drew me to COSMOS was Prof. Agarwal’s focus on using data science and social media analytics to understand real-world cyber behaviors. Prof. Agarwal’s vision of leading high-impact, multidisciplinary research perfectly aligns with my desire to apply my data science training to meaningful societal issues. It is inspiring to see in the news that Prof. Agarwal and COSMOS are widely recognized as global leaders in social media research, earning international awards for tracking algorithmic bias, combating misinformation, and uncovering narrative manipulation on platforms like YouTube. The culture at COSMOS also stood out to me because it encourages team members from different backgrounds to collaborate and solve these complex problems together.

How do you anticipate your role at COSMOS helping your growth on both a personal and professional level? Are there any specific skills or experiences you’re looking to gain?

At COSMOS, I hope to strengthen my skills in social media analytics, data preprocessing, Python, data visualization, and UI development, as these are core requirements for all COSMOS projects led by Prof. Agarwal. I’m also eager to gain a deeper understanding of tools such as BlogTracker and vTracker and how their data pipelines and interfaces work together. Working under Prof. Agarwal’s guidance will also give me valuable exposure to interdisciplinary research and help me grow as both a researcher and a technical professional.

From your experience, what tips, insights, or advice would you share with someone starting a new role at COSMOS?

My biggest piece of advice would be to fully embrace the collaborative nature of the lab and never hesitate to ask questions. Because COSMOS brings together people from so many different academic backgrounds, there is a wealth of knowledge all around you. A great way to tap into that is to pay attention during the daily huddles when cosmographers share updates on the projects and tasks they are working on. 

If you could share a meal with any historical figure or fictional character, who would it be, and what would you want to talk about and learn from them?

I would choose to share a meal with Maya Angelou. Having worked closely with the Celebrate Maya Project in Little Rock, I have developed a deep appreciation for her legacy, resilience, and profound ability to capture the human experience. During our conversation, I would love to connect my background in public service and a strong desire for social media analysis with her unique perspective on community engagement. I would want to talk to her about how we can best use modern data tools and technology to uncover societal needs, tell authentic stories, and shape public policies that protect vulnerable populations. 

Where did you complete your internship, and what was the primary focus of your projects?

I did my summer internship at Meta, Inc. (parent company of Facebook), where I worked on developing “Early Stage Ranking Models” for Product Centric Ads (Recommendation Systems)

What skills did you develop, what challenges did you face, and what was your most valuable takeaway from the experience?

During my internship, I developed skills in ranking systems, machine learning experimentation, model evaluation, and production-focused AI. The main challenge was learning how to build under real production constraints, such as scale and latency. My biggest takeaway was that strong ML solutions must be accurate, efficient, and practical for real-world deployment. This experience showed me how machine learning research becomes useful when it can work reliably in a real production system.

In what ways did your COSMOS research experience prepare you for your internship?

I work as a graduate assistant at the COSMOS Research Center under Prof. Nitin Agarwal’s supervision on projects that honed my skills in social computing, behavioral modeling, machine learning, AI, large-scale data analysis, and model evaluation. Prof. Agarwal is also my doctoral advisor and dissertation chair. His mentorship enabled me to conduct competitive, team-oriented, and application-driven research. He encourages us to publish at top-tier venues. These skills helped me tremendously during my internship at Meta, a highly competitive and mission-driven environment.

How has your internship experience enhanced your current work and research at COSMOS?

My internship influenced how I think about our research at COSMOS. It helped me see the value of building models that are not only accurate but also efficient, scalable, and useful in real-world applications. I now bring this practical view into the research project led by Prof. Agarwal at the COSMOS Research Center.

How did your internship broaden your perspective on the type of research conducted at COSMOS? 

My internship broadened my view of research conducted at COSMOS. At COSMOS, we study how user connections on social media shape social behavior from outside the platforms. At Meta, I studied these behaviors from within the platform. Specifically, I analyzed how similar user-item and user-content connections can be used to support recommendation systems. This helped me see that the same research ideas can apply to both social analysis and product systems. Further, I recognize the need for an application-oriented approach to research – something that Prof. Agarwal consistently champions.

What advice would you offer to fellow COSMOS researchers preparing for future internships?

Be steadfast. Work hard. Learn as much as you can. Know your research and its value to society. No shortcuts. Listen to Prof. Agarwal!

Share a memorable story or moment from your internship that stood out to you.

One memorable moment was when one of my peer leads went on a refresh, and I had to help set up a war room with senior engineers. It was an eye-opening experience because I had to respond quickly, organize the work, communicate clearly, and solve technical problems under pressure. The moment tested my engineering, leadership, and interpersonal skills.

If you had to describe your internship experience in one word, what would it be? 

Incredible!

In this month’s research spotlight, COSMOS highlights three studies published at the 14th International Conference on Complex Networks and their Applications (Complex Networks), held in New York, USA, that investigate how recommendation systems shape user attention, content visibility, and online behavior. As users move from one recommended video to another, digital platforms can quietly nudge them toward tightly connected clusters of content. These clusters, often described as content traps, may narrow exposure, reinforce specific viewpoints, and steer attention towards (or away from) certain narratives or topics.

The first study, “How Far is Too Far? Modeling User Attraction Pathways in Recommendation Networks via Random Walk Variants,” examines how easily users can encounter structurally influential groups within a YouTube recommendation network. Using hop-aware random walk simulations, the study models how users may move from different distances in the network and compares neutral exploration with popularity-driven navigation. The study shows that certain focal structures are more reachable than other network groupings, offering insight into how recommendation pathways can make some content clusters more visible than others.

The second study, “TrapIntensity: Quantifying Structural Entrapment via Hop-Aware Attraction and Retention,” builds on this idea by asking not only whether users can reach a content cluster, but also how strongly that cluster can hold attention. The framework combines attraction and retention into a unified trap intensity score, helping identify network regions that are both easy to enter and difficult to leave. This offers a more interpretable way to study content traps, echo chambers, and filter bubbles in recommendation systems.

The third study, “Persuasive Pathways into Content Traps: The Role of Persuasive Features in Structuring Algorithmic Content Cycles,” looks beyond network structure to examine the content itself. The research investigates how persuasive features in YouTube transcripts interact with topical uniformity and engagement. The study finds that highly homogeneous content groups tend to contain stronger persuasive signals and higher engagement, suggesting that content traps are not only structural but also rhetorical. In other words, users may remain in these cycles not just because of how recommendations are connected, but because the content itself is persuasive and reinforcing.

Together, these studies tell a broader story about algorithmic influence. AI-based recommendation systems do more than suggest content; they shape pathways of influence and attention. By combining network science, random walk modeling, persuasion theory, and engagement analysis, our research advances new ways to understand how content traps form, why they persist, and how they can be studied more transparently. Collectively, it reflects COSMOS’s mission to develop robust, interpretable, and socially meaningful approaches for analyzing digital ecosystems. For science, it contributes new methods for auditing recommendation networks and modeling user exposure. For society, it supports a deeper understanding of how online platforms and their AI-based algorithms can influence information diversity, user agency, and the dynamics of digital behavior.

COSMOS Research Center is pleased to announce a new publication in Springer’s Journal of Social Network Analysis and Mining titled “Large-Scale Toxicity Intervention in Social Networks: Evaluating Integer Programming-Optimized Focal Toxic Structures”.

Online harassment and toxic campaigns rarely happen in isolation. Instead, they are driven by focal toxic structures (densely connected groups of social media accounts) that work together to amplify harmful content and evade standard filters. Traditional content moderation systems focus on removing individual posts or banning single accounts, but these coordinated groups simply create new profiles or use coded language to keep campaigns alive. Platform moderation teams face strict resource limits and cannot review every flagged user, making it critical to know exactly which groups to prioritize for removal. To solve this, the study introduces an advanced optimization model combining Weighted Focal Structure Analysis with Integer Programming optimization. Instead of evaluating toxic groups one by one, this mathematical approach looks at the entire network at once to select the most impactful combination of groups to remove. It maximizes network disruption while respecting real-world constraints, such as moderation budget limits, overlap prevention, and minimum impact thresholds.

Using a large-scale dataset of 324,769 Telegram users active during the Russia-Ukraine conflict, the research systematically compared standard sequential selection against the new optimized approach and a hybrid model. The findings revealed that the mathematically optimized approach drastically outperformed standard baseline methods across every metric. It achieved up to a 149% increase in network fragmentation, split up the main toxic highways twice as effectively, and achieved nearly double the reduction in overall harmful content while removing 17% fewer total users.

This research advances computational social science by giving trust and safety teams a scalable, data-driven framework to dismantle harmful campaigns at their root. By shifting the focus from reactive post removals to strategic network interventions, COSMOS continues to pave the way for safer, more resilient digital ecosystems.

Click here to read the full article.

COSMOS Research Center is proud to highlight its recent presentation of three milestone studies at the Cyber Social Threats (CySoc), held at the 20th International AAAI Conference on Web and Social Media (ICWSM 2026) in Los Angeles, USA.

Organized by the Association for the Advancement of Artificial Intelligence (AAAI), ICWSM is recognized internationally as a flagship venue for computational social science, bridging advanced data science with human behavior analysis. Its specialized CySoc workshop provides an essential platform for addressing the dark side of digital platforms, focusing on cyber social threats, coordinated online manipulation, and digital behavior during acute political crises. Participating in this highly competitive venue highlights COSMOS’s ongoing commitment to advancing data-driven tools that safeguard public discourse and foster digital resilience.

Publishing at AAAI ICWSM CySoc directly advances COSMOS’s mission to analyze online influence, cognitive security, and information stability, supported in part by major federal grants dedicated to understanding dynamics across strategic regions, including the Indo-Pacific. Through these projects, COSMOS researchers examine real-world social movements, cross-platform behaviors, and digital discourse across key international contexts, including Nepal, Taiwan, and broader global trade ecosystems.

The three studies presented at CySoc showcase COSMOS’s multi-pronged approach to modeling and mitigating digital threats. The first study extends classical epidemiological frameworks by introducing an immediate relapse mechanism into the SEIRS model, successfully capturing how toxic behavior repeatedly flares up in online communities. The second paper analyzes multilingual discourse from the 2025 youth-led protests in Nepal across five temporal crisis phases, revealing a critical finding for platform moderation: while switching languages within reply chains often reduces general personal insults, it selectively elevates physical threat language, uncovering a covert threat vector that standard filters overlook. The third study applies Weighted Focal Structure Analysis (WFSA) to YouTube recommendation graphs during major events like the 2024 Taiwan presidential election and global tariff debates, demonstrating how algorithmic ranking and navigation depth concentrate structural power within specific content pathways.

Together, these studies highlight COSMOS’s international leadership in socio-cognitive security and ethical AI. By combining advanced mathematical modeling, natural language processing, and network science, COSMOS continues to deliver scalable, real-world insights to counter emerging cyber social threats across the globe.

In a recent Q&A feature published by Apprenticely, COSMOS Research Center director Prof. Nitin Agarwal reflected on a milestone judicial verdict holding tech giants Meta and YouTube liable for negligent, addictive platform design. The landmark ruling brings sweeping scrutiny to how major social media platforms operate, shifting public and legal focus toward the engineered mechanisms that drive modern digital environments.

Prof. Agarwal explained that social media algorithms are fundamentally optimized for attention maximization and engagement rather than user well-being. Powered by AI recommender systems, platforms leverage psychological triggers such as variable rewards, social validation, and the fear of missing out (FOMO). Features like infinite scroll and continuous autoplay intentionally strip away natural stopping cues, creating repetitive feedback loops that reinforce compulsive behavior. Because time-on-platform directly translates into advertising revenue, user retention is not an unintended side effect, but rather an explicitly engineered outcome.

Beyond individual psychological impacts, COSMOS’s research demonstrates how these platform vulnerabilities scale socially and politically. Supported by over $30 million in project funding from federal agencies, including the U.S. Department of War and the National Science Foundation, COSMOS studies how algorithms shape collective human behavior during crises and socio-political events. Adversarial actors routinely exploit these engagement-first models by deploying bots, coordinated posting, and outrage-driven content to game recommendation systems. By weaponizing human psychology and platform design, malicious actors effectively turn content curation algorithms into vectors for large-scale cognitive influence and disinformation.

Comparing these recent legal developments to the historical scrutiny faced by Big Tobacco, Prof. Agarwal outlined a potential path forward for tech regulation. As internal corporate knowledge and design intents come under increased judicial review, platforms may face mandatory duty-of-care standards, age protections for minors, and structural requirements for algorithmic transparency. Highlighting Arkansas Governor Sarah Huckabee Sanders’s initiative to tackle youth exposure and online harms, Prof. Agarwal noted growing momentum among policymakers. Serving as a member of the Governor’s AI Taskforce, he suggested that future AI systems could be reoriented away from pure attention harvesting and toward healthy engagement, fostering a more accountable and resilient digital ecosystem. 

Click here to read the full Q&A feature on Apprenticely.

COSMOS Research Center is proud to share that Prof. Nitin Agarwal, Maulden-Entergy Chair, Donaghey Distinguished Professor, and Founding Director of COSMOS at the University of Arkansas at Little Rock, has received the 2026 Faculty Excellence Award for Research and Creative Endeavors from the Donaghey College of Science, Technology, Engineering, and Mathematics (DCSTEM) College.

This prestigious recognition honors faculty whose research or creative endeavors have been particularly successful and have received recognition locally, nationally, and globally. Prof. Agarwal’s selection highlights his groundbreaking contributions to social computing, AI, cognitive security, online influence, and the study of digital and socio-cognitive behaviors across modern information and communication platforms.

At COSMOS, Prof. Agarwal leads research that advances understanding of how online narratives, influence campaigns, and digital behaviors emerge, evolve, and spread across social media and other networked environments. His work has supported the development of analytical tools and capabilities used to study cognitive threats, online manipulation, and adversarial information activities.

Prof. Agarwal’s research portfolio has received more than $60 million in funding from agencies including the U.S. Department of Defense, DARPA, the National Science Foundation, and the Department of State, and his publicly available social media analysis tools have supported organizations such as U.S. SOCOM, CYBERCOMMAND, EUCOM, INDOPACOM, NATO StratCom, defense agencies across Europe, Australia, and Singapore, and the Arkansas Attorney General’s office; his scholarly record includes 12 books and more than 400 peer-reviewed research articles with over 7,000 citations and more than 30 best paper awards, and his work has been recognized by NATO’s Innovation Hub as a top solution for countering cognitive warfare, while his COVID-19 Scam Tracker was recognized by the World Health Organization as a key technological innovation during the pandemic.

Beyond his research achievements, Prof. Agarwal has mentored and graduated close to 100 students, many of whom have gone on to leadership roles in academia and top global companies. This recognition reflects not only his sustained research excellence but also COSMOS’s continued commitment to advancing impactful, interdisciplinary research at the intersection of AI, social computing, cognitive security, and smart health.

We are thrilled to announce that three new COSMOS studies have been accepted and presented at the 30th Pacific Asia Conference on Information Systems (PACIS 2026) in Jakarta, Indonesia. The Pacific Asia Conference on Information Systems (PACIS) is one of the leading international conferences in information systems and the only Association for Information Systems conference dedicated to the Pacific Asia region. 

Publishing here is a strategic milestone because our current research extensively analyzes digital protest movements in Indonesia, specifically examining the widespread 2025 protests that fragmented across platforms such as X, YouTube, TikTok, and Instagram. Supported by research grants focused on the Indo-Pacific region, presenting our findings in Jakarta places our research directly at the heart of the region we are studying.

Globally, these studies redefine how we track digital collective action across fragmented digital ecosystems. By moving beyond outdated models that focus solely on individual influencers, our new frameworks provide platform governors, policymakers, and global researchers with replicable tools to audit AI-driven recommendation bias, track the spread of narratives, and monitor the behavioral unity of online movements before they peak. 

The first paper, “Narrative Shifts in YouTube Recommendation Networks: A Depth-Based Analysis of the Indonesian Protest,” investigates how YouTube’s AI recommendation algorithms actively reshape user exposure to political events. Using the Contextual Influence–Focal Structure Analysis (CI-FSA) framework, it reveals that as users dive deeper into algorithmic recommendations, protest-heavy content is gradually diluted and replaced by apolitical or government-response narratives. 

The second study, “Toxic Unity: Behavioral Homogenization and the Inverse Toxicity-Cohesion Mechanism in Protests”, applies the Multilayer Behavioral Homogenization Framework (MBHF) to explore a counterintuitive paradox. By examining over 147,000 tweets, the paper discovers that extreme behavioral cohesion during protests is inversely related to collective toxicity, proving that group unity is driven primarily by shared ideological alignment rather than direct social interaction. 

Finally, “Focal Collective Actors as Narrative Structures: Cross-Platform Brokerage in Digital Protest” examines how protest narratives persist across highly fragmented social media platforms such as X, TikTok, Instagram, and YouTube, where users rarely share the same identities. Applying the CI-FSA framework, we show that cross-platform continuity is sustained not by highly visible individual influencers but by structurally embedded “collective brokers” who link different discursive spaces.

Collectively, these publications reinforce COSMOS’s continued mission to explain, measure, and mitigate complex information challenges in today’s digitally connected world.

COSMOS Research Center is pleased to announce a recent publication in Springer’s Journal of Social Network Analysis and Mining titled “Discovering Cross-Platform Narrative Flow Templates Using Frequent Subgraph Mining,” authored by Ridwan Amure and Nitin Agarwal.

Social media narratives rarely remain confined to a single platform. They often move across digital ecosystems, adapting to the unique affordances of each space, from long-form commentary on YouTube to short-form expression on TikTok, public discourse on X, and visual storytelling on Instagram. This study addresses the need to better understand how narratives evolve across platforms over time.

The paper introduces a graph-mining framework for discovering narrative flow templates, which are recurring structural patterns that describe how narratives propagate between platforms. By adapting the SoPaGraMi algorithm for frequent subgraph mining, the study moves beyond traditional sequence-based approaches and captures more complex, non-linear diffusion structures in cross-platform discourse.

Using the recent Tariff War as a case study, the research analyzes narrative activity across YouTube, TikTok, X, and Instagram. The framework uncovers distinct diffusion motifs, including platform pathways associated with pro-American, pro-Chinese, and cooperative US-China narratives. These findings show how different platforms can serve complementary analytical, expressive, and visual functions in shaping online discourse.

This work contributes to the growing field of computational social science by offering a scalable approach for identifying coherent cross-platform diffusion structures. It advances COSMOS’s broader efforts to understand how narratives spread through multimodal social ecosystems and how data-driven methods can support deeper analysis of online information dynamics.

Read the full article here.

At the prestigious 14th International Conference on Complex Networks and their Applications (Complex Networks 2025) in New York, researchers from the COSMOS Research Center unveiled groundbreaking advancements in content moderation. These presentations shift the focus of digital governance away from isolated internet trolls, offering instead smart, automated ways to break up coordinated groups spreading online hate.

The first study, “Optimizing Focal Toxic Structure Selection for Social Network Disruption,” introduces a mathematical framework that combines Weighted Focal Structure Analysis (WFSA) with optimization algorithms. Instead of hunting down individual users, this tool precisely selects and breaks up tight-knit toxic communities. When tested on networks with over 520,000 users across platforms like Telegram and Twitter, this approach successfully removed up to 85% of network toxicity. This strategy proved 4.2 times more efficient than traditional methods while staying strictly under a platform’s realistic user ban limits.

The second study, “Toxicity-Driven Behavioral Homogenization in Multilayer Political Networks,” explores how people interact during political crises like the Russia-Ukraine conflict. Researchers discovered that fiercely opposing political groups often end up adopting the exact same toxic behaviors. By tracking users across multiple levels of interaction, they proved that online hate spreads primarily through shared political beliefs and ideological alignment, rather than through genuine social friendships.

The third study, “Weighted Focal Structure Analysis for Coordinated Toxicity Propagation in Social Networks,” focuses on how these toxic groups amplify harm. Using advanced simulations that model the spread of toxicity like a virus, the researchers found that coordinated groups spread harmful content up to 96.9% faster than individual influencers can on their own. They also discovered that the most accurate way to predict this spread is by using a model that specifically accounts for human skepticism, reflecting our natural resistance to harmful content.

While each study uses advanced network science, they approach digital toxicity from different, complementary angles. Together, these latest innovations from the COSMOS Research Center offer major digital platforms a definitive, highly scalable blueprint for safely dismantling organized disinformation campaigns and protecting healthy democratic conversations online.