nitin agarwal

What role do you play at COSMOS?

I work as a Data Engineer, supporting research through data engineering, analytics, and AI-driven workflows. This includes working with social media data, developing research workflows, experimenting with LLMs, and applying network analysis. I enjoy being at the intersection of research and engineering and helping transform ideas into practical and scalable solutions.

Please share a bit about your professional background and experience

I hold a Ph.D. in Computer Science and have experience across both academia and industry. My background includes teaching, research, software development, and data analytics, with research interests in areas such as social network analysis, privacy, and decentralized online social networks. I have always enjoyed work that combines computing with questions about people, information, and how we interact online.

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

I was drawn to COSMOS because of its interdisciplinary research and its focus on problems with meaningful societal impact. With my own background in social networks and online communities, I felt a natural connection with the socio-computational research led by Prof. Agarwal.

What particularly stood out to me is Prof. Agarwal’s vision and the way he has built COSMOS around research that goes beyond technical results and aims for meaningful real-world impact. I admire his ability to see the bigger picture, identify important research directions, and inspire us to think beyond the immediate task. He encourages new ideas, creates opportunities to explore them, and challenges us to think about how our research can make a broader contribution. The ambitious and forward-looking research culture he has created at COSMOS, along with his commitment to both impactful research and the growth of the people around him, were major reasons I wanted to be part of the center.

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?

I see my role at COSMOS as an opportunity to deepen my experience in data engineering, LLMs, and AI-driven research while connecting these areas with my background in network science and social computing. I am particularly interested in continuing to bridge research and engineering, taking research questions from ideas to approaches that can be implemented, tested, and ultimately put into practice.

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

Be curious, ask questions, and don’t expect to understand everything right away. There is a lot happening at COSMOS, and sometimes understanding why you are doing something is just as important as figuring out how to do it. I would also say not to hesitate to bring your own ideas to the table. An idea may start very simple, but conversations with others can take it in directions you did not initially expect.

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 want to learn from them?

I would choose Grace Hopper. I admire not only her contributions to computer science but also her curiosity and willingness to challenge conventional ways of thinking. I would love to hear what it was like to work in computing when so much of the field was still being invented. I would also be curious to hear what she thinks about today’s AI landscape, what excites her, what she thinks we might be overcomplicating, and where she thinks computing is headed next.

Why I Wrote the Book

I wrote Practical System Design: Building Reliable Systems Through Production Failures to explore why systems that appear correct during design reviews fail under real production conditions. Drawing on my experience in research, robotics, cloud infrastructure, high-traffic consumer platforms, and financial services, I examine fourteen incident-based case studies covering database connection pools, retries, slow dependencies, caching, containers, autoscaling, replication, observability, circuit breakers, rate limits, load shedding, and deployment strategies. Through these examples, I aim to help engineers understand how workloads, latency, concurrency, and resource limits affect system behavior and use measurable evidence to identify failure causes and design more reliable systems. 

Learning to Ask Better Questions

Production work changed the questions I asked during system design. Choosing a database, cache, messaging system, or cloud service matters, but the component name alone does not tell us whether a design can support its workload.

One example in the book begins with a service handling about 400 requests per second while each request occupies a database connection for roughly 70 milliseconds. That workload requires about 28 concurrent connections. At 560 requests per second and 105 milliseconds of connection occupancy, the requirement increases to about 59. A pool configured for 40 connections now has requests waiting even though the database technology and application architecture have not changed.

That calculation is simple, but it changes the investigation. Instead of saying the service is under high load, an engineer can identify the constrained resource, measure why demand for it increased, and evaluate whether a proposed correction changes the condition that produced the failure. I wanted the book to develop that habit of reasoning.

What COSMOS Contributed

My years at the COSMOS Research Center under Prof. Agrwal’s supervision played an important role in shaping how I approach engineering and problem-solving. Prof. Agarwal has brought computer science together with social and behavioral research, using large-scale data, network analysis, AI, machine learning, and computational methods to study online behavior and complex digital environments at COSMOS. Prof. Agarwal taught us to develop software that supports research by collecting, processing, and analyzing data, evaluating assumptions, and producing reliable tools for others to use. This experience helped me recognize that research and production engineering are closely connected, as reliability, observability, resource limits, deployment, and reproducibility become essential when researchers depend on software and data pipelines. I also learned the importance of using evidence to investigate problems, test hypotheses, and revise explanations based on observations. These research-driven practices continue to influence how I identify system failures, analyze performance, and develop reliable engineering solutions. 

Writing the Book

I did not want to write a catalog of technologies or a collection of architecture diagrams. There are already many good resources for learning what a cache, message queue, load balancer, or circuit breaker is. I was more interested in what happens when those components interact under production conditions.

That meant asking questions such as: If a dependency slows from 80 milliseconds to two seconds, how much concurrency does the caller now require? If requests time out and retry, how many additional attempts reach that dependency? If thousands of requests miss the same cache entry, what reaches the database? If autoscaling adds application instances while the database or external provider has a fixed concurrency ceiling, what did the additional instances actually solve?

The writing process involved repeatedly tracing each incident from a design or configuration decision to a runtime condition, then to a resource or dependency, a measurable change, and finally the customer or operational consequence. I rewrote sections when they explained a concept without helping the reader decide what to measure or change.

The diagrams went through the same process. I wanted a latency trace, connection-pool model, cache flow, or deployment figure to help the reader answer a question. If a diagram only made the page look more technical, it did not belong in the book.

Why This Matters to COSMOS Researchers

This is where I think the book connects most directly with the work being done at COSMOS today. Under Prof. Agarwal’s leadership, the center studies complex, multi-platform information environments and develops computational tools for analysts, policymakers, and researchers. As data volume, sources, models, and AI components increase, the supporting infrastructure directly dictates whether results can be produced, reproduced, and trusted. For example, if a data pipeline hits a slow external API, unrestricted retries can consume workers needed by other sources, creating dataset gaps that look like data anomalies when the real cause is a timeout or exhausted connection pool. Metrics that track collection latency, queue duration, and API errors make these issues far easier to diagnose.

The same principles apply to computational experiments and overall research validity. Changes in package versions or runtime environments can alter analytical outcomes, making containers and controlled image promotion essential for reproducibility, while metrics like queue depth offer far clearer insight into processing bottlenecks than CPU utilization alone. COSMOS has a strong history of turning research into software tools with real social impact. Reliability engineering supports that mission by making technical infrastructure easier to measure, reproduce, and diagnose, giving researchers practical models to reason about these conditions without needing to become site reliability engineers.

What Comes Next

Publishing Practical System Design has strengthened my interest in exploring how AI can support production-risk analysis. I am developing ways for AI agents to review system architectures and operational evidence before a release, identify potential failure modes, and help engineers determine what requires further investigation. I plan to expand my educational work by teaching system design through real-world incidents, encouraging learners to examine measurements, identify system limitations, challenge initial assumptions, and use evidence to determine what happened and how to improve system reliability. These approaches reflect the research-driven problem-solving habits I developed at COSMOS, which continue to shape my engineering work today.

In this month’s Research Spotlight, COSMOS highlights two studies presented at the 14th International Conference on Complex Networks and Their Applications (Complex Networks) in New York, USA. Led by Prof. Nitin Agarwal, the research examines two connected questions at the heart of modern digital influence: which groups drive information through social networks, and how narratives continue to spread once they cross from one platform to another?

The first study, “CI-FSA: Toward Scalable Discovery of Influential Groups in Social Networks,” challenges the idea that influence is driven primarily by a few powerful individuals. Instead, the research shows that influence often emerges from tightly connected groups working collectively. COSMOS introduces CI-FSA, a scalable method that identifies these influential subnetworks by considering both their structural cohesion and their ability to propagate information. Tested across multiple networks, including data from the 2024 Taiwan election and the Enron email communication network, the approach offers an interpretable way to uncover groups that may play important roles in coordinated influence, manipulation, and narrative formation.

The second study, “Modeling Cross-Platform Narrative Contagion Using a Multiplex SEIRZ Framework,” looks at what happens when narratives move beyond a single platform. Using data from Instagram, TikTok, X, and YouTube surrounding the 2024 Taiwanese presidential election, COSMOS models these platforms as interconnected layers of a larger information ecosystem. The findings show that narrative spread depends strongly on both exposure intensity and connections between platforms, demonstrating why studying social media platforms in isolation can overlook important pathways through which information gains reach and influence. 

Together, these studies provide a more complete picture of digital influence: connected groups can create the momentum behind information spread, while cross-platform pathways can extend that influence into new audiences and environments.

This work reflects Prof. Agarwal and COSMOS’s broader mission to develop scalable, interpretable methods for understanding complex online behavior. For science, these studies advance network analysis and cross-platform diffusion modeling. For society, they provide researchers, platforms, and policymakers with better tools to identify coordinated influence, monitor emerging narratives, and understand how information moves through increasingly interconnected digital ecosystems.

A groundbreaking new study, “Examining Linguistic Strategies for Digital Mobilization in Social Movements on Instagram and TikTok” in the Communications of the Association for Information Systems journal (vol. 58), reveals how subtle shifts in social media language serve as the primary engine driving political mobilization, civil unrest, and viral malicious campaigns on major digital platforms. By combining core principles from linguistics and communication theory, researchers from the COSMOS team mapped how specific types of functional vocabulary shape the trajectory of activist movements across Instagram and TikTok. By studying over 130,000 social media posts and comments, the authors show how words can influence people’s reactions, decisions, and actions, leading them to gather and stay involved over time.

To organize their analysis, the researchers grouped language into four main styles based on what the words try to achieve. Conative (or, direct action) language uses strong verbs to tell people what to do, like joining a rally or sharing a post. Informational language focuses on facts, dates, names, and locations to give context. Emotional language shares feelings like anger, hope, or fear to bring people closer together. Finally, expressive language shares opinions and judgments to help supporters agree on a common cause. The authors looked at major political events in Brazil and Taiwan to see these language styles in action. In Brazil, researchers analyzed posts surrounding the post-election protests and riots. They found that anti-government groups heavily used action-focused words early on to get people into the streets. On the other hand, pro-government groups used factual and emotional language to identify protesters and build long-term support. In Taiwan, during the 2024 presidential election, TikTok users involved in misleading information campaigns used urgent commands to spread claims of voter fraud, while fact-checkers used opinionated and factual words to coordinate their responses and rebuild trust.

By mapping these post histories, the researchers discovered that successful movements naturally shift their language as they grow. Movements often start with call-to-action words to gain initial attention, shift to factual language to build context, and finish with emotional language to keep supporters connected over time. These findings can help social media platforms and researchers spot early signs of offline protests or dangerous information campaigns before they spread too far.  

Social media platforms are no longer just virtual town squares; they are high-stakes battlegrounds where algorithms, viral narratives, and coordinated toxicity directly trigger real-world action. Uncovering these underlying mechanics was the focus of the COSMOS Research Center at the 18th International Conference on Advances in Social Networks Analysis and Mining in Rabat, Morocco, on August 24-27, 2026. ASONAM is a premier global venue at the intersection of information systems, behavioral science, and big data analytics.

ASONAM is recognized internationally as a premier global forum for network science, big data analytics, and computational social science. The conference provides an essential platform for addressing critical challenges across information systems, focusing on algorithmic manipulation, social mobilization, dynamic toxicity modeling, and platform governance during acute geopolitical events. Presenting a major body of work at this highly competitive venue highlights COSMOS’s ongoing commitment to advancing data-driven tools that safeguard public discourse and foster digital resilience.

Publishing at ASONAM 2026 directly advances COSMOS’s mission to analyze online influence, cognitive security, and digital platform resilience, supported in part by major federal research grants dedicated to national defense and information stability. Through these projects, COSMOS researchers examine real-world social movements, algorithmic recommendation traps, and cross-platform narrative trajectories across key international contexts, including the 2025 Indonesian protests, YouTube recommendation networks, global blogging ecosystems, and multi-platform communication channels.

The 13 accepted studies fall into three main research clusters that push the boundaries of computational social science. The first cluster focuses on AI-based algorithmic recommendation systems and digital platforms, introducing newly updated analytics infrastructure including VTracker 2.0, BlogTracker 2.0, and DataVerse. These tools use large language models (LLMs) and network science to detect content traps, map evolving YouTube recommendation pathways, and manage multi-platform data at scale. The second cluster explores real-world social mobilization and narrative diffusion, examining how symbolic structures, emotional coherence, and key focal groups drove online and offline activity during the 2025 Indonesian protests. The third cluster addresses online toxicity, covert influence, and dynamic modeling, using epidemic propagation theories, multiplex network algorithms, and temporal forecasting models to trace how toxic behavior and cross-platform campaigns spread over time.

By combining advanced AI, computational network science, and human behavioral analysis, these studies offer practical frameworks for identifying online threats before they scale. Together, this research reinforces COSMOS’s commitment to creating transparent, secure, and resilient digital spaces that preserve democratic integrity worldwide.

COSMOS Research Center is proud to celebrate a Best Paper Award at the International Conference on Integration of Digital and AI-related Technologies (IntAI 2026), held June 7–11, 2026, in Porto, Portugal.

The award-winning study, “Narrative Contagion Under Competing Information Environment: An Epidemiological Modeling Analysis of Tariff Discourse on X and Weibo,” authored by Prof. Nitin Agarwal, examines how competing narratives surrounding the 2025 tariff discourse spread across two very different digital environments.

Using more than 107,000 posts from X and Weibo, COSMOS applies epidemiological modeling to treat narratives much like contagion, measuring how effectively ideas are transmitted, reinforced, challenged, or suppressed through online engagement. 

The findings reveal a striking contrast. On X, both agreeing and disagreeing narratives remained below the threshold for self-sustaining spread. On Weibo, agreeing narratives reached a reproduction number of 1.8, while disagreeing narratives fell to 0.2, highlighting how platform architecture and governance can strongly influence the spread of competing viewpoints. 

This work advances COSMOS’s broader research on narrative dynamics, online influence, and cognitive security by showing that the spread of information depends not only on the message itself, but also on the digital environment through which it travels. The Best Paper recognition further reflects Prof. Agarwal and COSMOS’s continued leadership in developing AI-based computational approaches for understanding complex social information ecosystems.

COSMOS welcomes Vida Torgbe as a new Graduate Assistant, where she contributes to research on social media behaviors and dissemination efforts. She is currently pursuing an MS in Information Science at UA Little Rock and brings a multidisciplinary background in social sciences and data analysis. At COSMOS, she works with Prof. Nitin Agarwal to strengthen her skills in social media analytics, data science, and interdisciplinary research while contributing to projects that address complex societal challenges.

Vida shared with us her journey to COSMOS, her goals, and aspirations beyond!

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 to conduct experiments to evaluate hypotheses. Additionally, I contribute to the center’s research dissemination 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 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.

In this month’s research spotlight, COSMOS highlights three studies presented at the 14th International Conference on Complex Networks and their Applications in New York, USA, exploring the underlying mechanics of digital influence, grievance clustering, and narrative transmission. When major geopolitical events unfold, social media platforms function as dynamic information ecosystems where personal reactions can swiftly evolve into structured movements. Understanding how everyday concerns translate into connected communication networks, which policy storylines achieve sustained reach, and how visual symbols drive engagement is crucial for deciphering modern collective behavior.

The first study, “The Network Effect of Shared Grievances: Measuring Collective Concern of Tariff Policy,” examines how tariff-related grievances mobilize online communities. Using a multi-stage framework that integrates GPT-4o-mini classification, HDBSCAN clustering, and user-mention networks across six months of data, the authors analyzed the structural divergence between consumer and economic grievances. The results reveal that tangible consumer hardships form broad, overlapping clusters with fluid boundaries that experience sudden, synchronized surges around external triggering events. In contrast, macroeconomic concerns organize into dense, specialized clusters centered on specific trade sectors and policy figures, demonstrating how online grievance networks form the pre-mobilization groundwork for broader collective action.

The second study, “Modeling the Propagation Dynamics of Visual Elements with Epidemiological Frameworks,” introduces a novel computational approach to track how visual symbols spread across video platforms during information manipulation in Taiwan. Utilizing the PRISM color-shift model to extract key video frames alongside vision-language models for symbol classification, the authors evaluated five epidemiological models to trace symbol dissemination across nearly 2,000 YouTube videos. The SEIZ framework, which explicitly accounts for an undecided or skeptical audience compartment, achieved the highest fidelity by dropping modeling error to 0.45%. Crucially, the findings show that transmission effectiveness does not depend on symbol saturation, but rather on strategic pairing, with political symbols and dual-symbol combinations achieving near-optimal viral spread.

The third study, “How Tariff War Discourse Spreads on Social Media? A Study of Narrative Outbreak,” bridges qualitative narrative analysis with mathematical epidemiology to track competing storylines during international trade disputes. By clustering multilingual Twitter discourse and applying GPT-4o with structured prompts, the authors extracted five core narratives and modeled their transmission rates using bounded parameter optimization algorithms, including Nelder-Mead and L-BFGS-B. The analysis found that analytically grounded narratives, specifically those focused on reciprocal tariffs, strategic retaliation, and institutional trade responses, consistently demonstrated the highest transmissibility and lowest error. Meanwhile, emotionally charged and satirical narratives exhibited volatile decay, proving that policy-rich, expert-amplified content sustains longer digital lifespans.

Together, these studies advance COSMOS’s mission to build robust, interpretable approaches for analyzing complex digital ecosystems. For science, they bridge mathematical diffusion models with qualitative narrative extraction and AI-enhanced computer vision, offering scalable methodologies to audit information flows. For society, they provide platform owners, researchers, and policymakers with actionable diagnostic tools to identify emerging collective narratives, evaluate visual campaigns, and anticipate the trajectory of contentious public discourse.

COSMOS Research Center is pleased to announce a new publication in Springer’s Journal of Social Network Analysis and Mining titled “Identifying Cohesive Narrative Formations in Cross-Platform Trade-War Discourse Using Weighted Contextual Focal Structures Analysis.”

Geopolitical discourse across social media platforms rarely unfolds in isolated silos. Instead, complex storylines emerge through the dynamic interplay of user interactions, semantic alignment, and cross-platform mechanics. Traditional Contextual Focal Structure Analysis (CFSA) models have helped identify context-specific user groups, but they treat all connections equally and analyze apps separately. This makes it difficult to capture subtle semantic nuances, stance homogeneity, or varying interaction intensities across platforms. The study addresses these issues by introducing Weighted Contextual Focal Structures Analysis (W-CFSA), a novel framework that evaluates multiple platforms at once. By combining custom platform weights, interaction frequency, and text similarity, W-CFSA analyzes the entire network layout to identify highly connected groups that share the same core message.

Using a dataset of 73,266 posts collected from YouTube, TikTok, X (formerly Twitter), and Instagram during the 2025 U.S.-China trade policy discussions, the researchers systematically benchmarked W-CFSA against size-matched eigenvector-centrality baseline groups. The findings demonstrated that W-CFSA substantially outperformed standard baseline methods across all structural metrics. The identified focal sets maintained consistently higher internal weighted density and clustering coefficients while exerting a far greater impact on global network transitivity when suspended. Qualitative analysis further revealed that these focal sets correspond to distinct narrative architectures like specific causal interpretations of policy volatility or strategic leverage, rather than simple stance or sentiment categories.

This research advances computational social science by offering trust, safety, and strategic communication teams a methodological framework to trace cross-platform narrative propagation at its core. By bridging network topology with semantic context, COSMOS continues to pave the way for a deeper understanding of digital discourse and information ecosystems.

Click here to read the full article.

We are thrilled to announce that three new COSMOS studies have been accepted and presented at the 32nd Americas Conference on Information Systems (AMCIS 2026) in Reno, Nevada.

Organized by the Association for Information Systems, AMCIS brings together leading researchers examining how interface design and algorithms actively shape human judgment. It serves as a vital venue for dissecting the friction between platform mechanics and public understanding, particularly around toxic evasion, recommendation rabbit holes, and the cultural cues that anchor voter sentiment. Presenting our work here reinforces COSMOS’s commitment to building transparent and auditable AI tools that help communities navigate increasingly complex digital spaces.

Presenting at AMCIS directly reflects our work supported by federal grants dedicated to national defense, platform accountability, and socio-technical resilience. Led by Prof. Nitin Agarwal, these projects examine how users experience the internet during volatile moments, connecting the dots between algorithmic architecture and human cognition. Through this research, our team analyzes hundreds of thousands of user interactions across high-stakes arenas, including polarized debates on Reddit, multi-hop recommendation trails on YouTube during international trade disputes, and information manipulation efforts in Taiwan.

The three studies presented at AMCIS showcase COSMOS’s interdisciplinary approach, which fuses social computing and Artificial Intelligence (AI)-based techniques to diagnose and mitigate risks across digital platforms. The first study, led by Prof. Agarwal, investigates toxic algospeak across 13,780 high-evasion Reddit posts out of 721,236 climate discussions, uncovering a dual-mechanism cognitive framework where surface-level euphemisms trigger universal metalinguistic detection without harm discounting, whereas domain-specific jargon misuse requires epistemic sophistication, which enables experts to discount perceived harm by 16.5%. This distinction proves that one-size-fits-all moderation fails and offers practical governance guidance to separate user education from expert-led validation. 

The second study, led by Prof. Agarwal, introduces TrapIntensity, an auditable sociotechnical framework that measures AI algorithmic entrapment in YouTube recommendation graphs by pairing hop-aware random-walk network simulations with theory-driven persuasion cue extraction using large language models. This dual-layer metric reveals whether engagement concentration is driven by structural lock-in or rhetorical persuasion across different contexts, showing structural dominance in trade dispute networks and persuasion dominance in socio-political discourses. 

The third study examines a multimodal dataset of 1,973 YouTube videos and over 342,000 comments from Taiwan’s information environment, applying the PRISM perceptual keyframe framework alongside vision-language models to show that social, cultural, and political symbols systematically drive higher viewer interaction in information campaigns, with cultural symbols eliciting the strongest emotional resonance and driving expressed trust in Taiwanese institutions to nearly 45%. 

Together, these publications reinforce COSMOS’s continued mission to explain, measure, and mitigate complex digital challenges by bridging advanced AI methodologies with human-centered socio-technical systems.