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.