Luca Maria Aiello

@lajello.bsky.social

Computational Social Science researcher interested in Human Coordination and emergence of new phenomena in human-AI societies. Professor of Data Science at the IT University of Copenhagen. Network Science | NLP | AI agents http://www.lajello.com

Chapter 19: @lajello.bsky.social on Learning the Social Pragmatics of Language. A thought-provoking chapter arguing that CSS should move beyond syntax and semantics to study intentions, relationships, and social action expressed through language. A compelling vision for next generation of NLP & CSS.

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Taha Yasseri@tahayasseri.bsky.social · 2w ago

Chapter 18: @janlo.de on Exploring Theory with Agent-Based Modeling and Simulation. Showing how agent-based models can move CSS beyond prediction toward explanation, revealing how simple behavioural rules can generate complex social phenomena such as segregation, polarization, and filter bubbles.

Thrilled to share that our work has been accepted at @icwsm.bsky.social 2026 🌟 Thanks @cerenbudak.bsky.social and @lajello.bsky.social for the great collaboration! 📖 arxiv.org/abs/2510.01757

Arianna Pera@ariannapera.bsky.social · 10mo ago

How does online communication adapt to organizational context? In a new pre-print with @cerenbudak.bsky.social and @lajello.bsky.social, we study US labor unions’ Facebook use of discourse frames around wins and losses in representation elections. 🧵 📖 arxiv.org/abs/2510.01757

AI can now answer questions about video content, transforming how people consume information on the Web. An experiment with 900+ participants found that AI improves speed and accuracy of retrieval BUT, users become overreliant on AI answers, accepting wrong answers without any loss of confidence.

A two barplots side by side.The left plot shows accuracy of information retrieval (y-axis, ranging from 0 to 100%) across three conditions (No AI, AI, and AI providing wrong answers). The right plot shows self-reported confidence in the answer correctness (y-axis, ranging from 0 to 5) across the same three conditions. Across the two plots, each condition contains two bars: one for users who watched the relevant segment of the video containing the answer to a question, and one for users who did NOT watch the segment. The plot conveys the following messages. 1) In the "No AI" condition, people who did not watch the video provide far less accurate responses. 2) In the "AI" condition, accuracy is high regardless of whether people watched the video or not. 3) in the "AI providing wrong answer" condition, accuracy drops to the minimum values for people who did not check the video. 4) Self-reported confidence in the answers is always high across all conditions, with just a tiny difference between the watched vs. non-watched scenarios

Computational Social Scientists in the Nordics, unite! 🇩🇰🇫🇮🇳🇴🇸🇪🇮🇸 The brand new Nordic Society for CSS welcomes all researchers and practitioners based in the Nordics. The Society will promote student mobility, events, and education initiatives. Join for free: nosocss.org/join.html.