activity
20212026
most citedCrowdsourcing subjective annotations using pairwise comparisons reduces bias and error compared to the majority-vote method

19 citations · 34 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SI2026

Competing for a Finite Pool of Attention in Social Media? How a New Geopolitical Conflict Reshapes Engagement in Bluesky

Kamand Kalashi, Arash Badie-Modiri, Ali Salloum +3

Major geopolitical crises can rapidly reshape online public attention. Yet population-level increases in discussion volume about a new crisis reveal little about how users accommod…

cs.LG2026

Decentralised Federated Learning over Temporal Networks: The Role of Heterogeneities

Arash Badie-Modiri, Chiara Boldrini, Lorenzo Valerio +2

Decentralised federated learning, based on peer-to-peer communication, is increasingly proposed for on-device training of machine learning models, promising a privacy-preserving, c…

cs.SI2025★ 1 cited

Incivility in Public Health Policy Discussions Spills Over to Public Engagement with Climate Issues

Hasti Narimanzadeh, Arash Badie-Modiri, Iuliia Smirnova +1

Affective polarization and political sorting drive public antagonism around climate change and other issues at the science-policy nexus. We study cross-domain spillover of incivili…

cs.HC2023★ 19 cited

Crowdsourcing subjective annotations using pairwise comparisons reduces bias and error compared to the majority-vote method

Hasti Narimanzadeh, Arash Badie-Modiri, Iuliia Smirnova +1

How to better reduce measurement variability and bias introduced by subjectivity in crowdsourced labelling remains an open question. We introduce a theoretical framework for unders…

physics.soc-ph2021★ 14 cited

Directed Percolation in Temporal Networks

Arash Badie-Modiri, Abbas K. Rizi, Márton Karsai +1

Connectivity and reachability on temporal networks, which can describe the spreading of a disease, decimation of information or the accessibility of a public transport system over…