Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Biased Opinion Dynamics: When the Devil is in the Details
Authors: Aris Anagnostopoulos, Luca Becchetti, Emilio Cruciani, Francesco Pasquale, Sara Rizzo
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We analyze the convergence of the resulting process under two wellknown update rules, namely majority and voter. The framework we propose exhibits a rich structure, with a nonobvious interplay between topology and underlying update rule. [...] The relatively simple, yet general, model that we consider allows analytical investigation of the following question: How does a particular combination of network structure and opinion dynamics affects convergence to global adoption of the dominant opinion? In particular, how conducive is a particular combination to rapid adoption? |
| Researcher Affiliation | Academia | Aris Anagnostopoulos1 , Luca Becchetti1 , Emilio Cruciani2 , Francesco Pasquale3 and Sara Rizzo4 1Sapienza, University of Rome, Italy 2Inria, I3S Lab, UCA, CNRS, Sophia Antipolis, France 3Tor Vergata, University of Rome, Italy 4Gran Sasso Science Institute, L Aquila, Italy |
| Pseudocode | No | The paper is theoretical and does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper and does not involve the use of datasets for training or experimentation. |
| Dataset Splits | No | This is a theoretical paper and does not involve dataset validation splits. |
| Hardware Specification | No | This is a theoretical paper and does not report on experiments requiring specific hardware specifications. |
| Software Dependencies | No | This is a theoretical paper and does not mention specific software dependencies with version numbers for replication. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup or hyperparameters. |