Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.20144

Structural Dependence, Imitation or Herding? A Multi-Agent Simulation of Global Intermedia Agenda Setting

Mohammad Yousuf·Arif Md Tareque Habib
View original source ↗Read source document ↗Export metadata ↓

Abstract

News outlets in poorer countries cover the world much as the leading outlets of rich countries do. Two explanations compete. One says they depend on Western news agencies for lack of resources. The other says they choose to copy prestigious outlets or whoever moves first. Observation cannot tell these apart. This study measured how closely 18 national outlets in Bangladesh, Kenya, South Africa, India, Malaysia and Nigeria followed four core outlets in how much attention they gave, which angle they took and when they published, on the wars in Ukraine and Gaza and two UN climate conferences in 2022-2024. It then ran a simulation in which language models acted as the outlets' editors and dependence, imitation and herding were switched on and off. Of nine tests, one held: outlets that relied more on wire copy published sooner after the core did. Tier and local stake showed no effect distinguishable from none. The herding test was supported and the imitation test was not. The simulation did not decide between the mechanisms, because the dependence rule, as fitted, reduced to publishing wire copy. Wire copy sets the clock of the periphery's foreign news. Whether it sets the agenda stays open.

Explore related subjects

cs.CYcs.MAcs.SI

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Mohammad Yousuf, Arif Md Tareque Habib. 2026-10-14. Structural Dependence, Imitation or Herding? A Multi-Agent Simulation of Global Intermedia Agenda Setting. https://arxiv.org/abs/2610.20144

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Partitioning Israeli Municipalities into Politically Homogeneous Cantons: A Constrained Spatial Clustering Approach

Israeli society has experienced significant political polarization in recent years, reflected in five Knesset elections held within a four-year period (2019-2022). Public discourse increasingly references hypothetical divisions of the country into politically homogeneous "cantons." This paper develops a data-driven algorithmic approach to explore such divisions using publicly available municipality-level election results and geographic boundary data. We partition 255 Israeli municipalities, including the Israeli local authorities in Judea and Samaria, into cantons of neighbouring municipalities that maximize internal political similarity. We evaluate four clustering algorithms (Simulated Annealing, Agglomerative Clustering with contiguity constraints, Louvain Community Detection, and a K-Means baseline), four feature representations, three distance metrics and six values of K (3-20): 264 configurations. BlocShares with Agglomerative clustering gives the highest silhouette score (0.987 with Cosine distance), while NMF with Louvain community detection best balances political homogeneity, silhouette quality (0.142) and interpretability, with stable boundaries across the five elections (ARI 0.927). The resulting K=5 partition identifies five politically coherent regions -- a mixed center-right metropolitan core around Tel Aviv, a right-wing region from Jerusalem through the south and the Judea and Samaria authorities, a right-leaning northern region, an Arab canton, and a canton of the seven predominantly Haredi cities, held together by political similarity rather than shared borders. Built from the 2019-2021 elections only, the cantons still differ significantly in every bloc's vote share in the held-out 2022 election. An interactive web application accompanies this work.

cs.CY↗
arXiv2026

Mapping data literacy trajectories in K-12 education

Data literacy skills are fundamental in computer science education. However, understanding how data-driven systems work represents a paradigm shift from traditional rule-based programming. We conducted a systematic literature review of 84 studies to understand K-12 learners' engagement with data across disciplines and contexts. We propose the data paradigms framework that categorises learning activities along two dimensions: (i) logic (knowledge-based or data-driven systems), and (ii) explainability (transparent or opaque models). We further apply the notion of learning trajectories to visualize the pathways learners follow across these distinct paradigms. We detail four distinct trajectories as a provocation for researchers and educators to reflect on how the notion of data literacy varies depending on the learning context. We suggest these trajectories could be useful to those concerned with the design of data literacy learning environments within and beyond CS education.

cs.CY↗
arXiv2026

Generative AI Availability, Grades, and Student Satisfaction at a Large University

The availability of generative AI (GenAI) has raised concerns that college students substitute GenAI for cognitive effort, earning high grades without learning. If this "GenAI substitution hypothesis" is true, grades should rise disproportionately in GenAI-susceptible courses--those relying more on assessments like take-home problem sets and essays rather than in-class exams. Student satisfaction (self-reported understanding and interest) may also shift. We test this hypothesis using syllabus and administrative data from a large U.S. university (2016-2025; 138,386 students; 72,730 course offerings). We measure GenAI susceptibility by extracting assessment types from syllabi with a human-validated LLM pipeline. We then use a difference-in-differences design comparing courses before and after ChatGPT's release, modeling COVID-19 effects as persistent or transient. Effects on grades and satisfaction are small and sensitive to COVID modeling choices, with no detectable differences by students' prior academic preparation. Exploratory analyses show that the link between pre-college test scores and grades has not detectably weakened in more susceptible courses, and that instructors have been making courses less susceptible since before ChatGPT. Our findings temper concerns that GenAI inflates grades and reduces satisfaction, partly because instructors may adapt their pedagogy.

cs.CY↗