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Yuto Tamura

Publications and source records attributed to Yuto Tamura.

2 recordsLinked to original sources
arXiv2026

Converging Naming Styles, Persistent Network Locality: GitHub in the LLM Era

Social conventions often emerge through repeated interactions within social networks, allowing shared practices to coexist with variation across groups. Large language models (LLMs) introduce a potentially different coordination structure: a small number of widely used models can expose socially distant users to similar patterns and suggestions. Whether broad convergence under such shared technological influences eliminates network-local variation remains unclear. We examine this question in software development, where identifier naming styles provide observable conventions and LLM-based tools have rapidly diffused. Using public GitHub repositories created between 2015 and September 2025 across six programming languages, we characterize naming styles with 27 features and examine their association with detected LLM-related commits, their diversity across repository creation cohorts, and their relationship to owner proximity in a large-scale collaboration network. Repositories with detected LLM-related commits tend to use longer identifiers and, in several languages, make greater use of naming patterns already prevalent within the language. We also observe lower naming-style diversity in recent creation cohorts, with marked declines appearing around 2023-2024 in several languages, although their timing and trajectories differ. At the same time, network locality persists: in five of the six languages, repositories whose owners are closer in the collaboration network remain more similar in naming style even among recent, more homogeneous cohorts. These findings show that aggregate convergence and network-local variation can coexist, highlighting the need to examine not only how much cultural variation remains, but also how that variation continues to be structured by human social relationships in the era of widely shared AI systems.

cs.SI↗
arXiv2026

Revisiting Information Diffusion Beyond Explicit Social Ties: A Study of Implicit-Link Diffusion on Twitter

Information diffusion on social media platforms is often assumed to occur primarily through explicit social connections, such as follower or friend ties. However, information frequently propagates beyond these observable ties -- through external websites, search engines, or algorithmic recommendations -- creating implicit links. How the presence of implicit links affects the diffusion process remains unclear. In this study, we investigate the characteristics of implicit links on Twitter using four large-scale datasets. Our analysis reveals that users who are farther from the original source in the social network are more likely to engage in diffusion via implicit links. Although implicit links contribute less to the overall diffusion volume than explicit links, they play a distinct role in disseminating content across diverse and topologically distant communities. We further examine the user attributes associated with the formation of implicit links and show that these features are unevenly distributed across the network and exhibit moderate levels of homophily and monophily. Together, these findings demonstrate that implicit links exert a meaningful influence on information diffusion and highlight the importance of incorporating them into models of diffusion and social influence.

cs.SI↗