arXiv subjects

Masaki Chujyo

Publications and source records attributed to Masaki Chujyo.

7 recordsLinked to original sources

arXiv2026

From Detection to Characterization: A Large-Scale Study of Ragebait on Japanese X

Ragebait refers to online content intentionally designed to provoke anger or outrage and thereby increase attention and engagement. However, reliable large-scale detection and systematic analysis of ragebait remain limited, hindering efforts to understand its prevalence, impact, and mitigation. This study aims to develop an effective ragebait detection framework and to clarify the characteristics of ragebait at scale, providing a basis for understanding and mitigating emotionally provocative content online. We constructed a labeled dataset with the assistance of a large language model (LLM) and trained several Japanese language models for ragebait detection. The resulting ensemble classifier was then applied to a large-scale dataset of Japanese-language posts on X. Our analysis shows that ragebait is more prevalent in politically and socially contentious topics, including politics, discrimination, public health, and interpersonal conflict. Ragebait posts also spread faster and receive more negative reactions than non-ragebait posts, particularly anger, fear, disgust, sadness, and surprise. These findings demonstrate the utility of the proposed detector and provide a large-scale characterization of ragebait in Japanese online discourse.

cs.SI↗

arXiv2026

Network-distance decay of perceived online social support

Perceived social support can buffer stress, but how it is associated across online social networks at different graph distances remains unclear. Here we show that inferred perceived online social support in a large avatar communication application decays with network distance in a form better described, over the observed range, by a power-decay model than by a single exponential. We linked two-wave survey data from Pigg Party with behavioral logs, trained a random forest to infer perceived support for active users, and regressed Wave 2 scores on Wave 1 scores for users at hop distance $k$, adjusting for baseline support and covariates. Adjusted associations persisted across hops, consistent with power-law-like decay over the observed range. Individual-based simulations indicated that heterogeneous source-specific exponential decay rates can generate heavy-tailed aggregate decay. These results suggest that network position heterogeneity should be considered when characterizing distance-dependent associations among psychosocial states in online communities.

cs.SI↗

arXiv2026

Temporal Shifts and Causal Interactions of Emotions in Social and Mass Media: A Case Study of the "Reiwa Rice Riot" in Japan

In Japan, severe rice shortages in 2024 sparked widespread public controversy across both news media and social platforms, culminating in what has been termed the "Reiwa Rice Riot." This study proposes a framework to analyze the temporal dynamics and causal interactions of emotions expressed on X (formerly Twitter) and in news articles, using the "Reiwa Rice Riot" as a case study. While recent studies have shown that emotions mutually influence each other between social and mass media, the patterns and transmission pathways of such emotional shifts remain insufficiently understood. To address this gap, we applied a machine learning-based emotion classification grounded in Plutchik's eight basic emotions to analyze posts from X and domestic news articles. Our findings reveal that emotional shifts and information dissemination on X preceded those in news media. Furthermore, in both media platforms, the fear was initially the most dominant emotion, but over time intersected with hope which ultimately became the prevailing emotion. Our findings suggest that patterns in emotional expressions on social media may serve as a lens for exploring broader social dynamics.

cs.SI↗

arXiv2026

Impact of seed node position on network robustness under localized attacks

Localized attacks (LAs), where damage propagates from a single seed node to its neighbors, pose significant threats to the robustness of complex networks. Although previous studies have extensively analyzed network vulnerability under such attacks, they typically assume random seed node placement and evaluate average robustness. However, the structural position of the seed node can significantly impact the extent of damage. This study proposes the Localized Attack Vulnerability Index (LAVI), a node-level metric that quantifies the potential impact of a LA initiated at a specific node. LAVI quantifies the cumulative number of severed links during attack progression, capturing how local connectivity and topological position amplify the resulting damage. Numerical experiments on synthetic and real-world networks demonstrate that LAVI correlates more strongly with network robustness degradation than standard centrality measures, such as degree, closeness, and betweenness. Our findings highlight that classical centrality metrics fail to capture key dynamics of spatially localized failures, while LAVI provides an accurate and generalizable indicator of node vulnerability under such disruptions.

physics.soc-ph↗

arXiv2026

An attention economy model of co-evolution between content quality and audience selectivity

Human attention has become a scarce and strategically contested resource in digital environments. Content providers increasingly engage in excessive competition for visibility, often prioritizing attention-grabbing tactics over substantive quality. Despite extensive empirical evidence, however, there is a lack of theoretical models that explain the fundamental dynamics of the attention economy. Here, we develop a minimal mathematical framework to explain how content quality and audience attention coevolve under limited attention capacity. Using an evolutionary game approach, we model strategic feedback between providers, who decide how much effort to invest in production, and consumers, who choose whether to search selectively for high-quality content or to engage passively. Analytical and numerical results reveal three characteristic regimes of content dynamics: collapse, boundary, and coexistence. The transitions between these regimes depend on how effectively audiences can distinguish content quality. When audience discriminability is weak, both selective attention and high-quality production vanish, leading to informational collapse. When discriminability is sufficient and incentives are well aligned, high- and low-quality content dynamically coexist through feedback between audience selectivity and providers' effort. These findings identify two key conditions for sustaining a healthy information ecosystem: adequate discriminability among audiences and sufficient incentives for high-effort creation. The model provides a theoretical foundation for understanding how institutional and platform designs can prevent the degradation of content quality in the attention economy.

physics.soc-ph↗

arXiv2023

Optimal Network Robustness Against Attacks in Varying Degree Distributions

In varying degree distributions, we investigate the optimally robust networks against targeted attacks to nodes with higher degrees. In considering that a network tends to have more robustness with a smaller variance of degree distributions, we clarify the optimal robustness at random regular graphs in their comprehensive discrete or random perturbations. By comparing robustness measurements on them, we find that random regular graphs have the optimal robustness against attacks in varying degree distributions.

physics.soc-ph↗

arXiv2020

A loop enhancement strategy for network robustness

Many real systems are extremely vulnerable against attacks, since they are scale-free networks as commonly existing topological structure in them. Thus, in order to improve the robustness of connectivity, several edge rewiring methods have been so far proposed by enhancing degree-degree correlations. In fact, onion-like structures with positive degree-degree correlations are optimally robust against attacks. On the other hand, recent studies suggest that the robustness and loops are strongly related to each other. Therefore, we focus on enhancing loops as a new approach for improving the robustness. In this work, we propose edge rewiring methods and evaluate the effect on the robustness by applying to real networks. Our proposed methods are two types of rewirings in preserving degrees or not for investigating the effect of the degree modification on the robustness. Numerical results show that our proposed methods improve the robustness to the level as same or more than the state-of-the-art methods. Furthermore, our work shows that the following two points are more important for further improving the robustness. First, the robustness is strongly related to loops more than degree-degree correlations. Second, it significantly improves the robustness by reducing the gap between the maximum and minimum degrees.

physics.soc-ph↗