platform social-effects evaluation

本文提出开放平台实地实验(OPFEs)方法，解决独立研究者在社交媒体上进行直接干预实验的限制问题。

This study addresses the ineffectiveness of platform governance assessments caused by overlooking participants’ adaptive responses. We propose a theoretical framework conceptualizing governance as an intervention within an adaptive system. By developing a multi-agent best-response model and a full-platform simulator, we validated a dynamic evaluation framework across 72 case studies. Experimental results demonstrate that the simulator achieves an adaptation quality score of 0.836 and attains a 100% win rate against all baselines, significantly outperforming traditional static methods. This research overcomes the limitations of static assessment paradigms, offering novel theoretical insights into complex governance interactions and providing a high-precision quantitative tool for evaluating dynamic platform ecosystems.

A lack of standardized, quantitative metrics for information environment manipulation targeting Web-scale knowledge graphs hinders systematic analysis and cross-study comparison. Method: This paper adapts the BEND framework to the Web graph setting, proposing a novel set of quantifiable, SEO-driven information manipulation indicators that capture both community-level structural anomalies and SEO-specific behavioral signals. The approach integrates graph topological analysis with SEO signal modeling and validates indicator validity via face validity assessment. Contribution/Results: Applied to two empirical case studies involving Kremlin-affiliated websites, the metrics successfully detect covert manipulative link patterns and coordinated content strategies, substantially enhancing interpretability and detectability of search-oriented information manipulation. This work bridges a critical gap in Web-scale information manipulation quantification and delivers a reusable analytical framework for platform governance and algorithmic auditing.

Current research on online behavioral change suffers from narrow behavioral coverage, overreliance on API-restricted platforms as data sources, and a persistent theory–empiricism gap. To address these limitations, this study conducts a systematic literature review of 148 peer-reviewed articles published between 2000 and 2023, constructing a four-dimensional knowledge graph encompassing behavioral categories, detection methodologies, platform ecosystems, and theoretical foundations. Our analysis uncovers three salient trends: (1) affective orientation dominates behavioral modeling; (2) platform distribution is heavily skewed toward a few API-constrained platforms; and (3) theoretical integration remains markedly underdeveloped. We propose a novel methodology framework—“Multi-behavioral Modeling, Heterogeneous Data Integration, and Theory–Practice Alignment”—and deliver a structured research map that precisely identifies critical gaps. This work advances the computational behavioral paradigm and offers an actionable methodological guide for digital social governance.

This study identifies the “echo-platform” phenomenon in social media ecosystems—characterized by escalating platform-level ideological homophily and deepening cross-platform user segmentation, leading to structural fragmentation of the digital public sphere. We propose the first operationalizable three-dimensional framework—comprising platform centrality, news credibility, and user diversity—and apply it to 126 million URLs and cross-platform behavioral data from nearly six million users across nine platforms. Methodologically, we integrate network centrality analysis, automated credibility classification, and heterogeneity measurement. Empirical results reveal a systematic bifurcation: “mainstream platforms” exhibit higher centrality and credibility with ideologically diverse users, whereas “alternative tech platforms” are peripheral, dominated by low-credibility content, and host highly homogeneous user populations. Our work establishes a reproducible quantitative benchmark and a novel theoretical paradigm for studying polarization in digital public spheres.

为理解社交媒体使用如何影响福祉，提出CAST框架，通过多维度测量和建模个体在不同时间尺度上的行为、生理及体验。

本文提出一个多标准框架来评估社会技术干预措施，通过调查研究者对40种干预措施在五个评价标准上的看法，帮助设计者和政策制定者更全面地比较不同干预方案。

研究通过分析超过50,000个Bluesky平台上的启动包及其关联用户，探讨了这些精选账户集合如何帮助新用户快速建立社交网络及对内容传播的影响。

This study addresses the security and efficacy challenges faced by activist communities under surveillance and censorship mechanisms prevalent in mainstream social media. While decentralized social networks (DSNs) offer a promising alternative, they lack a systematic evaluation framework aligned with activists’ organizational needs. To bridge this gap, this work proposes the first conceptual framework that links core activist requirements—such as minimal overhead, community building, online and offline safety, and sustainable operations—with DSN technical characteristics, including resource efficiency, interoperability, and data ownership. Grounded in sociotechnical systems theory, the framework enables a structured comparison of Mastodon and Bluesky, revealing how distinct DSN infrastructures differentially enable or constrain collective action. The analysis yields actionable platform selection strategies for activist groups operating under political and technological constraints.

This study addresses the unintended long-term consequences of user interventions—such as “sleep reminders”—on recommender systems that dynamically adapt to user feedback. Through a large-scale field experiment on a short-video platform, combined with causal inference, log analysis, and dynamic policy modeling, the authors demonstrate that such interventions can inadvertently “retrain” the recommendation algorithm, inducing systemic shifts in content delivery. Contrary to expectations, the sleep reminder not only failed to reduce usage but increased late-night viewing duration by 14.75% and overall usage by 2.18%, with effects persisting for several weeks. These findings challenge the conventional paradigm of evaluating interventions under static assumptions and underscore the necessity of accounting for algorithmic adaptation in digital well-being policies.