Abstract：This paper explores the impact of using advanced technology such as artificial intelligence (AI) to match marketers with social media influencers. We develop a theoretical model to examine how matching accuracy affects the competition between influencers and the profitability of a social media platform. Our findings show that improving matching accuracy may not always benefit the platform, especially for platforms with intermediate follower density. Two opposing effects of technology improvement affect the prices of influencer marketing campaigns: advanced technology, such as AI, enhances the matching between influencers and marketers and also intensifies competition between different types of influencers. The overall effect on prices can be negative for some influencers because of the asymmetric nature of such matching technology: the matching outcome for influencers with a narrower audience (niche influencers) is more sensitive to matching accuracy than that for those with a broader audience (general influencers). As a result, more niche influencers begin to participate in marketing campaigns when matching accuracy improves, which reduces the prices offered by sufficiently general influencers and may lead to a decline in platform revenue. Additionally, we find that adjusting commission rates in response to technology improvements could help mitigate the negative impact although it may not eliminate it entirely. Our findings offer valuable insights for social media platforms seeking to remain competitive in the influencer marketing landscape.

主要发现：AI 匹配提高准确性时，也会改变网红竞争结构；更精准不一定带来更高平台收入。

Abstract：Digital advertising's cultural surround is algorithmically tuned. The popular narrative about digital advertising is that it is hyper-targeted and customized. In this article, we argue that digital platforms' targeted advertising is more productively understood as tuned advertising, where algorithmic models optimize the resonance between ads and consumers in a continuous rhythmic flow of images, videos, and text. We develop the concept of tuned advertising by drawing on a study conducted with 204 young people from the Australian state of Victoria, where they sent us over 5,169 screenshots of alcohol, gambling, and unhealthy food ads they saw in their digital feeds. We identify five different "vibes" in the collections of ads they sent us that reflect different subjectivities and practices of consumption. We argue that digital platforms create a mode of advertising organized around tuning the vibe between ads, content, and users.

主要发现：社媒广告的核心不是一次性锁定目标人群，而是在连续内容流中调谐广告、内容与用户状态的匹配感。

Abstract：Despite the growing impact of algorithms on digital culture, and the importance of algorithmic awareness, little literacy research has investigated how algorithmic awareness and speculation shapes cultural production on digital platforms. Developing Bucher's concept of the "algorithmic imagination" for digital literacy research, we conduct a study of #BookTok, the home of book-related content on TikTok, the most algorithm-driven social media platform to date. Through a multimodal content analysis of 57 videos containing #algorithm and #BookTok, we propose and explore a typology of five categories of "algorithmic imaginings": critique, defense, explanation, how to work, and exploration of the algorithm. These imaginaries move beyond rational attempts to deconstruct the algorithm and critique its role in platform capitalism toward playful explorations of the human-algorithmic relationship. This constitutes for us another dimension of critical literacy, as producers anthropomorphize technology in a manner that addresses the symbiotic meaning-making of human and machine head-on.

主要发现：用户并非被动接受算法，而会通过批判、解释、试探和拟人化想象来学习如何与算法共处。

Abstract：The emergence of generative artificial intelligence (GenAI) represents a watershed moment in the evolution of digital platforms. The capabilities of this AI technology go beyond traditional AI systems, enabling the autonomous generation of novel outcomes with significant implications for platform value creation, architecture, governance, and stakeholder interactions. We develop an integrative conceptual framework that identifies four key mechanisms through which GenAI transforms digital platforms: intelligent automation, democratization, hyper-personalization, and collaborative innovation. Through intelligent automation, GenAI transforms boundary resources from passive interfaces into active, intelligent mediators of value creation. Democratization systematically lowers barriers to platform participation. Hyper-personalization enables dynamic, individual-level adaptation of platform content. Collaborative innovation transforms platform innovation by making GenAI an active participant in human-AI value co-creation. We use this framework to situate the papers in the special issue and develop a research agenda that explores the transformative impact of GenAI on platform stakeholder relationships.

主要发现：GenAI 会把平台从“连接用户和内容”的系统，推进为能主动生成、调适和参与共创的智能基础设施。

Abstract：Algorithms profoundly shape user experiences on digital platforms, raising concerns about their negative impacts and highlighting the importance of algorithm literacy. Research on individuals' understanding of algorithms and their effects is expanding rapidly but lacks a cohesive framework. We conducted a systematic integrative literature review across social sciences and humanities (n = 169), addressing algorithm literacy in terms of its key conceptualizations and the endogenous, exogenous, and personal factors that influence it. We argue that existing research can be framed in terms of experiential learning cycles and outline how this approach can be beneficial for acquiring algorithm literacy. Finally, we propose a future research agenda that includes defining core competencies relevant to algorithm literacy, standardization of measures, integrating subjective and factual aspects of algorithm literacy, and task- and domain-specific approaches.

主要发现：算法素养需要同时包含用户的主观理解和事实知识，并应结合具体平台任务与使用场景来研究。