死亡互联网理论：关于人工智能交互与社交媒体未来的综述
The Dead Internet Theory: A Survey on Artificial Interactions and the Future of Social Media
AI总结：
本研究综述了死亡互联网理论（DIT）的起源与影响，指出AI和企业议程主导社交媒体导致真实互动衰退，提出需以人为中心的方法恢复真实的在线互动与社区建设。
摘要：
死亡互联网理论（DIT）认为，当今的互联网，尤其是社交媒体，在很大程度上被非人类活动、AI生成的内容和企业议程所主导，导致真实人类互动的衰退。本研究探讨了DIT的起源、核心主张及其影响，强调其在社交媒体平台背景下的相关性。该理论的出现是对网络空间同质化感知的回应，突出了机器人激增、算法生成内容以及参与度指标优先于真实用户互动等问题。AI技术在这一现象中发挥着核心作用，因为社交媒体平台越来越多地使用算法和机器学习来策划内容、驱动参与度并最大化广告收入。尽管这些工具增强了可扩展性和个性化，但它们也优先考虑病毒式传播和消费，而非真实交流，导致了信任的侵蚀、内容多样性的丧失以及非人性化的互联网体验。本研究在社交媒体背景下重新定义了DIT，提出为了收入而将内容消费商品化已取代了有意义的人际连接。通过关注参与度指标，平台营造了一种人工性和疏离感，凸显了以人为中心的方法来恢复真实在线互动和社区建设的必要性。
英文摘要：
The Dead Internet Theory (DIT) suggests that much of today's internet, particularly social media, is dominated by non-human activity, AI-generated content, and corporate agendas, leading to a decline in authentic human interaction. This study explores the origins, core claims, and implications of DIT, emphasizing its relevance in the context of social media platforms. The theory emerged as a response to the perceived homogenization of online spaces, highlighting issues like the proliferation of bots, algorithmically generated content, and the prioritization of engagement metrics over genuine user interaction. AI technologies play a central role in this phenomenon, as social media platforms increasingly use algorithms and machine learning to curate content, drive engagement, and maximize advertising revenue. While these tools enhance scalability and personalization, they also prioritize virality and consumption over authentic communication, contributing to the erosion of trust, the loss of content diversity, and a dehumanized internet experience. This study redefines DIT in the context of social media, proposing that the commodification of content consumption for revenue has taken precedence over meaningful human connectivity. By focusing on engagement metrics, platforms foster a sense of artificiality and disconnection, underscoring the need for human-centric approaches to revive authentic online interaction and community building.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
