用AI生成假论文操控引用，暴露学术平台监管漏洞
From Content Creation to Citation Inflation: A GenAI Case Study
- 用生成式AI伪造论文并嵌入可疑引用链
- 假论文可绕过审核，持续提升作者的H指数和i10指数
- 揭示平台内容审核缺陷，适合关注学术诚信的研究者
本文研究了生成式AI在预印本平台中生成可疑学术论文的现象及其对引用体系的影响。基于对生成式AI增强网络安全研究中异常出版模式的观察，我们识别出多个可疑论文集群与作者画像。这些论文普遍技术内容贫乏、结构重复、作者身份无法验证，且存在作者间相互引用的闭环现象。为评估此类行为的可行性，我们开展受控实验：使用生成式AI撰写一篇假论文，嵌入指向可疑文献的引用，并上传至ResearchGate平台。结果表明，此类论文可绕过平台审核机制，长期公开可见，并有效抬高作者的H指数与i10指数。本文深入分析了其运作机制，指出现有平台内容审核的系统性弱点，提出加强平台问责与维护学术诚信的改进建议。
原文摘要 · Abstract (English)
This paper investigates the presence and impact of questionable, AI-generated academic papers on widely used preprint repositories, with a focus on their role in citation manipulation. Motivated by suspicious patterns observed in publications related to our ongoing research on GenAI-enhanced cybersecurity, we identify clusters of questionable papers and profiles. These papers frequently exhibit minimal technical content, repetitive structure, unverifiable authorship, and mutually reinforcing citation patterns among a recurring set of authors. To assess the feasibility and implications of such practices, we conduct a controlled experiment: generating a fake paper using GenAI, embedding citations to suspected questionable publications, and uploading it to one such repository (ResearchGate). Our findings demonstrate that such papers can bypass platform checks, remain publicly accessible, and contribute to inflating citation metrics like the H-index and i10-index. We present a detailed analysis of the mechanisms involved, highlight systemic weaknesses in content moderation, and offer recommendations for improving platform accountability and preserving academic integrity in the age of GenAI.
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