AI-generated text has proliferated across various online platforms, offering both transformative prospects and posing significant risks related to misinformation and manipulation. Addressing these challenges, this paper introduces SAID (Social media AI Detection), a novel benchmark developed to assess AI-text detection models' capabilities in real social media platforms. It incorporates real AI-generate text from popular social media platforms like Zhihu and Quora. Unlike existing benchmarks, SAID deals with content that reflects the sophisticated strategies employed by real AI users on the Internet which may evade detection or gain visibility, providing a more realistic and challenging evaluation landscape. A notable finding of our study, based on the Zhihu dataset, reveals that annotators can distinguish between AI-generated and human-generated texts with an average accuracy rate of 96.5%. This finding necessitates a re-evaluation of human capability in recognizing AI-generated text in today's widely AI-influenced environment. Furthermore, we present a new user-oriented AI-text detection challenge focusing on the practicality and effectiveness of identifying AI-generated text based on user information and multiple responses. The experimental results demonstrate that conducting detection tasks on actual social media platforms proves to be more challenging compared to traditional simulated AI-text detection, resulting in a decreased accuracy. On the other hand, user-oriented AI-generated text detection significantly improve the accuracy of detection.

翻译：AI生成文本已在各大网络平台激增，既带来变革性前景，也引发了与虚假信息和操纵相关的重大风险。为应对这些挑战，本文提出了SAID（社交媒体AI检测）这一新型基准测试，旨在评估AI文本检测模型在真实社交媒体平台上的表现。该基准测试整合了知乎、Quora等流行社交平台上的真实AI生成文本。与现有基准不同，SAID处理的内容反映了真实互联网AI用户采用的复杂策略——这些文本可能旨在规避检测或获取曝光，从而提供了更真实且更具挑战性的评估场景。基于知乎数据集的一项显著发现表明，标注者区分AI生成文本与人类生成文本的平均准确率可达96.5%。这一发现促使我们重新评估当前广泛受AI影响环境下人类识别AI生成文本的能力。此外，我们提出了一项面向用户的新兴AI文本检测挑战，聚焦于基于用户信息及多轮回复识别AI生成文本的实用性与有效性。实验结果表明，在实际社交媒体平台上执行检测任务比传统模拟AI文本检测更具挑战性，导致准确率下降。另一方面，面向用户的AI生成文本检测能显著提升检测准确率。