Who Said That? Benchmarking Social Media AI Detection
Wanyun Cui Linqiu Zhang Qianle Wang Shuyang Cai
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.