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.

社交媒体 AI 检测（SAID）是一个用于评估实际社交媒体平台上 AI 文本检测模型能力的新基准，该研究表明标注者可以通过 96.5% 的平均准确率区分 AI 生成文本和人工生成文本，为今天广泛受 AI 影响的环境下重新评估人类识别 AI 生成文本的能力提供了必要性。此外，作者提出了一个基于用户信息和多个回答进行 AI 生成文本检测的新挑战，实验证明在实际社交媒体平台上进行检测任务相较于传统模拟的 AI 文本检测更具挑战性，但用户导向的 AI 生成文本检测可显著提高检测准确率。

社交媒体 AI 检测基准评估

Who Said That? Benchmarking Social Media AI Detection

In the Internet, ubiquitous presence of redundant, unedited, raw videos has
made video summarization an important problem. Traditional methods of video
summarization employ a heuristic set of hand-crafted features, which in many
cases fail to capture subtle abstraction of a scene. This paper presents a deep
learning method that maps the context of a video to the importance of a scene
similar to that is perceived by humans. In particular, a convolutional neural
network (CNN)-based architecture is proposed to mimic the frame-level shot
importance for user-oriented video summarization. The weights and biases of the
CNN are trained extensively through off-line processing, so that it can provide
the importance of a frame of an unseen video almost instantaneously.
Experiments on estimating the shot importance is carried out using the publicly
available database TVSum50. It is shown that the performance of the proposed
network is substantially better than that of commonly referred feature-based
methods for estimating the shot importance in terms of mean absolute error,
absolute error variance, and relative F-measure.

本文提出了一种基于卷积神经网络的用户定向视频摘要生成方法，与传统的基于手工特征的方法相比，通过离线训练得到的权重和偏差使得该系统可以几乎实时地提取视频帧的重要性，实验结果表明该方法在估计视频镜头重要性方面表现优异。