Recent LLMs are able to generate high-quality multilingual texts,
indistinguishable for humans from authentic human-written ones. Research in
machine-generated text detection is however mostly focused on the English
language and longer texts, such as news articles, scientific papers or student
essays. Social-media texts are usually much shorter and often feature informal
language, grammatical errors, or distinct linguistic items (e.g., emoticons,
hashtags). There is a gap in studying the ability of existing methods in
detection of such texts, reflected also in the lack of existing multilingual
benchmark datasets. To fill this gap we propose the first multilingual (22
languages) and multi-platform (5 social media platforms) dataset for
benchmarking machine-generated text detection in the social-media domain,
called MultiSocial. It contains 472,097 texts, of which about 58k are
human-written and approximately the same amount is generated by each of 7
multilingual LLMs. We use this benchmark to compare existing detection methods
in zero-shot as well as fine-tuned form. Our results indicate that the
fine-tuned detectors have no problem to be trained on social-media texts and
that the platform selection for training matters.

该研究提出了第一个多语种（22 种语言）和多平台（5 个社交媒体平台）数据集 ——MultiSocial，用于在社交媒体领域评估机器生成文本检测的性能，结果显示 fine-tuned 的检测器在社交媒体文本上训练没有问题，并且训练过程中选择的平台对结果有影响。