  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.
