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Misinformation is becoming increasingly prevalent on social media and in newsarticles. It has become so widespread that we require algorithmic assistanceutilising machine learning to detect such content. Training these machinelearning models require datasets of sufficient scale, diversity and quality.However, datasets in the field of automatic misinformation detection arepredominantly monolingual, include a limited amount of modalities and are notof sufficient scale and quality. Addressing this, we develop a data collectionand linking system (MuMiN-trawl), to build a public misinformation graphdataset (MuMiN), containing rich social media data (tweets, replies, users,images, articles, hashtags) spanning 21 million tweets belonging to 26 thousandTwitter threads, each of which have been semantically linked to 13 thousandfact-checked claims across dozens of topics, events and domains, in 41different languages, spanning more than a decade. The dataset is made availableas a heterogeneous graph via a Python package (mumin). We provide baselineresults for two node classification tasks related to the veracity of a claiminvolving social media, and demonstrate that these are challenging tasks, withthe highest macro-average F1-score being 62.55% and 61.45% for the two tasks,respectively. The MuMiN ecosystem is available athttps://mumin-dataset.github.io/, including the data, documentation, tutorialsand leaderboards.

MuMiN: A Large-Scale Multilingual Multimodal Fact-Checked Misinformation Social Network Dataset