2022/02/23 by Dan Saattrup Nielsen, Nielsen, Dan Saattrup, Ryan McConville +1 · 17 citations
Computer Science · Social Sciences · #Artificial intelligence #Baseline (sea) #Classifier (UML) #Computer science #Crowdsourcing #Data science #Documentation #Flagging #Graph #Hate Speech and Cyberbullying Detection #Information retrieval #Machine learning #Misinformation #Misinformation and Its Impacts #Natural language processing #Python (programming language) #Social media #Topic Modeling #World Wide Web #cs.CL #cs.CY #cs.IR #cs.LG #cs.SI
paper · pdf · doi:10.48550/arxiv.2202.11684
published in arXiv (Cornell University) (Cornell University) · 9+3 pages
openalex publication_date 2022/02/23 · arxiv created 2022/03/08 · arxiv updated 2022/03/09 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Misinformation is becoming increasingly prevalent on social media and in news articles. It has become so widespread that we require algorithmic assistance utilising machine learning to detect such content. Training these machine learning models require datasets of sufficient scale, diversity and quality. However, datasets in the field of automatic misinformation detection are predominantly monolingual, include a limited amount of modalities and are not of sufficient scale and quality. Addressing this, we develop a data collection and linking system (MuMiN-trawl), to build a public misinformation graph dataset (MuMiN), containing rich social media data (tweets, replies, users, images, articles, hashtags) spanning 21 million tweets belonging to 26 thousand Twitter threads, each of which have been semantically linked to 13 thousand fact-checked claims across dozens of topics, events and domains, in 41 different languages, spanning more than a decade. The dataset is made available as a heterogeneous graph via a Python package (mumin). We provide baseline results for two node classification tasks related to the veracity of a claim involving social media, and demonstrate that these are challenging tasks, with the highest macro-average F1-score being 62.55% and 61.45% for the two tasks, respectively. The MuMiN ecosystem is available at https://mumin-dataset.github.io/, including the data, documentation, tutorials and leaderboards.