ReINTEL: A Multimodal Data Challenge for Responsible Information Identification on Social Network Sites
This challenge provides a new benchmark dataset and competition for researchers working on responsible information identification on social media, particularly for Vietnamese content.
This paper introduces the ReINTEL Shared Task, a competition focused on classifying news as 'reliable' or 'unreliable' using multimodal data (text, visual, metadata) from social networks. It involved over 60 participants and nearly 1,000 submissions, evaluated using AUC-ROC scores.
This paper reports on the ReINTEL Shared Task for Responsible Information Identification on social network sites, which is hosted at the seventh annual workshop on Vietnamese Language and Speech Processing (VLSP 2020). Given a piece of news with respective textual, visual content and metadata, participants are required to classify whether the news is `reliable' or `unreliable'. In order to generate a fair benchmark, we introduce a novel human-annotated dataset of over 10,000 news collected from a social network in Vietnam. All models will be evaluated in terms of AUC-ROC score, a typical evaluation metric for classification. The competition was run on the Codalab platform. Within two months, the challenge has attracted over 60 participants and recorded nearly 1,000 submission entries.