SemEval-2017 Task 8: RumourEval: Determining rumour veracity and support for rumours
This addresses the issue of false claims in media for researchers and practitioners in natural language processing, though it is incremental as it builds on existing shared task frameworks.
The paper tackled the problem of identifying rumour veracity and discourse in text by introducing RumourEval, a SemEval shared task, which resulted in a large annotated dataset and two concrete challenges with participant results.
Media is full of false claims. Even Oxford Dictionaries named "post-truth" as the word of 2016. This makes it more important than ever to build systems that can identify the veracity of a story, and the kind of discourse there is around it. RumourEval is a SemEval shared task that aims to identify and handle rumours and reactions to them, in text. We present an annotation scheme, a large dataset covering multiple topics - each having their own families of claims and replies - and use these to pose two concrete challenges as well as the results achieved by participants on these challenges.