Open Theses

Unverified or false information is quickly propagated and amplified via social networks. If untrue, this information can lead to bad decisions and harmful consequences in the real world. To support human fact-checkers in the time-consuming verification process, numerous automated NLP fact-checking systems and datasets have been proposed. These systems typically compare textual claims with highly-credible evidence documents to infer the claim’s veracity. Recent works found problems with this setup when creating datasets with more realistic claims, because claims may not be specific enough, or because the available evidence may be inconclusive, to be verified.

The goal of this thesis is to better understand the challenges when applying automated fact-checking on realistic claims, and to identify ways to close this gap.