  Check-worthy claim detection aims at providing plausible misinformation to
downstream fact-checking systems or human experts to check. This is a crucial
step toward accelerating the fact-checking process. Many efforts have been put
into how to identify check-worthy claims from a small scale of pre-collected
claims, but how to efficiently detect check-worthy claims directly from a
large-scale information source, such as Twitter, remains underexplored. To fill
this gap, we introduce MythQA, a new multi-answer open-domain question
answering(QA) task that involves contradictory stance mining for query-based
large-scale check-worthy claim detection. The idea behind this is that
contradictory claims are a strong indicator of misinformation that merits
scrutiny by the appropriate authorities. To study this task, we construct
TweetMythQA, an evaluation dataset containing 522 factoid multi-answer
questions based on controversial topics. Each question is annotated with
multiple answers. Moreover, we collect relevant tweets for each distinct
answer, then classify them into three categories: "Supporting", "Refuting", and
"Neutral". In total, we annotated 5.3K tweets. Contradictory evidence is
collected for all answers in the dataset. Finally, we present a baseline system
for MythQA and evaluate existing NLP models for each system component using the
TweetMythQA dataset. We provide initial benchmarks and identify key challenges
for future models to improve upon. Code and data are available at:
https://github.com/TonyBY/Myth-QA
