Presents an open-source podcast annotation tool and a small annotated dataset (7 episodes, 1,960 utterances, 300 check-worthy claims) for claim detection and stance classification in English, Norwegian, and German.
LiveFC: A System for Live Fact-Checking of Audio Streams
1 Pith paper cite this work. Polarity classification is still indexing.
abstract
The advances in the digital era have led to rapid dissemination of information. This has also aggravated the spread of misinformation and disinformation. This has potentially serious consequences, such as civil unrest. While fact-checking aims to combat this, manual fact-checking is cumbersome and not scalable. While automated fact-checking approaches exist, they do not operate in real-time and do not always account for spread of misinformation through different modalities. This is particularly important as proactive fact-checking on live streams in real-time can help people be informed of false narratives and prevent catastrophic consequences that may cause civil unrest. This is particularly relevant with the rapid dissemination of information through video on social media platforms or other streams like political rallies and debates. Hence, in this work we develop a platform named LiveFC, that can aid in fact-checking live audio streams in real-time. LiveFC has a user-friendly interface that displays the claims detected along with their veracity and evidence for live streams with associated speakers for claims from respective segments. The app can be accessed at http://livefc.factiverse.ai and a screen recording of the demo can be found at https://bit.ly/3WVAoIw.
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Annotation Tool and Dataset for Fact-Checking Podcasts
Presents an open-source podcast annotation tool and a small annotated dataset (7 episodes, 1,960 utterances, 300 check-worthy claims) for claim detection and stance classification in English, Norwegian, and German.