AI-generated evidence summaries yield crowd truthfulness judgments with accuracy comparable to full webpages while cutting time by roughly 13-15 percent.
A simple but tough-to-beat baseline for the Fake News Challenge stance detection task
1 Pith paper cite this work, alongside 208 external citations. Polarity classification is still indexing.
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abstract
Identifying public misinformation is a complicated and challenging task. An important part of checking the veracity of a specific claim is to evaluate the stance different news sources take towards the assertion. Automatic stance evaluation, i.e. stance detection, would arguably facilitate the process of fact checking. In this paper, we present our stance detection system which claimed third place in Stage 1 of the Fake News Challenge. Despite our straightforward approach, our system performs at a competitive level with the complex ensembles of the top two winning teams. We therefore propose our system as the 'simple but tough-to-beat baseline' for the Fake News Challenge stance detection task.
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Efficiency and Effectiveness of LLM-Based Summarization of Evidence in Crowdsourced Fact-Checking
AI-generated evidence summaries yield crowd truthfulness judgments with accuracy comparable to full webpages while cutting time by roughly 13-15 percent.