The proliferation of online misinformation significantly undermines the credibility of web content. Recently, crowd workers have been successfully employed to assess misinformation to address the limited scalability of professional fact-checkers. An alternative approach to crowdsourcing is the use of large language models (LLMs). These models are however also not perfect. In this paper, we investigate the scenario of crowd workers working in collaboration with LLMs to assess misinformation. We perform a study where we ask crowd workers to judge the truthfulness of statements under different conditions: with and without LLMs labels and explanations. Our results show that crowd workers tend to overestimate truthfulness when exposed to LLM-generated information. Crowd workers are misled by wrong LLM labels, but, on the other hand, their self-reported confidence is lower when they make mistakes due to relying on the LLM. We also observe diverse behaviors among crowd workers when the LLM is presented, indicating that leveraging LLMs can be considered a distinct working strategy.

Misinformation has been rapidly spreading online. The common approach to dealing with it is deploying expert fact-checkers who follow forensic processes to identify the veracity of statements. Unfortunately, such an approach does not scale well. To deal with this, crowdsourcing has been looked at as an opportunity to complement the work done by trained journalists. In this article, we look at the effect of presenting the crowd with evidence from others while judging the veracity of statements. We implement variants of the judgment task design to understand whether and how the presented evidence may or may not affect the way crowd workers judge truthfulness and their performance. Our results show that, in certain cases, the presented evidence and the way in which it is presented may mislead crowd workers who would otherwise be more accurate if judging independently from others. Those who make appropriate use of the provided evidence, however, can benefit from it and generate better judgments.