When assessing the quality of health information encountered online, reasoners may rely on the wisdom of others and the degree of consensus apparent. However, it is unclear whether reasoners weigh the opinions of others evenly or make assumptions about the amount of evidence that each has seen. We investigated this question in an online experiment where people were asked to rate their belief in a series of health claims both before and after reading responses from other users. The degree of consensus among these users and their level of expertise (non-experts vs. expert organisations) was manipulated within-subjects. While we found belief change increased monotonically with the degree of consensus for both experts and non-experts, our results indicate qualitatively different patterns of increase between the two groups. Our study suggests that people reason from consensus using nuanced assumptions about the evidence underlying other people's opinions.

Many people turn to social media for public health information, but such platforms often contain conflicting and inaccurate medical advice. To assess complex health claims online, people may consider the prevailing consensus; however, previous work suggests that people may not be very sensitive to important cues to consensus “quality”. To explore further, across two experiments we tested people’s sensitivity to the consensus-quality cues of source diversity and source expertise. Via a mock Twitter platform, participants rated their belief in a series of health claims both before and after reading various kinds of tweets about the claims. Experiment 1 showed that experts (both individual medical experts and health organisations) were more persuasive than non-experts. Additionally, stances that were supported by a diverse set of sources were more persuasive. Experiment 2 showed that participants continue to favour experts even when outnumbered in tweet quantity by non-experts. When experts were not present, however, participants favoured high tweet quantity. Both experiments suggest that cues to consensus quality (namely, expertise and source diversity) and consensus quantity (tweet quantity) are salient cues in belief revision. These findings are important in understanding how socially acquired health information (and misinformation) shifts opinion, and the role that experts can play.