Large-scale analysis of fact-checked stories on Twitter reveals graded effects of ambiguity and falsehood on information reappearance.

Misinformation disrupts our information ecosystem, adversely affecting individuals and straining social cohesion and democracy. Understanding what causes online (mis)information to (re)appear is crucial for fortifying our information ecosystem. We analyzed a large-scale Twitter (now "X") dataset of about 2 million tweets across 123 fact-checked stories. Previous research suggested a falsehood effect (false information reappears more frequently) and an ambiguity effect (ambiguous information reappears more frequently). However, robust indicators for their existence remain elusive. Using polynomial statistical modeling, we compared a falsehood model, an ambiguity model, and a dual effect model. The data supported the dual effect model ( $13.76$ times as likely as a null model), indicating both ambiguity and falsehood promote information reappearance. However, evidence for ambiguity was stronger: the ambiguity model was $6.6$ times as likely as the falsehood model. Various control checks affirmed the ambiguity effect, while the falsehood effect was less stable. Nonetheless, the best-fitting model explained $<7\%$ of the variance, indicating that (i) the dynamics of online (mis)information are complex and (ii) falsehood effects may play a smaller role than previous research has suggested. These findings underscore the importance of understanding the dynamics of online (mis)information, though our focus on fact-checked stories may limit the generalizability to the full spectrum of information shared online. Even so, our results can inform policymakers, journalists, social media platforms, and the public in building a more resilient information environment, while also opening new avenues for research, including source credibility, cross-platform applicability, and psychological factors.