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Abstract In the classical information theoretic framework, information “value” is proportional to how novel/surprising the information is. Recent work building on such notions claimed that false news spreads faster than truth online because false news is more novel and therefore surprising. However, another determinant of surprise, semantic meaning (e.g., information’s consistency or inconsistency with prior beliefs), should also influence value and sharing. Examining sharing behavior on Twitter, we observed separate relations of novelty and belief consistency with sharing. Though surprise could not be assessed in those studies, belief consistency should relate to less surprise, suggesting the relevance of semantic meaning beyond novelty. In two controlled experiments, belief-consistent (vs. belief-inconsistent) information was shared more despite consistent information being the least surprising. Manipulated novelty did not predict sharing or surprise. Thus, classical information theoretic predictions regarding perceived value and sharing would benefit from considering semantic meaning in contexts where people hold pre-existing beliefs. 

Computer scientists have responded to the high prevalence of inaccurate political information online by creating systems that identify and flag false claims. Warning users of inaccurate information as it is displayed has obvious appeal, but it also poses risk. Compared to post-exposure corrections, real-time corrections may cause users to be more resistant to factual information. This paper presents an experiment comparing the effects of real-time corrections to corrections that are presented after a short distractor task. Although real-time corrections are modestly more effective than delayed corrections overall, closer inspection reveals that this is only true among individuals predisposed to reject the false claim. In contrast, individuals whose attitudes are supported by the inaccurate information distrust the source more when corrections are presented in real time, yielding beliefs comparable to those never exposed to a correction. We find no evidence of real-time corrections encouraging counterargument. Strategies for reducing these biases are discussed.