Understanding the landscape of web-based medical misinformation about vaccination.

Given the high rates of vaccine hesitancy, web-based medical misinformation about vaccination is a serious issue. We sought to understand the nature of Google searches leading to medical misinformation about vaccination, and guided by fuzzy-trace theory, the characteristics of misinformation pages related to comprehension, inference-making, and medical decision-making. We collected data from web pages presenting vaccination information. We assessed whether web pages presented medical misinformation, had an overarching gist, used narrative, and employed emotional appeals. We used Search Engine Optimization tools to determine the number of backlinks from other web pages, monthly Google traffic, and Google Keywords. We used Coh-Metrix to measure readability and Gist Inference Scores (GIS). For medical misinformation web pages, Google traffic and backlinks were heavily skewed with means of 138.8 visitors/month and 805 backlinks per page. Medical misinformation pages were significantly more likely than other vaccine pages to have backlinks from other pages, and significantly less likely to receive at least one visitor from Google searches per month. The top Google searches leading to medical misinformation were "the truth about vaccinations," "dangers of vaccination," and "pro con vaccines." Most frequently, pages challenged vaccine safety, with 32.7% having an overarching gist, 7.7% presenting narratives, and 17.3% making emotional appeals. Emotional appeals were significantly more common with medical misinformation than other high-traffic vaccination pages. Misinformation pages had a mean readability grade level of 11.5, and a mean GIS of - 0.234. Low GIS scores are a likely barrier to understanding gist, and are the "Achilles' heel" of misinformation pages.

Gist Inference Scores Predict Cloze Comprehension "In Your Own Words" for Native, Not ESL Readers.

Three patient education texts from the National Cancer Institute (NCI) were subjected to a Coh-Metrix analysis, then further analyzed to obtain Gist Inference Scores (GIS), a new measure of the likelihood that readers will make appropriate inferences about a text's bottom-line meaning. In the GIS formula, the Coh-Metrix psycholinguistic variables referential cohesion, deep cohesion, and latent semantic analysis (LSA) verb overlap increase GIS because cohesive texts that describe related actions are likely to induce gist representations. The Coh-Metrix variables word concreteness, imagability for content words, and hypernymy for nouns and verbs are negatively weighted because they tend to promote verbatim mental representations. NCI texts were modified for a cloze procedure with every tenth word replaced by a blank starting with the second sentence. Participants in two studies received all three cloze-modified texts. Fuzzy-Trace Theory suggests that people are more likely to comprehend high GIS texts "in their own words," and thus fill-the-blanks with multiple words that differ from those omitted by the cloze procedure expressing comparable meaning. In Study One, non-native English speakers appropriately filled blanks with different words at the same rate for all three texts of low-, medium-, and high-GIS. In Study Two, replicating previous findings, for high GIS texts, native English speakers filled blanks appropriately with words other than those removed significantly more often than for medium- or low-GIS texts. High GIS texts apparently afford readers more semantic and lexical flexibility, but non-native English speakers may be ill-equipped to capitalize on this characteristic of high GIS texts.