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What's not in the news headlines or titles of Alzheimer disease articles? #InMice.

ABSTRACT: There is increasing scrutiny around how science is communicated to the public. For instance, a Twitter account @justsaysinmice (with 70.4K followers in January 2021) was created to call attention to news headlines that omit that mice, not humans, are the ones for whom the study findings apply. This is the case of many headlines reporting on Alzheimer disease (AD) research. AD is characterized by a degeneration of the human brain, loss of cognition, and behavioral changes, for which no treatment is available. Around 200 rodent models have been developed to study AD, even though AD is an exclusively human condition that does not occur naturally in other species and appears impervious to reproduction in artificial animal models, an information not always disclosed. It is not known what prompts writers of news stories to either omit or acknowledge, in the story's headlines, that the study was done in mice and not in humans. Here, we raised the hypothesis that how science is reported by scientists plays a role on the news reporting. To test this hypothesis, we investigated whether an association exists between articles' titles and news' headlines regarding the omission, or not, of mice. To this end, we analyzed a sample of 623 open-access scientific papers indexed in PubMed in 2018 and 2019 that used mice either as models or as the biological source for experimental studies in AD research. We found a significant association (p < 0.01) between articles' titles and news stories' headlines, revealing that when authors omit the species in the paper's title, writers of news stories tend to follow suit. We also found that papers not mentioning mice in their titles are more newsworthy and significantly more tweeted than papers that do. Our study shows that science reporting may affect media reporting and asks for changes in the way we report about findings obtained with animal models used to study human diseases.

SUBMITTER: Triunfol M

PROVIDER: S-EPMC8205157 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:BackgroundRapid identification of subject experts for medical topics helps in improving the implementation of discoveries by speeding the time to market drugs and aiding in clinical trial recruitment, etc. Identifying such people who influence opinion through social network analysis is gaining prominence. In this work, we explore how to combine named entity recognition from unstructured news articles with social network analysis to discover opinion leaders for a given medical topic.MethodsWe employed a Conditional Random Field algorithm to extract three categories of entities from health-related new articles: Person, Organization and Location. We used the latter two to disambiguate polysemy and synonymy for the person names, used simple rules to identify the subject experts, and then applied social network analysis techniques to discover the opinion leaders among them based on their media presence. A network was created by linking each pair of subject experts who are mentioned together in an article. The social network analysis metrics (including centrality metrics such as Betweenness, Closeness, Degree and Eigenvector) are used for ranking the subject experts based on their power in information flow.ResultsWe extracted 734,204 person mentions from 147,528 news articles related to obesity from January 1, 2007 through July 22, 2010. Of these, 147,879 mentions have been marked as subject experts. The F-score of extracting person names is 88.5%. More than 80% of the subject experts who rank among top 20 in at least one of the metrics could be considered as opinion leaders in obesity.ConclusionThe analysis of the network of subject experts with media presence revealed that an opinion leader might have fewer mentions in the news articles, but a high network centrality measure and vice-versa. Betweenness, Closeness and Degree centrality measures were shown to supplement frequency counts in the task of finding subject experts. Further, opinion leaders missed in scientific publication network analysis could be retrieved from news articles.

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What's not in the news headlines or titles of Alzheimer disease articles? #InMice.

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