Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings  

作　　者：Ruopeng An Quinlan Batcheller Junjie Wang Yuyi Yang 

机构地区：[1]Brown School,Washington University in St.Louis,One Brookings Drive,St.Louis,Missouri 63130,United States [2]Department of kinesiology and health promotion,Dalian University of Technology,No.2 Linggong Road,Dalian 116024,China

出　　处：《Journal of Data and Information Science》2023年第3期88-97,共10页数据与情报科学学报（英文版）

摘　　要：Purpose:Media exaggerations of health research may confuse readers’understanding,erode public trust in science and medicine,and cause disease mismanagement.This study built artificial intelligence(AI)models to automatically identify and correct news headlines exaggerating obesity-related research findings.Design/methodology/approach:We searched popular digital media outlets to collect 523 headlines exaggerating obesity-related research findings.The reasons for exaggerations include:inferring causality from observational studies,inferring human outcomes from animal research,inferring distant/end outcomes(e.g.,obesity)from immediate/intermediate outcomes(e.g.,calorie intake),and generalizing findings to the population from a subgroup or convenience sample.Each headline was paired with the title and abstract of the peer-reviewed journal publication covered by the news article.We drafted an exaggeration-free counterpart for each original headline and fined-tuned a BERT model to differentiate between them.We further fine-tuned three generative language models-BART,PEGASUS,and T5 to autogenerate exaggeration-free headlines based on a journal publication’s title and abstract.Model performance was evaluated using the ROUGE metrics by comparing model-generated headlines with journal publication titles.Findings:The fine-tuned BERT model achieved 92.5%accuracy in differentiating between exaggeration-free and original headlines.Baseline ROUGE scores averaged 0.311 for ROUGE-1,0.113 for ROUGE-2,0.253 for ROUGE-L,and 0.253 ROUGE-Lsum.PEGASUS,T5,and BART all outperformed the baseline.The best-performing BART model attained 0.447 for ROUGE-1,0.221 for ROUGE-2,0.402 for ROUGE-L,and 0.402 for ROUGE-Lsum.Originality/value:This study demonstrated the feasibility of leveraging AI to automatically identify and correct news headlines exaggerating obesity-related research findings.

关 键 词：Artificial intelligence Deep neural networks NEWS Headlines EXAGGERATION OBESITY 

分 类 号：G353.1[文化科学—情报学]