Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings
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 find...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Sciendo
2023-08-01
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| Series: | Journal of Data and Information Science |
| Subjects: | |
| Online Access: | https://doi.org/10.2478/jdis-2023-0014 |
| _version_ | 1797736151057956864 |
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| author | An Ruopeng Batcheller Quinlan Wang Junjie Yang Yuyi |
| author_facet | An Ruopeng Batcheller Quinlan Wang Junjie Yang Yuyi |
| author_sort | An Ruopeng |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T13:09:27Z |
| format | Article |
| id | doaj.art-925b1a28bc45450292fb1466d55d1cc7 |
| institution | Directory Open Access Journal |
| issn | 2543-683X |
| language | English |
| last_indexed | 2024-03-12T13:09:27Z |
| publishDate | 2023-08-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Journal of Data and Information Science |
| spelling | doaj.art-925b1a28bc45450292fb1466d55d1cc72023-08-28T06:17:24ZengSciendoJournal of Data and Information Science2543-683X2023-08-0183889710.2478/jdis-2023-0014Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findingsAn Ruopeng0Batcheller Quinlan1Wang Junjie2Yang Yuyi31Brown School, Washington University in St. Louis, One Brookings Drive, St. Louis, Missouri63130, United States1Brown School, Washington University in St. Louis, One Brookings Drive, St. Louis, Missouri63130, United States2Department of kinesiology and health promotion, Dalian University of Technology, No.2 Linggong Road, Dalian116024, China1Brown School, Washington University in St. Louis, One Brookings Drive, St. Louis, Missouri63130, United StatesMedia 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.https://doi.org/10.2478/jdis-2023-0014artificial intelligencedeep neural networksnewsheadlinesexaggerationobesity |
| spellingShingle | An Ruopeng Batcheller Quinlan Wang Junjie Yang Yuyi Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings Journal of Data and Information Science artificial intelligence deep neural networks news headlines exaggeration obesity |
| title | Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings |
| title_full | Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings |
| title_fullStr | Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings |
| title_full_unstemmed | Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings |
| title_short | Build neural network models to identify and correct news headlines exaggerating obesity-related scientific findings |
| title_sort | build neural network models to identify and correct news headlines exaggerating obesity related scientific findings |
| topic | artificial intelligence deep neural networks news headlines exaggeration obesity |
| url | https://doi.org/10.2478/jdis-2023-0014 |
| work_keys_str_mv | AT anruopeng buildneuralnetworkmodelstoidentifyandcorrectnewsheadlinesexaggeratingobesityrelatedscientificfindings AT batchellerquinlan buildneuralnetworkmodelstoidentifyandcorrectnewsheadlinesexaggeratingobesityrelatedscientificfindings AT wangjunjie buildneuralnetworkmodelstoidentifyandcorrectnewsheadlinesexaggeratingobesityrelatedscientificfindings AT yangyuyi buildneuralnetworkmodelstoidentifyandcorrectnewsheadlinesexaggeratingobesityrelatedscientificfindings |