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• W2981871007 abstract "News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public health issues. Traditional qualitative content analysis for news sentiments and focuses are time-consuming and may not efficiently convey sentiments nor the focuses of news media.We used descriptive statistics and state-of-art text mining to conduct sentiment analysis and topic modeling, to efficiently analyze over 3 million Reuters news articles during 2007-2017 for identifying their coverage, sentiments, and focuses for public health issues. Based on the top keywords from public health scientific journals, we identified 10 major public health issues (i.e., air pollution, alcohol drinking, asthma, depression, diet, exercise, obesity, pregnancy, sexual behavior, and smoking).The news coverage for seven public health issues, Smoking, Exercise, Alcohol drinking, Diet, Obesity, Depression, and Asthma decreased over time. The news coverage for Sexual behavior, Pregnancy, and Air pollution fluctuated during 2007-2017. The sentiments of the news articles for three of the public health issues, exercise, alcohol drinking, and diet were predominately positive and associated such as energy. Sentiments for the remaining seven public health issues were mainly negative, linked to negative terms, e.g., diseases. The results of topic modeling reflected the media's focus on public health issues.Text mining methods may address the limitations of traditional qualitative approaches. Using big data to understand public health needs is a novel approach that could help clinical and translational science awards programs focus on community-engaged research efforts to address community priorities." @default.
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• W2981871007 date "2019-10-23" @default.
• W2981871007 modified "2023-10-11" @default.
• W2981871007 title "Mining news media for understanding public health concerns" @default.
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• W2981871007 doi "https://doi.org/10.1017/cts.2019.434" @default.
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