Mining news media for understanding public health concerns

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...

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Published inJournal of clinical and translational science Vol. 5; no. 1; p. e1
Main Authors Zolnoori, Maryam, Huang, Ming, Patten, Christi A., Balls-Berry, Joyce E., Goudarzvand, Somaieh, Brockman, Tabetha A., Sagheb, Elham, Yao, Lixia
Format Journal Article
LanguageEnglish
Published England Cambridge University Press 01.01.2021
Subjects
Air pollution
Alcohol
Algorithms
Asthma
Content analysis
Data mining
Data Science in Clinical and Translational Research
Diet
Digital archives
Drinking behavior
Implementation, Policy and Community Engagement
Internet
Keywords
Media coverage
Medical Subject Headings-MeSH
News
News media
Obesity
Outdoor air quality
Pregnancy
Public health
Public health issue
Reuters
Sentiment analysis
Sexual behavior
Smoking
Statistical analysis
Topic modeling
Trends
Weight control
Topic modeling
Sentiment analysis
Reuters
Public health issue
News
Online AccessGet full text
ISSN2059-8661
2059-8661
DOI10.1017/cts.2019.434

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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.
AbstractList Introduction: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.Methods: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”).Results: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.Conclusions: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.
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.
Abstract Introduction: 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. Methods: 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”). Results: 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. Conclusions: 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.
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.INTRODUCTIONNews 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").METHODSWe 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.RESULTSThe 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.CONCLUSIONSText 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.
ArticleNumber e1
Author Goudarzvand, Somaieh
Patten, Christi A.
Zolnoori, Maryam
Huang, Ming
Balls-Berry, Joyce E.
Brockman, Tabetha A.
Sagheb, Elham
Yao, Lixia
AuthorAffiliation 1 Department of Health Sciences Research, Mayo Clinic , Rochester , MN , USA
5 School of Computing and Engineering, University of Missouri-Kansas , Kansas City , MO , USA
4 Mayo Clinic College of Medicine and Science , Rochester , MN , USA
3 Department of Psychiatry and Psychology, Mayo Clinic , Rochester , MN , USA
2 Center for Clinical and Translational Science, Community Engagement Program, Mayo Clinic , Rochester , MN , USA
AuthorAffiliation_xml – name: 1 Department of Health Sciences Research, Mayo Clinic , Rochester , MN , USA
– name: 3 Department of Psychiatry and Psychology, Mayo Clinic , Rochester , MN , USA
– name: 4 Mayo Clinic College of Medicine and Science , Rochester , MN , USA
– name: 2 Center for Clinical and Translational Science, Community Engagement Program, Mayo Clinic , Rochester , MN , USA
– name: 5 School of Computing and Engineering, University of Missouri-Kansas , Kansas City , MO , USA
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  givenname: Maryam
  surname: Zolnoori
  fullname: Zolnoori, Maryam
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  surname: Huang
  fullname: Huang, Ming
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  givenname: Christi A.
  surname: Patten
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  surname: Balls-Berry
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Issue 1
Keywords Topic modeling
Sentiment analysis
Reuters
Public health issue
News
Language English
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The Association for Clinical and Translational Science 2019.
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
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Snippet News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public health...
Introduction:News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment of public...
Abstract Introduction: News media play an important role in raising public awareness, framing public opinions, affecting policy formulation, and acknowledgment...
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SubjectTerms Air pollution
Alcohol
Algorithms
Asthma
Content analysis
Data mining
Data Science in Clinical and Translational Research
Diet
Digital archives
Drinking behavior
Implementation, Policy and Community Engagement
Internet
Keywords
Media coverage
Medical Subject Headings-MeSH
News
News media
Obesity
Outdoor air quality
Pregnancy
Public health
Public health issue
Reuters
Sentiment analysis
Sexual behavior
Smoking
Statistical analysis
Topic modeling
Trends
Weight control
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Title Mining news media for understanding public health concerns
URI https://www.ncbi.nlm.nih.gov/pubmed/33948233
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