Mining news media for understanding public health concerns
© The Association for Clinical and Translational Science 2019..
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.
| Medienart: |
E-Artikel |
|---|
| Erscheinungsjahr: |
2019 |
|---|---|
| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:5 |
|---|---|
| Enthalten in: |
Journal of clinical and translational science - 5(2019), 1 vom: 23. Okt., Seite e1 |
| Sprache: |
Englisch |
|---|
| Beteiligte Personen: |
Zolnoori, Maryam [VerfasserIn] |
|---|
| Links: |
|---|
| Themen: |
News |
|---|
| Anmerkungen: |
Date Revised 16.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
|---|
| doi: |
10.1017/cts.2019.434 |
|---|
| funding: |
|
|---|---|
| Förderinstitution / Projekttitel: |
|
| PPN (Katalog-ID): |
NLM325011761 |
|---|
| LEADER | 01000caa a22002652 4500 | ||
|---|---|---|---|
| 001 | NLM325011761 | ||
| 003 | DE-627 | ||
| 005 | 20240216232136.0 | ||
| 007 | cr uuu---uuuuu | ||
| 008 | 231225s2019 xx |||||o 00| ||eng c | ||
| 024 | 7 | |a 10.1017/cts.2019.434 |2 doi | |
| 028 | 5 | 2 | |a pubmed24n1295.xml |
| 035 | |a (DE-627)NLM325011761 | ||
| 035 | |a (NLM)33948233 | ||
| 040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
| 041 | |a eng | ||
| 100 | 1 | |a Zolnoori, Maryam |e verfasserin |4 aut | |
| 245 | 1 | 0 | |a Mining news media for understanding public health concerns |
| 264 | 1 | |c 2019 | |
| 336 | |a Text |b txt |2 rdacontent | ||
| 337 | |a ƒaComputermedien |b c |2 rdamedia | ||
| 338 | |a ƒa Online-Ressource |b cr |2 rdacarrier | ||
| 500 | |a Date Revised 16.02.2024 | ||
| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a © The Association for Clinical and Translational Science 2019. | ||
| 520 | |a 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 | ||
| 520 | |a 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") | ||
| 520 | |a 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 | ||
| 520 | |a 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 | ||
| 650 | 4 | |a News | |
| 650 | 4 | |a News | |
| 650 | 4 | |a Public health issue | |
| 650 | 4 | |a Reuters | |
| 650 | 4 | |a Sentiment analysis | |
| 650 | 4 | |a Topic modeling | |
| 700 | 1 | |a Huang, Ming |e verfasserin |4 aut | |
| 700 | 1 | |a Patten, Christi A |e verfasserin |4 aut | |
| 700 | 1 | |a Balls-Berry, Joyce E |e verfasserin |4 aut | |
| 700 | 1 | |a Goudarzvand, Somaieh |e verfasserin |4 aut | |
| 700 | 1 | |a Brockman, Tabetha A |e verfasserin |4 aut | |
| 700 | 1 | |a Sagheb, Elham |e verfasserin |4 aut | |
| 700 | 1 | |a Yao, Lixia |e verfasserin |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of clinical and translational science |d 2017 |g 5(2019), 1 vom: 23. Okt., Seite e1 |w (DE-627)NLM271221658 |x 2059-8661 |7 nnns |
| 773 | 1 | 8 | |g volume:5 |g year:2019 |g number:1 |g day:23 |g month:10 |g pages:e1 |
| 856 | 4 | 0 | |u http://dx.doi.org/10.1017/cts.2019.434 |3 Volltext |
| 912 | |a GBV_USEFLAG_A | ||
| 912 | |a GBV_NLM | ||
| 951 | |a AR | ||
| 952 | |d 5 |j 2019 |e 1 |b 23 |c 10 |h e1 | ||