Public Opinions Toward Diseases: Infodemiological Study on News Media Data

Huang, Ming; ElTayeby, Omar; Zolnoori, Maryam; Yao, Lixia

doi:10.2196/10047

Cited by 26 publications

(14 citation statements)

References 33 publications

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“…Collecting News Articles From Reuters News Agency Reuters News Agency is a leading global information media agency and the world's largest international text and television news provider [15]. We developed a web crawler in Python [10] to download news articles from an online archive of Reuters news agency [16].…”

Section: Collecting Cleaning and Filtering News Articlesmentioning

confidence: 99%

“…Sentiment analysis denotes systematic evaluation of opinions and their intensities of the words and sentences in a large collection of text documents by using computerized algorithms [9]. Sentiment analysis is widely used in the area of healthcare [9,15,25,26], particularly for identifying the attitudes and opinions of patients' posts in social media toward a specific healthcare issue. For example, Hopper and Uriyo used sentiment analysis to review patients' feedback for a selected group of gynecologists in Virginia [27].…”

Section: Sentiment Analysismentioning

confidence: 99%

“…In another study, Clark et al applied sentiment analysis to quantify sentiments of patients toward breast cancer treatment experience [28]. In our previous work, we also used sentiment analysis to identify the sentiments of news articles toward hundreds of diseases and medical conditions [15]. Computerized algorithms were developed to automatize the process of sentiment analysis, enabling researchers to evaluate sentiments of a large volume of text documents.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…In this work, we used a python module, Valence Aware Dictionary and sEntiment Reasoner (VADER) [29], to quantify the sentiments of the articles toward the 10 identified public health issues. VADER was specifically tuned to identify the sentiments of a wide range of social media data [15,30]. VADER reports a normalized and weighted sentiment score for a given sentence, according to predefined score of each word and embedded rules.…”

Section: Sentiment Analysismentioning

confidence: 99%

“…Due to the challenges (e.g., intensive human labor) of topic analysis with traditional manual qualitative methods, topic modeling methods were developed to automatically identify hidden topics in a massive collection of text documents [15,31,32]. The most frequently used topic modeling method is Latent Dirichlet Allocation (LDA) that was introduced by Blei et al in 2003 [33].…”

Section: Topic Modelingmentioning

confidence: 99%

See 4 more Smart Citations

Mining news media for understanding public health concerns

Zolnoori

Huang

Patten

et al. 2019

J. Clin. Trans. Sci.

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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.

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Section: Collecting Cleaning and Filtering News Articlesmentioning

confidence: 99%