Dataset Information

Semantic network analysis of vaccine sentiment in online social media.

ABSTRACT: To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.We constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.The semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.Semantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.

SUBMITTER: Kang GJ

PROVIDER: S-EPMC5548132 | biostudies-literature | 2017 Jun

REPOSITORIES: biostudies-literature

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Semantic network analysis of vaccine sentiment in online social media.

Kang Gloria J GJ Ewing-Nelson Sinclair R SR Mackey Lauren L Schlitt James T JT Marathe Achla A Abbas Kaja M KM Swarup Samarth S

Vaccine 20170527 29

<h4>Objective</h4>To examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.<h4>Background</h4>Vaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.<h4>Methods</h4>We constructed semantic net ...[more]

PMID: 28554500

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Project description:BACKGROUND:Although vaccination rates are above the threshold for herd immunity in South Korea, a growing number of parents have expressed concerns about the safety of vaccines. It is important to understand these concerns so that we can maintain high vaccination rates. OBJECTIVE:The aim of this study was to develop a childhood vaccination ontology to serve as a framework for collecting and analyzing social data on childhood vaccination and to use this ontology for identifying concerns about and sentiments toward childhood vaccination from social data. METHODS:The domain and scope of the ontology were determined by developing competency questions. We checked if existing ontologies and conceptual frameworks related to vaccination can be reused for the childhood vaccination ontology. Terms were collected from clinical practice guidelines, research papers, and posts on social media platforms. Class concepts were extracted from these terms. A class hierarchy was developed using a top-down approach. The ontology was evaluated in terms of description logics, face and content validity, and coverage. In total, 40,359 Korean posts on childhood vaccination were collected from 27 social media channels between January and December 2015. Vaccination issues were identified and classified using the second-level class concepts of the ontology. The sentiments were classified in 3 ways: positive, negative or neutral. Posts were analyzed using frequency, trend, logistic regression, and association rules. RESULTS:Our childhood vaccination ontology comprised 9 superclasses with 137 subclasses and 431 synonyms for class, attribute, and value concepts. Parent's health belief appeared in 53.21% (15,709/29,521) of posts and positive sentiments appeared in 64.08% (17,454/27,236) of posts. Trends in sentiments toward vaccination were affected by news about vaccinations. Posts with parents' health belief, vaccination availability, and vaccination policy were associated with positive sentiments, whereas posts with experience of vaccine adverse events were associated with negative sentiments. CONCLUSIONS:The childhood vaccination ontology developed in this study was useful for collecting and analyzing social data on childhood vaccination. We expect that practitioners and researchers in the field of childhood vaccination could use our ontology to identify concerns about and sentiments toward childhood vaccination from social data.

Project description:BACKGROUND:There are more than 6000 rare diseases in existence today, with the number of patients with these conditions rapidly increasing. Most research to date has focused on the diagnosis, treatment, and development of orphan drugs, while few studies have examined the topics and emotions expressed by patients living with rare diseases on social media platforms, especially in online health communities (OHCs). OBJECTIVE:This study aimed to determine the topic categorizations and sentiment polarity for albinism in a Chinese OHC, Baidu Tieba, using multiple methods. The OHC was deeply mined using topic mining, social network analysis, and sentiment polarity analysis. Through these methods, we determined the current situation of community construction, identifying the ongoing needs and problems experienced by people with albinism in their daily lives. METHODS:We used the albinism community on the Baidu Tieba platform as the data source in this study. Term frequency-inverse document frequency, latent dirichlet allocation models, and naive Bayes were employed to mine the various topic categories. Social network analysis, which was completed using the Gephi tool, was employed to analyze the evolution of the albinism community. Sentiment polarity analysis was performed using a long short-term memory algorithm. RESULTS:We identified 8 main topics discussed in the community: daily sharing, family, interpersonal communication, social life and security, medical care, occupation and education, beauty, and self-care. Among these topics, daily sharing represented the largest proportion of the discussions. From 2012 to 2019, the average degree and clustering coefficient of the albinism community continued to decline, while the network center transferred from core communities to core users. A total of 68.43% of the corpus was emotional, with 35.88% being positive and 32.55% negative. There were statistically significant differences in the distribution of sentiment polarity between topics (P<.001). Negative emotions were twice as high as positive emotions in the social life and security topic. CONCLUSIONS:The study reveals insights into the emotions expressed by people with albinism in the Chinese OHC, Baidu Tieba, providing health care practitioners with greater appreciation of the current emotional support needed by patients and the patient experience. Current OHCs do not exert enough influence due to limited effective organization and development. Health care sectors should take greater advantage of OHCs to support vulnerable patients with rare diseases to meet their evidence-based needs.

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Semantic network analysis of vaccine sentiment in online social media.

Publications

Semantic network analysis of vaccine sentiment in online social media.

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