A Text-Mining Analysis to Examine Dominant Sources of Online Information and Content on Continuous Glucose Monitors.

Purpose: The purpose of this study is to use text-mining methods to examine the dominant sources of online information and content about continuous glucose monitors (CGMs). Because the internet is the most popular source for health information, it is important to understand what is being said about CGMs in online sources of information.

Methods: A text miner, algorithmic-driven statistical program was used to identify the main sources of online information and topics on CGMs.

COVID-19 topics and emotional frames in vaccine hesitation: A social media text and sentiment analysis.

Objective: Addressing gaps in COVID-19 vaccine-hesitancy research, the current study aimed to add depth and nuance to the exploratory research examining vaccine-hesitant groups. Using a larger, but more focused conversation occurring on social media, the results can be used by health communicators to frame emotionally resonant messaging to improve COVID-19 vaccine advocacy while also mitigating negative concerns for vaccine-hesitant individuals.

Methods: Social media mentions were collected using a social media listening software, Brandwatch, to examine topics and sentiments in COVID-19 hesitancy discourse during a period of September 1, 2020, through December 31, 2020.

Telehealth adoption during the COVID-19 pandemic: A social media textual and network analysis.

Objective: The telemedicine industry has rapidly grown during the COVID-19 pandemic, and telemedicine has become a common form of care. The present study looks at the online conversation regarding telemedicine at the beginning of the pandemic and one year later. The Technology Acceptance Model is utilized to explain the findings.

Social media conversations about community water fluoridation: formative research to guide health communication.

Objectives: Community water fluoridation (CWF) is one of the greatest public health achievements of the 20th century. Despite this achievement, there are still misunderstandings about the safety of water fluoridation. Previous communication campaigns advocating CWF have been unsuccessful in combating these misunderstandings, suggesting a need for a new way to promote CWF.

Identifying #addiction concerns on twitter during the COVID-19 pandemic: A text mining analysis.

The 2019 Novel Coronavirus (COVID-19) is responsible for thousands of deaths and hospitalizations. To curb the spread of this highly transmissible disease, governments enacted protective guidelines for its citizens, including social distancing and stay-at-home orders. These restrictions on social interactions can be especially problematic for individuals managing or recovering from addiction given that treatment often involves access to services and resources that became limited or even unavailable at this time.

Sudden Infant Death Syndrome and Safe Sleep on Twitter: Analysis of Influences and Themes to Guide Health Promotion Efforts.

Background: In the United States, sudden infant death syndrome (SIDS) is the leading cause of death in infants aged 1 month to 1 year. Approximately 3500 infants die from SIDS and sleep-related reasons on a yearly basis. Unintentional sleep-related deaths and bed sharing, a known risk factor for SIDS, are on the rise.

Reactions to foodborne Escherichia coli outbreaks: A text-mining analysis of the public's response.

Foodborne illnesses caused by bacteria are being reported at an increasing rate in the United States. We performed a text-mining analysis to look at nearly 13,000 tweets from two foodborne Escherichia coli outbreaks in 2018. Concerns from the public included staying informed about contaminated lettuce, recognizing signs of infection, and holding responsible farms accountable.

A text-mining analysis of the public's reactions to the opioid crisis.

Background: Opioid abuse has become an epidemic in the United States. On August 25, 2016, the former Surgeon General of the United States sent an open letter to care providers asking for their help with combatting this growing health crisis. Social media forums like Twitter allow for open discussions among the public and up-to-date exchanges of information about timely topics like opioids.

Public reactions to e-cigarette regulations on Twitter: a text mining analysis.

Background: In May 2016, the Food and Drug Administration (FDA) issued a final rule that deemed e-cigarettes to be within their regulatory authority as a tobacco product. News and opinions about the regulation were shared on social media platforms, such as Twitter, which can play an important role in shaping the public's attitudes. We analysed information shared on Twitter for insights into initial public reactions.

E-Cigarette Topics Shared by Medical Professionals: A Comparison of Tweets from the United States and United Kingdom.

Medical professionals are now relying on social media platforms like Twitter to express their recommendations for the use or avoidance of products like electronic cigarettes (e-cigs), which may have long-term health consequences for users. The goal of this study is to compare how physicians from the United States and the United Kingdom talk about e-cigs on Twitter and identify the topics that these groups perceive as salient. Comparing tweets from the U.

E-Cigarette Social Media Messages: A Text Mining Analysis of Marketing and Consumer Conversations on Twitter.

Background: As the use of electronic cigarettes (e-cigarettes) rises, social media likely influences public awareness and perception of this emerging tobacco product.

Objective: This study examined the public conversation on Twitter to determine overarching themes and insights for trending topics from commercial and consumer users.

Methods: Text mining uncovered key patterns and important topics for e-cigarettes on Twitter.

Identifying the public's concerns and the Centers for Disease Control and Prevention's reactions during a health crisis: An analysis of a Zika live Twitter chat.

The arrival of the Zika virus in the United States caused much concern among the public because of its ease of transmission and serious consequences for pregnant women and their newborns. We conducted a text analysis to examine original tweets from the public and responses from the Centers for Disease Control and Prevention (CDC) during a live Twitter chat hosted by the CDC. Both the public and the CDC expressed concern about the spread of Zika virus, but the public showed more concern about the consequences it had for women and babies, whereas the CDC focused more on symptoms and education.

Detecting themes of public concern: a text mining analysis of the Centers for Disease Control and Prevention's Ebola live Twitter chat.

A diagnosis of Ebola on US soil triggered widespread panic. In response, the Centers for Disease Control and Prevention held a live Twitter chat to address public concerns. This study applied a textual analytics method to reveal insights from these tweets that can inform communication strategies.