Social Media and Health

Use of social media platforms and data to disseminate health information and to understand and improve individual and population health behaviors and outcomes.

Online Reviews of Specialized Drug Treatment Facilities—Identifying Potential Drivers of High and Low Patient Satisfaction

Nov. 21, 2019

Anish K. Agarwal, Vivien Wong, Arthur M. Pelullo, Sharath Guntuku, Daniel Polsky, David A. Asch, Jonathan Muruako, Raina M. Merchant

Background: Despite the importance of high-quality and patient-centered substance use disorder treatment, there are no standardized ratings of specialized drug treatment facilities and their services. Online platforms offer insights into potential drivers of high and low patient experience.

Objective: We sought to analyze publicly available online review content of specialized drug treatment facilities and identify themes within high and low ratings.

Design:

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Combining Crowd-Sourcing and Automated Content Methods to Improve Estimates of Overall Media Coverage: Theme Mentions in E-cigarette and Other Tobacco Coverage

Nov. 13, 2019

Laura A. Gibson, Leeann Siegel, Elissa Kranzler, Allyson Volinsky, Matthew B. O’Donnell, Sharon Williams, Qinghua Yang, Yoonsang Kim, Steven Binns, Hy Tran, Veronica Maidel Epstein, Timothy Leffel, Michelle Jeong, Jiaying Liu, Stella Lee, Sherry Emery, Robert C. Hornik...

Abstract [from journal]

Exposure to media content can shape public opinions about tobacco. Accurately describing content is a first step to showing such effects. Historically, content analyses have hand-coded tobacco-focused texts from a few media sources which ignored passing mention coverage and social media sources, and could not reliably capture over-time variation. By using a combination of crowd-sourced and automated coding, we labeled the population of all e-cigarette and other tobacco-related (including cigarettes, hookah, cigars, etc.) 'long-form texts' (focused and

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Studying Expressions of Loneliness in Individuals Using Twitter: An Observational Study

Nov. 4, 2019

Sharath Chandra Guntuku, Rachelle Schneider, Arthur Pelullo, Jami Young, Vivien Wong, Lyle Ungar, Daniel Polsky, Kevin Volpp, Raina Merchant

Abstract [from journal]

Objectives: Loneliness is a major public health problem and an estimated 17% of adults aged 18-70 in the USA reported being lonely. We sought to characterise the (online) lives of people who mention the words 'lonely' or 'alone' in their Twitter timeline and correlate their posts with predictors of mental health.

Setting and Design: From approximately 400 million tweets collected from Twitter in Pennsylvania, USA, between 2012 and 2016, we identified users whose Twitter posts contained the words 'lonely' or '

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Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter

Nov. 1, 2019

Abeed Sarker, Graciela Gonzalez-Hernandez, Yucheng Ruan, Jeanmarie Perrone

Abstract [from journal]

Importance: Automatic curation of consumer-generated, opioid-related social media big data may enable real-time monitoring of the opioid epidemic in the United States.

Objective: To develop and validate an automatic text-processing pipeline for geospatial and temporal analysis of opioid-mentioning social media chatter.

Design, Setting, and Participants: This cross-sectional, population-based study was conducted from December 1, 2017, to August 31, 2019, and used more than 3 years of

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The Relationship Between Exogenous Exposure to "The Real Cost" Anti-smoking Campaign and Campaign-Targeted Beliefs

Robert C. Hornik, PhD
Sep. 26, 2019

Elissa C. Kranzler, Robert C. Hornik

Abstract [from journal]

Though previous evaluations of "The Real Cost" anti-smoking campaign demonstrate effects on anti-smoking beliefs and behaviors, results rely on self-reported recall as a measure of exposure and are thus open to reverse causation concerns. Exogenous measures of exposure, assessed independently of outcomes, support stronger causal inferences. In this study, we examined the relationship between Target Rating Points (TRPs) for specific ads available over four-week periods and anti-smoking beliefs in a national sample of adolescent nonsmokers and

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Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing

Aug. 21, 2019

Karthik V. Sarma, Brennan M.R. Spiegel, Mark W. Reid, Shawn Chen, Raina M. Merchant, Emily Seltzer, Corey W. Arnold

Abstract [from journal]

Social media presents a rich opportunity to gather health information with limited intervention through the analysis of completely unstructured and unlabeled microposts. We sought to estimate the health-related quality of life (HRQOL) of Twitter users using automated semantic processing methods. We collected tweets from 878 Twitter users recruited through online solicitation and in-person contact with patients. All participants completed the four-item Centers for Disease Control Healthy Days Questionnaire at the time of enrollment and 30 days later to

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Content Analysis of Metaphors About Hypertension and Diabetes on Twitter: Exploratory Mixed-Methods Study

Jan. 23, 2019

Lauren Sinnenberg, Christina Mancheno, Frances K Barg, David A Asch, Christy Lee Rivard, Emma Horst-Martz, Alison Buttenheim, Lyle...

ABSTRACT [FROM JOURNAL]

Background: Widespread metaphors contribute to the public’s understanding of health. Prior work has characterized the metaphors used to describe cancer and AIDS. Less is known about the metaphors characterizing cardiovascular disease.

Objective: The objective of our study was to characterize the metaphors that Twitter users employ in discussing hypertension and diabetes.

Methods: We filtered approximately 10 billion tweets for keywords related to diabetes and hypertension. We coded a random...

Protecting Clinical Trial Participants and Study Integrity in the Age of Social Media

Oct. 24, 2018

ABSTRACT [FROM JOURNAL]

Social media communication among clinical trial participants has the potential to pose risks to their safety and to trial integrity. The Social Media ADEPT framework may help mitigate that potential by encouraging investigators and patient partners to work together to Assess social media risks, Design studies to minimize those risks, Educate participants, Preempt problems, and Take additional steps as needed, such as intervening when problems arise.

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