Machine Learning and Natural Language Processing for Geolocation-Centric Monitoring and Characterization of Opioid-Related Social Media Chatter
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
...#OldBoysClub: How Academic Twitter May Perpetuate Gender Disparities in Health Services Research
Outside of the brick and mortar walls of academic institutions – and conferences attended by researchers -- there is an invisible conversation happening. Academic Twitter, as it’s affectionately known, is a world unto itself. Yet, it turns out, there are ways in which it bears a striking resemblance to the familiar “old boys’ club.”
The Relationship Between Exogenous Exposure to "The Real Cost" Anti-smoking Campaign and Campaign-Targeted Beliefs
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
...Estimating the Health-Related Quality of Life of Twitter Users Using Semantic Processing
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
...Raina Merchant Named National Academy of Medicine Emerging Leader
Content Analysis of Metaphors About Hypertension and Diabetes on Twitter: Exploratory Mixed-Methods Study
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...
Photo Page: 2018 Penn Behavioral Science and Health Symposium
Asch and Merchant Newspaper Commentary Targets Fake News in Medicine
Mountain Top Behavioral Economics: 2018 Roybal Retreat Photo Page
Photo Page: Poster Session at 2018 Roybal Behavioral Economics Retreat
Penn Nursing's Rising Guru of Women's Mobile Health
Protecting Clinical Trial Participants and Study Integrity in the Age of Social Media
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.