A Sentiment Analysis of Breast Cancer Treatment Experiences and Healthcare Perceptions Across Twitter
Abstract: Background: Social media has the capacity to afford the healthcare industry with valuable feedback
from patients who reveal and express their medical decision-making process, as well as self-reported
quality of life indicators both during and post treatment. In prior work, Crannell et al. , we
have studied an active cancer patient population on Twitter and compiled a set of tweets describing
their experience with this disease. We refer to these online public testimonies as “Invisible Patient
Reported Outcomes” (iPROs), because they carry relevant indicators, yet are difficult to capture
by conventional means of self-report.
Methods: Our present study aims to identify tweets related to the patient experience as an additional
informative tool for monitoring public health. Using Twitter’s public streaming API, we
compiled over 5.3 million “breast cancer” related tweets spanning September 2016 until mid December
2017. We combined supervised machine learning methods with natural language processing to
sift tweets relevant to breast cancer patient experiences. We analyzed a sample of 845 breast cancer
patient and survivor accounts, responsible for over 48,000 posts. We investigated tweet content with
a hedonometric sentiment analysis to quantitatively extract emotionally charged topics.
Results: We found that positive experiences were shared regarding patient treatment, raising support,
and spreading awareness. Further discussions related to healthcare were prevalent and largely
negative focusing on fear of political legislation that could result in loss of coverage.
Conclusions: Social media can provide a positive outlet for patients to discuss their needs and
concerns regarding their healthcare coverage and treatment needs. Capturing iPROs from online
communication can help inform healthcare professionals and lead to more connected and personalized
[edit database entry]
Bongard's work focuses on understanding the general nature of cognition, regardless of whether it is found in humans, animals or robots. This unique approach focuses on the role that morphology and evolution plays in cognition. Addressing these questions has taken him into the fields of biology, psychology, engineering and computer science.
Danforth is an applied mathematician interested in modeling a variety of physical, biological, and social phenomenon. He has applied principles of chaos theory to improve weather forecasts as a member of the Mathematics and Climate Research Network, and developed a real-time remote sensor of global happiness using messages from Twitter: the Hedonometer. Danforth co-runs the Computational Story Lab with Peter Dodds, and helps run UVM's reading group on complexity.
Laurent studies the interaction of structure and dynamics. His research involves network theory, statistical physics and nonlinear dynamics along with their applications in epidemiology, ecology, biology, and sociology. Recent projects include comparing complex networks of different nature, the coevolution of human behavior and infectious diseases, understanding the role of forest shape in determining stability of tropical forests, as well as the impact of echo chambers in political discussions.
Hines' work broadly focuses on finding ways to make electric energy more reliable, more affordable, with less environmental impact. Particular topics of interest include understanding the mechanisms by which small problems in the power grid become large blackouts, identifying and mitigating the stresses caused by large amounts of electric vehicle charging, and quantifying the impact of high penetrations of wind/solar on electricity systems.
Bagrow's interests include: Complex Networks (community detection, social modeling and human dynamics, statistical phenomena, graph similarity and isomorphism), Statistical Physics (non-equilibrium methods, phase transitions, percolation, interacting particle systems, spin glasses), and Optimization(glassy techniques such as simulated/quantum annealing, (non-gradient) minimization of noisy objective functions).