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 treatment regimens.

Introduction: Twitter has shown potential for monitoring public health trends, BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , disease surveillance, BIBREF6 , and providing a rich online forum for cancer patients, BIBREF7 . Social media has been validated as an effective educational and support tool for breast cancer patients, BIBREF8 , as well as for generating awareness, BIBREF9 . Successful supportive organizations use social media sites for patient interaction, public education, and donor outreach, BIBREF10 . The advantages, limitations, and future potential of using social media in healthcare has been thoroughly reviewed, BIBREF11 . Our study aims to investigate tweets mentioning “breast” and “cancer" to analyze patient populations and selectively obtain content relevant to patient treatment experiences. Our previous study, BIBREF0 , collected tweets mentioning “cancer” over several months to investigate the potential for monitoring self-reported patient treatment experiences. Non-relevant tweets (e.g. astrological and horoscope references) were removed and the study identified a sample of 660 tweets from patients who were describing their condition. These self-reported diagnostic indicators allowed for a sentiment analysis of tweets authored by patients. However, this process was tedious, since the samples were hand verified and sifted through multiple keyword searches. Here, we aim to automate this process with machine learning context classifiers in order to build larger sets of patient self-reported outcomes in order to quantify the patent experience. Patients with breast cancer represent a majority of people affected by and living with cancer. As such, it becomes increasingly important to learn from their experiences and understand their journey from their own perspective. The collection and analysis of invisible patient reported outcomes (iPROs) offers a unique opportunity to better understand the patient perspective of care and identify gaps meeting particular patient care needs.

Question: How were breast cancer related posts compiled from the Twitter streaming API?
Answer: By using  keywords `breast' AND `cancer' in tweet collecting process.