Mining User-Generated Data to Examine Online Information Seeking for Reproductive and Sexual Health

With the ubiquity of the Internet, health information that previously could only be obtained from a healthcare provider is now easily accessible to consumers. Indeed, many people turn to the Internet for health information despite their preference to see a healthcare provider. Infodemiology leverages the digital footprints left behind from online health information seeking to derive insights into human behavior. By applying computational approaches to passively-collected, user-generated data, a number of useful insights have been generated for public health issues spanning including chronic diseases, infectious diseases, behavioral health, and mental health.

However, few infodemiology studies have examined reproductive and sexual health. This area is particularly ripe for exploration as the sensitivity of these health concerns often drive people to trade accuracy (by obtaining information from a trusted provider) in lieu of the anonymity of the Internet. The main contribution of this dissertation is to fill this existing gap in infodemiology literature by contributing two case studies of online information seeking for reproductive and sexual health.

Prior to delving into the case studies, I provide a general backdrop for online information seeking and existing models of health information seeking that conceptualize health information seeking behaviors and the search process. Following this background, I present the first case studies examining online search engine queries for contraceptive methods during a period of heightened uncertainty for U.S. healthcare reform. The second case study examines online information seeking on the subreddit r/STD for recent information seeking related to sexually transmitted infections (STIs). Both of these case studies are extremely timely given the focus on the repeal of the Affordable Care Act and record high rates of STIs in the U.S., respectively.

Specifically, I point to some of the most interesting and important results of these case studies. First, I identified unprecedented information seeking for intrauterine devices (IUDs) in response to uncertainty regarding the contraceptive mandate. This is critical given many public health leaders have speculated about the potential implications of the recent election and ours is the first study to link the change in administration to potential health outcomes using data. Second, I identified salient information needs of a previously unstudied platform, the subreddit r/STD. This community primarily seeks information about the human papillovirus (HPV), herpes simplex virus (HSV), transmission and risk of STIs during oral sex, and medical testing window periods. In the process, I identified and examined sharing of online resources, which is understudied in public health. Finally, I find evidence supporting experience and salience (external and internal uncertainty) as motivators linking these health information searches.

Both of these studies have implications for medical decision making and can support recent calls in the public health community for data-driven approaches to determine and tailor health communication.