Towards Automatic Evaluation of Health-Related CQA Data

The paper reports on evaluation of Russian community question answering (CQA) data in health domain. About 1,500 question-answer pairs were manually evaluated by medical professionals, in addition automatic evaluation based on reference disease-medicine pairs was performed. Although the results of the manual and automatic evaluation do not fully match, we find the method still promising and propose several improvements. Automatic processing can be used to dynamically monitor the quality of the CQA content and to compare different data sources. Moreover, the approach can be useful for symptomatic surveillance and health education campaigns.This work is partially supported by the Russian Foundation for Basic Research, project #14-07-00589 “Data Analysis and User Modelling in Narrow-Domain Social Media”. We also thank assessors who volunteered for the evaluation and Mail.Ru for granting us access to the data

Mining Social Media to Understand Consumers' Health Concerns and the Public's Opinion on Controversial Health Topics.

Social media websites are increasingly used by the general public as a venue to express health concerns and discuss controversial medical and public health issues. This information could be utilized for the purposes of public health surveillance as well as solicitation of public opinions. In this thesis, I developed methods to extract health-related information from multiple sources of social media data, and conducted studies to generate insights from the extracted information using text-mining techniques. To understand the availability and characteristics of health-related information in social media, I first identified the users who seek health information online and participate in online health community, and analyzed their motivations and behavior by two case studies of user-created groups on MedHelp and a diabetes online community on Twitter. Through a review of tweets mentioning eye-related medical concepts identified by MetaMap, I diagnosed the common reasons of tweets mislabeled by natural language processing tools tuned for biomedical texts, and trained a classifier to exclude non medically-relevant tweets to increase the precision of the extracted data. Furthermore, I conducted two studies to evaluate the effectiveness of understanding public opinions on controversial medical and public health issues from social media information using text-mining techniques. The first study applied topic modeling and text summarization to automatically distill users' key concerns about the purported link between autism and vaccines. The outputs of two methods cover most of the public concerns of MMR vaccines reported in previous survey studies. In the second study, I estimated the public's view on the ac{ACA} by applying sentiment analysis to four years of Twitter data, and demonstrated that the the rates of positive/negative responses measured by tweet sentiment are in general agreement with the results of Kaiser Family Foundation Poll. Finally, I designed and implemented a system which can automatically collect and analyze online news comments to help researchers, public health workers, and policy makers to better monitor and understand the public's opinion on issues such as controversial health-related topics.PhDInformationUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120714/1/owenliu_1.pd