HealthTrust: Assessing the Trustworthiness of Healthcare Information on the Internet

As well recognized, healthcare information is growing exponentially and is made more available to public. Frequent users such as medical professionals and patients are highly dependent on the web sources to get the appropriate information promptly. However, the trustworthiness of the information on the web is always questionable due to the fast and augmentative properties of the Internet. Most search engines provide relevant pages to given keywords, but the results might contain some unreliable or biased information. Consequently, a significant challenge associated with the information explosion is to ensure effective use of information. One way to improve the search results is by accurately identifying more trustworthy data. Surprisingly, although trustworthiness of sources is essential for a great number of daily users, not much work has been done for healthcare information sources by far. In this dissertation, I am proposing a new system named HealthTrust, which automatically assesses the trustworthiness of healthcare information over the Internet. In the first phase, an unsupervised clustering using graph topology, on our collection of data is employed. The goal is to identify a relatively larger and reliable set of trusted websites as a seed set without much human efforts. After that, a new ranking algorithm for structure-based assessment is adopted. The basic hypothesis is that trustworthy pages are more likely to link to trustworthy pages. In this way, the original set of positive and negative seeds will propagate over the Web graph. With the credibility-based discriminators, the global scoring is biased towards trusted websites and away from untrusted websites. Next, in the second phase, the content consistency between general healthcare-related webpages and trusted sites is evaluated using information retrieval techniques to evaluate the content-semantics of the webpage with respect to the medical topics. In addition, graph modeling is employed to generate contents-based ranking for each page based on the sentences in the seed pages. Finally, in order to integrate the two components, an iterative approach that integrates the credibility assessments from structure-based and content-based methods to give a final verdict - a HealthTrust score for each webpage is exploited. I demonstrated the first attempt to integrate structure-based and content-based approaches to automatically evaluate the credibility of online healthcare information through HealthTrust and make fundamental contributions to both information retrieval and healthcare informatics communities

Positivity Bias in Customer Satisfaction Ratings

Customer ratings are valuable sources to understand their satisfaction and are critical for designing better customer experiences and recommendations. The majority of customers, however, do not respond to rating surveys, which makes the result less representative. To understand overall satisfaction, this paper aims to investigate how likely customers without responses had satisfactory experiences compared to those respondents. To infer customer satisfaction of such unlabeled sessions, we propose models using recurrent neural networks (RNNs) that learn continuous representations of unstructured text conversation. By analyzing online chat logs of over 170,000 sessions from Samsung's customer service department, we make a novel finding that while labeled sessions contributed by a small fraction of customers received overwhelmingly positive reviews, the majority of unlabeled sessions would have received lower ratings by customers. The data analytics presented in this paper not only have practical implications for helping detect dissatisfied customers on live chat services but also make theoretical contributions on discovering the level of biases in online rating platforms.Comment: This paper will be presented at WWW'18 conferenc

A Study on Urban Regeneration Policy Change in Korea

After the physical redevelopment and reconstruction in the late 1970s, the paradigm on urban regeneration in Korea shifted from maintenance to restoration and sustainability. This study highlighted that those changes occurred rapidly and not gradually over a short period of time. This study researched diachronic changes on urban regeneration policies after the 1970s in Korea using an analysing model that compensated for the theoretical limits of Hogwood and Peters. The limitations of former policies and internal and external socio-economic factors are shown to have affected dynamic policy changes. This study’s academic significance is that it suggests policy implications for cities that have similar urban growth processes to Korea

Lightweight and Robust Representation of Economic Scales from Satellite Imagery

Satellite imagery has long been an attractive data source that provides a wealth of information on human-inhabited areas. While super resolution satellite images are rapidly becoming available, little study has focused on how to extract meaningful information about human habitation patterns and economic scales from such data. We present READ, a new approach for obtaining essential spatial representation for any given district from high-resolution satellite imagery based on deep neural networks. Our method combines transfer learning and embedded statistics to efficiently learn critical spatial characteristics of arbitrary size areas and represent them into a fixed-length vector with minimal information loss. Even with a small set of labels, READ can distinguish subtle differences between rural and urban areas and infer the degree of urbanization. An extensive evaluation demonstrates the model outperforms the state-of-the-art in predicting economic scales, such as population density for South Korea (R^2=0.9617), and shows a high potential use for developing countries where district-level economic scales are not known.Comment: Accepted for oral presentation at AAAI 202

Personal Network Recovery Enablers and Relapse Risks for Women With Substance Dependence

We examined the experiences of women in treatment for substance dependence and their treatment providers about personal networks and recovery. We conducted six focus groups at three women’s intensive substance abuse treatment programs. Four coders used thematic analysis to guide the data coding and an iterative process to identify major themes. Coders identified social network characteristics that enabled and impeded recovery and a reciprocal relationship between internal states, relationship management, and recovery. Although women described adding individuals to their networks, they also described managing existing relationships through distancing from or isolating some members to diminish their negative impact on recovery. Treatment providers identified similar themes, but focused more on contextual barriers than the women. The focus of interventions with this population should be on both internal barriers to personal network change such as mistrust and fear, and helping women develop skills for managing enduring network relationships