VisOHC: Designing Visual Analytics for Online Health Communities

Abstract

Through online health communities (OHCs), patients and caregivers exchange their illness experiences and strategies for overcoming the illness, and provide emotional support. To facilitate healthy and lively conversations in these communities, their members should be continuously monitored and nurtured by OHC administrators. The main challenge of OHC administrators' tasks lies in understanding the diverse dimensions of conversation threads that lead to productive discussions in their communities. In this paper, we present a design study in which three domain expert groups participated, an OHC researcher and two OHC administrators of online health communities, which was conducted to find with a visual analytic solution. Through our design study, we characterized the domain goals of OHC administrators and derived tasks to achieve these goals. As a result of this study, we propose a system called VisOHC, which visualizes individual OHC conversation threads as collapsed boxes–a visual metaphor of conversation threads. In addition, we augmented the posters' reply authorship network with marks and/or beams to show conversation dynamics within threads. We also developed unique measures tailored to the characteristics of OHCs, which can be encoded for thread visualizations at the users' requests. Our observation of the two administrators while using VisOHC showed that it supports their tasks and reveals interesting insights into online health communities. Finally, we share our methodological lessons on probing visual designs together with domain experts by allowing them to freely encode measurements into visual variables.

Bridging Text Visualization and Mining: A Task-Driven Survey

“…[25], [27], [35], [39], [42], [67], [75], [80], [86], [96], [116], [125], [127], [142], [144], [154], [163], [177], [183], [187], [208], [212], [220], [233], [242], [253], [258], [264], [365] word cloud (56) [5], [22], [26], [37], [53], [54], [56], [64], [81], [87], [96], [128], [147], [157], [161], [196], [173], [186], [189], [192], [203], [217...…”

“…geotagging 8[13], [184], [201] data/information extraction (535) [65], [184], [218] domain adaption (54) [325], [343], [375] data enrichment 7[22], [123], [233] alignment (3) [76], [9], [273] event analysis (113) [54], [115], [220] discourse analysis (28) [16], [192], [370] content analysis (78) [157], [186], [322] sentiment analysis (467) [250], [347], [333] lexical/syntactical analysis (73) [250], [333], [347] question answering (156) [153], [265], [295] text summarization (526) [50], [64], [187] classification 1299cross language text classification (2) [130], [350] image classification (37) [219], [227], [352] sub-document classification (4) [111], [142], [172] binary classification (64) [237],…”

The State of the Art in Sentiment Visualization

“…[GBB14], VisOHC by Kwon et al . [KKL*16] and GeoSentiment by Pino et al . [PKS16], and stacked bar charts used by Dehiya and Mueller [DM16] and Mohammad et al .…”

Detecting clinically related content in online patient posts

“…Even if moderators exist, sifting through a large number of community posts to identify posts needing clinical expertise can be overwhelming. To address this issue, a research team developed visualization tools to help moderators understand trends of aggregated online health community posts [14]. Furthermore, Huh et al showed that moderators participate in online health communities to provide clinical expertise [3] and recommended patients to see a doctor [2].…”