Using Social Media Websites to Support Scenario-Based Design of Assistive Technology

Indiana University-Purdue University Indianapolis (IUPUI)Having representative users, who have the targeted disability, in accessibility studies is vital to the validity of research findings. Although it is a widely accepted tenet in the HCI community, many barriers and difficulties make it very resource-demanding for accessibility researchers to recruit representative users. As a result, researchers recruit non-representative users, who do not have the targeted disability, instead of representative users in accessibility studies. Although such an approach has been widely justified, evidence showed that findings derived from non-representative users could be biased and even misleading. To address this problem, researchers have come up with different solutions such as building pools of users to recruit from. But still, the data is not widely available and needs a lot of effort and resource to build and maintain. On the other hand, online social media websites have become popular in the last decade. Many online communities have emerged that allow online users to discuss health-related subjects, exchange useful information, or provide emotional support. A large amount of data accumulated in such online communities have gained attention from researchers in the healthcare domain. And many researches have been done based on data from social media websites to better understand health problems to improve the wellbeing of people. Despite the increasing popularity, the value of data from social media websites for accessibility research remains untapped. Hence, my work aims to create methods that could extract valuable information from data collected on social media websites for accessibility practitioners to support their design process. First, I investigate methods that enable researchers to effectively collect representative data from social media websites. More specifically, I look into machine learning approaches that could allow researchers to automatically identify online users who have disabilities (representative users). Second, I investigate methods that could extract useful information from user-generated free-text using techniques drawn from the information extraction domain. Last, I explore how such information should be visualized and presented for designers to support the scenario-based design process in accessibility studies

What Are Our Patients Telling Each Other That They Aren't Telling Us? A Social Media Content Analysis Examining Prominently Used Open Source Groups on Social Media Platforms for Novel Solutions to Commonly Experienced Prosthetic Problems

Aim of the Study: The specific aims of this study were to investigate real-world social media interactions among the amputee community, targeting novel approaches to commonly experienced prosthetic problems. Potential areas of insight included information regarding challenges that were being faced, life hacks in use, advice being given across message boards, and negative impacts of their prostheses users experience that might be corrected with future research development if prosthetics practitioners were aware of the problems being discussed. Background: Social media content analysis has been used in the Technology and Communications fields for years, but it has only recently been applied to healthcare. After a review of the literature, it was determined that content analysis of social media has never previously been applied to the field of prosthetics and orthotics. Methods: The approach was to examine specifically identified, open-access social media groups across multiple social media platforms, data-mining posts and coding the information accordingly in order to perform statistical analysis across groups, subject matters, and social media platforms.Topics of interest included common prosthetic problems, comfort, cosmesis, skin type, comorbidities, emerging technologies, phantom pain, and prosthetics life hacks. Results: Statistical analysis was performed based on the numbers of postings pertaining to certain topics in order to compare data across social media groups, social media platforms, identifiable user demographics, and any other potentially pertinent relationships that could be analyzed. The outcomes for this project include the codes, the categories, and the resultant findings of the statistical analysis. Conclusions: The most commonly identified problem within the data was comfort. Facebook data proved more likely to have posters sharing stories, posters on Reddit were more likely to be asking questions. Advertisements were more prominent on Facebook while research-based posts were more common on Reddit. Life hacks were rarely discussed. Family members of amputees were more likely to discuss the injury location, cause, and comorbidities than amputees themselves were. Facebook posters were more likely to fall into the category of advocacy groups. Posters on Reddit were more likely to fall into the categories of health care providers, vendors, and those considering undergoing amputation surger