The Design Space of Generative Models

Card et al.'s classic paper "The Design Space of Input Devices" established the value of design spaces as a tool for HCI analysis and invention. We posit that developing design spaces for emerging pre-trained, generative AI models is necessary for supporting their integration into human-centered systems and practices. We explore what it means to develop an AI model design space by proposing two design spaces relating to generative AI models: the first considers how HCI can impact generative models (i.e., interfaces for models) and the second considers how generative models can impact HCI (i.e., models as an HCI prototyping material)

Credibility perceptions of content contributors and consumers in social media

This panel addresses information credibility issues in the context of social media. During this panel, participants will discuss people's credibility perceptions of online content in social media from the perspectives of both content contributors and consumers. Each panelist will bring her own perspective on credibility issues in various social media, including Twitter (Morris), Wikipedia (Metzger; Francke), blogs (Rieh), and social Q&A (Jeon). This panel aims to flesh out multi‐disciplinary approaches to the investigation of credibility and discuss integrated conceptual frameworks and future research directions focusing on assessing and establishing credibility in social media.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/111174/1/meet14505101022.pd

Understanding Blind People's Experiences with Computer-Generated Captions of Social Media Images

ABSTRACT Research advancements allow computational systems to automatically caption social media images. Often, these captions are evaluated with sighted humans using the image as a reference. Here, we explore how blind and visually impaired people experience these captions in two studies about social media images. Using a contextual inquiry approach (n=6 blind/visually impaired), we found that blind people place a lot of trust in automatically generated captions, filling in details to resolve differences between an image's context and an incongruent caption. We built on this in-person study with a second, larger online experiment (n=100 blind/visually impaired) to investigate the role of phrasing in encouraging trust or skepticism in captions. We found that captions emphasizing the probability of error, rather than correctness, encouraged people to attribute incongruence to an incorrect caption, rather than missing details. Where existing research has focused on encouraging trust in intelligent systems, we conclude by challenging this assumption and consider the benefits of encouraging appropriate skepticism

Twitter as health information source : exploring the parameters affecting dementia-related tweets

Unlike other media, research on the credibility of information present on social media is limited. This limitation is even more pronounced in the case of healthcare, including dementia-related information. The purpose of this study was to identify user groups that show high bot-like behavior and profile features that deviation from typical human behavior. We collected 16,691 tweets about dementia posted over the course of a month by 8400 users. We applied inductive coding to categorize users. The BotOrNot? API was used to compute a bot score. This work provides insight into relations between user features and a bot score. We performed analysis techniques such as Kruskal-Wallis, stepwise multiple variable regression, user tweet frequency analysis and content analysis on the data. These were further evaluated for the most frequently referenced URLs in the tweets and most active users in terms of tweet frequency. Initial results indicated that the majority of users are regular users and not bots. Regression analysis revealed a clear relationship between different features. Independent variables in the user profiles such as geo_data and favourites_count, correlated with the final bot score. Similarly, content analysis of the tweets showed that the word features of bot profiles have an overall smaller percentage of words compared to regular profiles. Although this analysis is promising, it needs further enhancements

Addressing the Accessibility of Social Media

Social media platforms are deeply ingrained in society, and they offer many different spaces for people to engage with others. Unfortunately, accessibility barriers prevent people with disabilities from fully participating in these spaces. Social media users commonly post inaccessible media, including videos without captions (which are important for people who are deaf or hard of hearing) and images without alternative text (descriptions read aloud by screen readers for people who are blind). Users with motor impairments must find workarounds to deal with the complex user interfaces of these platforms, and users with cognitive disabilities may face barriers to composing and sharing information. Accessibility researchers, industry practitioners, and end-users with disabilities will come together to outline challenges and solutions for improving social media accessibility. The workshop starts with a panel of end-users with disabilities who will recount their Perspectives of Successes and Barriers. Industry professionals from social media companies (e.g., Facebook and LinkedIn) will detail their Design Process and Implementation Challenges in a panel with questions from attendees. The attendees will share their work and tackle Open Challenges and Future Research Directions. This workshop will forge collaborations between researchers and practitioners, and define high-priority accessibility challenges for social media platforms