Online discussions have become integral to how people exchange ideas, form opinions, and participate in collective deliberation. While sighted users can comfortably engage with online discussions, blind users who are dependent on screen readers are forced to listen to long threads narrated in a single, monotonic voice that lacks prosodic variation, rhythm, or emotion. This robotic auditory experience not only deteriorates the user engagement with the content but also increases cognitive strain, by making it difficult to remain attentive and discern meaning beyond literal words. In an interview study, most blind participants reported that monotonous narration hindered their ability to detect salient information, perceive emotional cues, and comprehend content authors’ intents in discussions. Many described experiencing mental fatigue when listening to ‘flat’, ‘uninspiring’ voices, noting that their attention tended to diminish quickly over time. The participants also indicated that they often tried to ‘add’ prosodic variation or emotional inflection themselves in their minds, but characterized this compensatory effort as mentally taxing and cognitively demanding. To address this issue, we introduce VoxVista, a multi-voice design framework driven by a large language model that leverages a custom voice-preference dataset to assign personalized voice profiles to user posts in discussions, thereby replacing the traditional monotone narration in screen readers with a more expressive, dynamic, and contextually-aware narration. In a study with 20 blind participants, we observed that VoxVista significantly improved user engagement, comprehension, and willingness to continue listening to longer discussions.

While online shopping platforms provide convenience and autonomy to blind users, their non-visual interactions remain underexplored at a micro-behavioral level. Existing studies have primarily emphasized accessibility and usability challenges but have overlooked how fine-grained, screen reader-driven keystroke-level behaviors reflect users’ cognitive strategies. In this paper, we present the findings of a longitudinal study with 25 blind participants to examine their micro-behavioral patterns, using keyboard activity and screen reader logs on both familiar and unfamiliar e-commerce websites. We complemented this study with semi-structured interviews to contextualize the uncovered micro-behavioral patterns. Our results revealed patterns in how blind users draw upon cognitive maps and well-established shortcut routines developed on familiar websites to streamline navigation on unfamiliar platforms. However, unfamiliar websites, even when structurally accessible, often introduced elevated navigation entropy, increased shortcut failures, and induced more exploratory behavior, as users worked to reconstruct new mental models. Additionally, we also identified a strong preferential structure in keyboard shortcut use, where users maintain a personalized and often chronologically-ranked sequence of keystrokes. Furthermore, most users approached shopping with pre-planned objectives, relying on targeted search queries rather than broad ad-hoc product exploration for securing the ‘best deals’. Based on the study insights, we discuss design considerations for assistive technology developers and e-commerce websites to further improve the online shopping experience for blind users.

Coordinating viewpoints with another person during a collaborative task can provide informative cues on human behavior. Despite the massive shift of collaborative spaces into virtual environments, versatile setups that enable eye-tracking in an online collaborative environment (distributed eye-tracking) remain unexplored. In this study, we present DisETrac- a versatile setup for eye-tracking in online collaborations. Further, we demonstrate and evaluate the utility of DisETrac through a user study. Finally, we discuss the implications of our results for future improvements. Our results indicate promising avenue for developing versatile setups for distributed eye-tracking.

When conducting eye tracking studies, having a mechanism to collect data, build workflows, and validate results in a FAIR (i.e., findable, accessible, interoperable, and reusable) manner, facilitates automation. Given the vast landscape of vendor-specific eye tracking software, adopting FAIR metadata standards for the eye tracking domain is one step towards this. In this paper, we propose an approach to simplify the creation, execution, and validation of eye tracking studies through metadata. Using a metadata format that we developed, we first describe two eye trackers, and two datasets collected using them. Next, we use this metadata to simulate real-time data collection by replaying each dataset. From this replayed data, we analyze eye movements in real-time, and synthesize eye movement data from analytics in real-time. Based on our results, we discuss the utility of metadata in real-time eye tracking studies, and how this idea can be generalized into other applications.

The vastness of the web imposes a prohibitive cost on building large-scale search engines with limited resources. Crawl frontiers thus need to be optimized to improve the coverage and freshness of crawled content. In this paper, we propose an approach for modeling the dynamics of change in the web using archived copies of webpages. To evaluate its utility, we conduct a preliminary study on the scholarly web using 19,977 seed URLs of authors’ homepages obtained from their Google Scholar profiles. We first obtain archived copies of these webpages from the Internet Archive (IA), and estimate when their actual updates occurred. Next, we apply maximum likelihood to estimate their mean update frequency (λ) values. Our evaluation shows that λ values derived from a short history of archived data provide a good estimate for the true update frequency in the short-term, and that our method provides better estimations of updates at a fraction of resources compared to the baseline models. Based on this, we demonstrate the utility of archived data to optimize the crawling strategy of web crawlers, and uncover important challenges that inspire future research directions.

Autism Spectrum Disorder is a developmental disorder that often impairs a child's normal development of the brain. Early Diagnosis is crucial in the long term treatment of ASD, but this is challenging due to the lack of a proper objective measures. Subjective measures often take more time, resources, and have false positives or false negatives. There is a need for efficient objective measures that can help in diagnosing this disease early as possible with less effort. This paper presents EEG and Eye movement data for the diagnosis of ASD using machine learning algorithms. There are number of studies on classification of ASD using EEG or Eye tracking data. However, all of them simply use either Eye movements or EEG data for the classification. In our study we combine Eye movements and EEG data to develop an efficient methodology for diagnosis. This paper presents several models based on EEG, and eye movements for the diagnosis of ASD.

People develop personal information collections consisting of distributed web resources as both reminders that resources exist and to provide rapid access to these resources. Managing such collections is necessary to preserve their value. Unexpected changes within distributed collections can cause them to become outdated, requiring revisions to or removal of no-longer-appropriate resources and replacements for lost resources. In an effort to alleviate this problem, this paper presents a categorization and classification framework including a tool that supports the management and active curation of distributed collections of Web-based resources. We assess the need for such a system and analyze how current tools affect the management of personal collections with survey of 106 participants from online and offline communities. Results of the survey show that personal collections are common and collection management is an issue for ~20% of respondents. Additionally we examine and categorize the various degrees of change that digital documents endure within the boundaries of a distributed collection. Consequently, this paper will focus on two research questions. First, what facets of the change detection process can be automated? And second, looking at this problem from a user standpoint where each document contributes towards the overall meaning of the collection, what strategies can be used to effectively detect the consequences of the various types of change found in document collections?

We present a conceptual framework expanding the use of eye movement as a source of implicit relevance feedback. While gaze time has been the primary feature to be incorporated in interest modeling, this work constructs a model of human oculomotor plant features during user's interaction with multiple everyday applications with the goal of better interpreting user gaze data. The following presents the anatomical reasoning behind incorporating additional gaze features, the integration of the additional features into an existing interest modeling architecture, and a plan for assessing the impact of the addition of the features.

Interactive web search involves selecting which documents to read further and locating the parts of the documents that are relevant to the user's current activity. In this paper, we introduce UIMaP: User Interest Modeling and Personalization, a search task based personal user interest model to support users' information gathering tasks. The novelty of our approach lies in the use of topic modeling to generate fine-grained models of user interest and visualizations that direct user's attention to documents or parts of documents that match user's inferred interests. User annotations are used to help generate personalized visualizations for user's search tasks. Based on 1267 user annotations from 17 users, we show the performance comparisons of four different topic models: LDA+H, LDA+KL, LDA+JSD, and LDA+TopN.