As people increasingly turn to AI for personal deliberation beyond task-oriented assistance, concerns about sycophancy in these value-laden contexts have grown. Unlike human flattery, which is intentional and self-interested, AI sycophancy emerges as a byproduct of RLHF’s reward structure for user-preference alignment. Yet the observable behavior is similar: both produce responses that preserve what users want to hear. Focusing on this phenomenon through Goffman’s face-work framework, we operationalize AI sycophancy as excessive face-saving, either active (preserving positive face through agreement) or passive (preserving negative face by withholding challenge). In a mixed-methods study (N=31), participants engaged with AI across three moral dilemmas under these conditions and a non-sycophantic neutral baseline. Sycophantic responses increased decision confidence but reduced open-minded thinking; participants felt supported yet found the conversations unproductive. Neutral responses, though initially uncomfortable, promoted cognitive flexibility and meaningful deliberation. These findings reveal a confidence-competence trade-off in AI-mediated moral reasoning and suggest that effective AI for personal deliberation requires calibrated friction, not unconditional agreement.

While it is known that North Korean defectors (NKDs) struggle with South Korea's healthcare system, the specific challenges of their patient journey remain underexplored. To investigate this, we conducted interviews with 10 NKDs about an 8-step patient journey and identified the clinical consultation step as a critical barrier for all participants, marked by three key challenges: expressing symptoms, managing social and cultural concerns, and overcoming language differences. In response, we developed Medibridge, a mobile prototype that allows users to rehearse with an AI doctor before a real hospital visit to generate a tangible "Helper Note"for their actual consultation. Our evaluation with 15 NKDs showed improvements in perceived communication capability, including greater expression clarity, reduced social and cultural concerns, and enhanced linguistic confidence. Our contributions include an empirical understanding of NKDs' healthcare challenges, a novel AI-powered rehearsal system that prepares users for real-world clinical communication, and design implications for inclusive technologies for displaced populations.

Online browsing often requires balancing open-ended exploration with focused comparison, yet most interfaces remain static regardless of user focus. Adaptive interfaces offer a way to better align interface presentation with browsing needs, but how such adaptations should be triggered, designed, and controlled in practice remains unclear. We investigate adaptive browsing interfaces that modify information hierarchy, information granularity, and session-based ordering in response to inferred browsing intent. Drawing from a conceptual framework of five adaptation dimensions, we implement ReLay, a browser-based probe that applies lightweight, in-situ adaptations automatically while allowing user overrides. In a two-phase study (n = 10), participants welcomed adaptive changes when they were transparent, consistent, and easily reversible. Users approached control as calibration rather than error correction, verifying system behavior before relying on it. These findings illustrate how intent-responsive, controllable adaptation can support browsing without diminishing user agency. Our work contributes (1) a conceptual framework for adaptive interface behaviors in online browsing, (2) an instantiation of the framework through three selected dimensions, and (3) empirical insights into user acceptance, control, and design implications for adaptive browsing systems.

Understanding unfamiliar codebases is essential yet challenging in software development. Visual aids such as call graphs and control flow graphs can help, but often lead to information overload and limited interactivity. Meanwhile, LLM-based code assistants provide accessible natural language explanations that reduce cognitive barriers, but lack spatial context for code navigation. We posit that integrating these two complementary approaches can address their respective limitations. To validate this integration, we introduce CodeVoyager, a tool that combines LLM with interactive visual aids to support more effective code comprehension. We first conducted an exploratory study (n=11) to assess the tool’s potential and identify areas for refinement. Following iterative refinement, we evaluated the enhanced tool against a widely used chat-based code assistant in a within-subjects study (n=16). Results showed that CodeVoyager improved code comprehension and increased user trust. These improvements were achieved by enabling seamless interaction between textual explanations and visual code exploration, mirroring how developers naturally discuss code. This work contributes to visual-LLM integrated developer tools through (1) a novel integration approach mirroring natural code discussion, (2) empirical evidence of improved comprehension and trust, and (3) design implications for multimodal code comprehension systems.

Generative information retrieval (GenIR) enables users to obtain synthesized information through iterative interaction with LLMs, fundamentally reshaping how AI-generated content is produced and consumed. Within this shift, users may encounter AI-generated informational content through two primary pathways: actively creating it themselves or consuming content generated by others. We examine whether authorship biases evaluation---whether users judge AI output from their own interactions more favorably than equivalent output from others. In a mixed-methods experiment (N=28, 2×2 within-subjects), participants interacted with an AI system to retrieve and craft information, then evaluated both their own result and equivalent output generated through the same process but framed as someone else's. Results reveal a selective authorship bias: participants rated self-obtained information significantly higher in quality, but showed no corresponding difference in trust. This pattern suggests that hallucination-aware skepticism constrained trust judgments, but could not prevent quality-driven selection behavior, even in the presence of information conflicts. Given that iterative interactions are inherent to GenIR, diverse interventions seem needed to support users' critical evaluation.

Conversational shopping agents powered by large language models are increasingly used for online product exploration, yet the role of interaction style in shaping shopping behavior and user experience remains underexplored in shopping IR. To address the gap, we conducted two studies in experience-goods domains. Study 1 involved 24 participants and compared a neutral conversational agent with a traditional product search interface, confirming functional adequacy and identifying two unmet needs, self-reflective preference structuring and socially grounded relational guidance. Study 2 involved 30 participants and evaluated two personas derived from these needs, Self-Mirroring and Relational Peer, against the same neutral agent in a within-subjects design with information availability held constant. Self-Mirroring increased critical thinking scores and sustained follow-up questioning of retrieved content, whereas Relational Peer increased social presence while reducing explicit verification behaviors such as comparing alternatives and checking conditions. The discussion outlines implications for AI shopping agents that adapt interaction style to decision context, balancing efficient exploration with user-led evaluation.

Ensuring visual content accessible on websites is essential for users with visual impairments. However, existing alt text authoring tools often provide alt text only in the primary language or use literal machine translations for other languages, often failing to capture cultural nuances and resulting in inaccurate or misleading descriptions. To overcome this limitation, we introduce AltCAT, the first Alt text authoring tool for web developers that incorporates Culturally-Aware Translation. AltCAT integrates culture-specific knowledge and region-appropriate image description conventions into alt text translations. Our tool consists of two main components: an Alt Text Processing Engine that detects images and alt text on a webpage, generates high-quality alt text, and provides culturally-aware translations using large multimodal models, and an Interactive Authoring Interface for refinement and HTML export. Our key contributions are introducing culturally-aware alt text translation and a practical authoring tool for multilingual web accessibility.

Conversational argument search systems influence how users access diverse perspectives but are prone to selective exposure. To address this, we propose two strategies: an interface-level multi-agent framework that structures perspective presentation and an interaction-level questioning strategy that encourages deeper engagement. We evaluate these strategies through a 2 x 2 factorial user study, examining their impact on selective exposure. Results show that the multi-agent setup facilitates broader perspective comparison, while agent-initiated questioning fosters deeper reflection; together, they promote more balanced argument access. Based on these findings, we discuss conversational search systems to mitigate selective exposure by implementing multi-agent interactions and questioning mechanisms.

The automatic generation of sports highlight videos is emerging in both the sports entertainment domain and research community. Earlier methods for generating highlights rely on visual-audio cues or contextual cues, so they may not capture the overall flow of the game well. In this paper, we propose a technique based on Win Probability Added (WPA), an empirical sabermetric baseball statistic, to generate baseball highlights that can better reflect in-game dynamics. Additionally, we introduce methods for generating “biased” highlights toward one team by systematically manipulating WPAs. Through a mixed-method user study with 43 baseball enthusiasts, we found that participants evaluated WPA-based highlights more favorably than existing AI highlights. For (un)favorably biased highlights, the game result (win/loss) was the most dominating factor in user perception, but bias directions and strengths also had nuanced effects on them. Our work contributes to the development of automated tools for generating customized sports highlights.

Many families often live geographically apart from each other due to work, education, or marriage. Therefore, long-distance families frequently use computer-mediated communication (CMC) tools to stay connected. While CMC tools have significantly improved family communication, they cannot fully mediate social presence. To examine the potential of telepresence robots for improving long-distance family communication, we conducted a two-week qualitative in situ study involving eight families. We analyzed recorded videos of their family interactions and conducted pre- and post-deployment interviews. Our findings highlight telepresence robots’ potential as family communication tools, enabling immersive, natural, and dynamic interactions through physical embodiment and autonomy. Particularly, we identified five categories of family interaction mediated by telepresence robots: engaging in multi-party family communication, exploring home, restoring family routines, providing support, and having joint physical activities. Based on our findings, we present design guidelines for leveraging telepresence robots as effective family communication tools.

The adoption of generative AI technologies is swiftly expanding. Services employing both linguistic and multimodal models are evolving, offering users increasingly precise responses. Consequently, human reliance on these technologies is expected to grow rapidly. With the premise that people will be impacted by the output of AI, we explored approaches to help AI output produce better results. Initially, we evaluated how contemporary AI services competitively meet user needs, then examined society's depiction as mirrored by Large Language Models (LLMs). We did a query experiment, querying about social conventions in various countries and eliciting a one-word response. We compared the LLMs' value judgments with public data and suggested a model of decision-making in value-conflicting scenarios which could be adopted for future machine value judgments. This paper advocates for a practical approach to using AI as a tool for investigating other remote worlds. This research has significance in implicitly rejecting the notion of AI making value judgments and instead arguing a more critical perspective on the environment that defers judgemental capabilities to individuals. We anticipate this study will empower anyone, regardless of their capacity, to receive safe and accurate value judgment-based outputs effectively.

The self-reference effect is a psychological phenomenon where information relating to oneself is processed more deeply and remembered more effectively than other information. We propose "self-referential reviews," crafted by merging personal information with existing reviews using the novel "Self-Referential ReviewMaker" prototype, which leverages Large Language Models (LLMs). The essence of the "self-referential review" lies in harnessing the self-reference effect, making the readers feel as if they are the protagonist of the review. To validate the efficacy of self-referential reviews, we conducted a user study focusing on online reviews with thirty-four participants. The contributions of our paper are centered around self-referential reviews, highlighting (1) the creation of these reviews using our new prototype, Self-Referential ReviewMaker, (2) their effectiveness in enhancing review helpfulness through the self-reference effect, and (3) the identification of additional factors influencing the self-reference effect with further discussion on enhancing user-focused review systems.

Since the COVID-19 pandemic, video conferencing platforms have given rise to new virtual activities, such as virtual study rooms where users utilize video to share ambient presence for study motivation. In virtual study rooms, it can be challenging for the users to determine what to reveal and what to hide on camera, as the video needs to strongly convey their presence without revealing more than necessary. In this paper, we investigate the conflicting needs of virtual study room users to reveal and hide on camera, as well as the methods they employ to cope with these needs using videos. To this end, we conducted a three-step qualitative study. The first study involved interviews to discover the key user needs that entail the conflict to reveal and hide. The second study utilized virtual study room screen analysis to identify the video features that characterize virtual study room videos. In the last study, we employed interviews to associate the video features with the key user needs. Based on these findings, we discussed the effects of studying together that could be applied to a non-physical and non-interactive co-studying environment and the need for further development of video conferencing tools to effectively share ambient presence.

Studies on supervised machine learning (ML) recommend involving workers from various backgrounds in training dataset labeling to reduce algorithmic bias. Moreover, sophisticated tasks for categorizing objects in images are necessary to improve ML performance, further complicating micro-tasks. This study aims to develop a task design incorporating the fair participation of people, regardless of their specific backgrounds or task’s difficulty. By collaborating with 75 labelers from diverse backgrounds for 3 months, we analyzed workers’ log-data and relevant narratives to identify the task’s hurdles and helpers. The findings revealed that workers’ decision-making tendencies were affected by the "community" that positively helps workers. Also, the machine’s feedback perceived by workers could make people easily engaged in works. Based on these findings, we suggest an extended human-in-the-loop approach that connects labelers, machines, and communities rather than isolating individual workers.

Recommendation system often suffers from popularity bias. Often the training data inherently exhibits long-tail distribution in item popularity (data bias). Moreover, the recommendation systems could give unfairly higher recommendation scores to popular items even among items a user equally liked, resulting in over-recommendation of popular items (model bias). In this study we propose a novel method to reduce the model bias while maintaining accuracy by directly regularizing the recommendation scores to be equal across items a user preferred. Akin to contrastive learning, we extend the widely used pairwise loss (BPR loss) which maximizes the score differences between preferred and unpreferred items, with a regularization term that minimizes the score differences within preferred and unpreferred items, respectively, thereby achieving both high debias and high accuracy performance with no additional training. To test the effectiveness of the proposed method, we design an experiment using a synthetic dataset which induces model bias with baseline training; we showed applying the proposed method resulted in drastic reduction of model bias while maintaining accuracy. Comprehensive comparison with earlier debias methods showed the proposed method had advantages in terms of computational validity and efficiency. Further empirical experiments utilizing four benchmark datasets and four recommendation models indicated the proposed method showed general improvements over performances of earlier debias methods. We hope that our method could help users enjoy diverse recommendations promoting serendipitous findings. Code available at https://github.com/stillpsy/popbias.

Writing about experiences of trauma and other challenges in life is known to provide measurable health benefits. Though writing for an audience may ensure better benefits, confiding one's most troubled memories in others risks a social stigma. Conversational agents can provide a virtual audience that ensures privacy and allows social disclosure. To understand the writing experience with an agent, we created Diarybot, a chatbot assistant for expressive writing. We designed two versions, Basic and Responsive, to explore the writing experience with and without bot follow-up interactions compared to a Google doc baseline. Findings from a 4-day user study with 30 participants reveal that social disclosure with Diarybot can encourage narrative writing, with relative ease and emotional expression in Basic chat. Responsive chat can mediate social acceptance of the bot and provide guidance for self-reflection in the process. We discuss design reflections on social disclosure with agents in pursuit of wellbeing.

Individuals interact with algorithms in various ways. Users even game and circumvent algorithms so as to achieve favorable outcomes. This study aims to come to an understanding of how various stakeholders interact with each other in tricking algorithms, with a focus towards online review communities. We employed a mixed-method approach in order to explore how and why users write machine non-translatable reviews as well as how those encrypted messages are perceived by those receiving them. We found that users are able to find tactics to trick the algorithms in order to avoid censoring, to mitigate interpersonal burden, to protect privacy, and to provide authentic information for enabling the formation of informative review communities. They apply several linguistic and social strategies in this regard. Furthermore, users perceive encrypted messages as both more trustworthy and authentic. Based on these findings, we discuss implications for online review community and content moderation algorithms.

Artificial intelligence (AI) algorithms are making remarkable achievements even in creative fields such as aesthetics. However, whether those outside the machine learning (ML) community can sufficiently interpret or agree with their results, especially in such highly subjective domains, is being questioned. In this paper, we try to understand how different user communities reason about AI algorithm results in subjective domains. We designed AI Mirror, a research probe that tells users the algorithmically predicted aesthetic scores of photographs. We conducted a user study of the system with 18 participants from three different groups: AI/ML experts, domain experts (photographers), and general public members. They performed tasks consisting of taking photos and reasoning about AI Mirror's prediction algorithm with think-aloud sessions, surveys, and interviews. The results showed the following: (1) Users understood the AI using their own group-specific expertise; (2) Users employed various strategies to close the gap between their judgments and AI predictions overtime; (3) The difference between users' thoughts and AI pre-dictions was negatively related with users' perceptions of the AI's interpretability and reasonability. We also discuss design considerations for AI-infused systems in subjective domains.

Although group chat discussions are prevalent in daily life, they have a number of limitations. When discussing in a group chat, reaching a consensus often takes time, members contribute unevenly to the discussion, and messages are unorganized. Hence, we aimed to explore the feasibility of a facilitator chatbot agent to improve group chat discussions. We conducted a needfinding survey to identify key features for a facilitator chatbot. We then implemented GroupfeedBot, a chatbot agent that could facilitate group discussions by managing the discussion time, encouraging members to participate evenly, and organizing members' opinions. To evaluate GroupfeedBot, we performed preliminary user studies that varied for diverse tasks and different group sizes. We found that the group with GroupfeedBot appeared to exhibit more diversity in opinions even though there were no differences in output quality and message quantity. On the other hand, GroupfeedBot promoted members' even participation and effective communication for the medium-sized group.

Automated journalism refers to the generation of news articles using computer programs. Although it is widely used in practice, its user experience and interface design remain largely unexplored. To understand the user perception of an automated news system, we designed NewsRobot, a research prototype that automatically generated news on major events of the PyeongChang 2018 Winter Olympic Games in real-time. It produces six types of news by combining two kinds of content (general/individualized) and three styles (text, text+image, text+image+sound). A total of 30 users participated in using NewsRobot, completing surveys and interviews on their experience. Our findings are as follows: (1) Users preferred individualized news yet considered it less credible, (2) more presentation elements were appreciated but only if their quality was assured, and (3) NewsRobot was considered factual and accurate yet shallow in depth. Based on our findings, we discuss implications for designing automated journalism user interfaces.

We investigated the similarities and differences among four addiction groups in Korean adolescents: Non-Addiction (NONE), Smartphone Addiction (SA), Internet Addiction (IA), and Internet-Smartphone Addiction (BOTH). For the dependent variables, we examined 12 addiction-risk factors related to psychology, family, and school environment that can influence the adolescents’ normative developmental path. To collect data, we conducted an addiction-risk factor comparison survey with 768 Korean adolescents in their first year of junior high school. Depending on the addiction groups, a multivariate analysis of variance or Tukey HSD post-hoc test was used to analyze statistical differences among the 12 addiction-risk factors. Our analysis yielded two key findings on how Internet addiction and smartphone addiction differ in terms of addiction-risk factors: (a) there were more similarities between the SA and NONE groups than between the IA and NONE groups, and (b) there were more similarities between the IA and BOTH groups than between the SA and BOTH groups.

Neural network based models for collaborative filtering have started to gain attention recently. One branch of research is based on using deep generative models to model user preferences where variational autoencoders were shown to produce state-of-the-art results. However, there are some potentially problematic characteristics of the current variational autoencoder for CF. The first is the too simplistic prior that VAEs incorporate for learning the latent representations of user preference. The other is the model's inability to learn deeper representations with more than one hidden layer for each network.

Recent advances in artificial intelligence (AI) have increased the opportunities for users to interact with the technology. Now, users can even collaborate with AI in creative activities such as art. To understand the user experience in this new user--AI collaboration, we designed a prototype, DuetDraw, an AI interface that allows users and the AI agent to draw pictures collaboratively. We conducted a user study employing both quantitative and qualitative methods. Thirty participants performed a series of drawing tasks with the think-aloud method, followed by post-hoc surveys and interviews. Our findings are as follows: (1) Users were significantly more content with DuetDraw when the tool gave detailed instructions. (2) While users always wanted to lead the task, they also wanted the AI to explain its intentions but only when the users wanted it to do so. (3) Although users rated the AI relatively low in predictability, controllability, and comprehensibility, they enjoyed their interactions with it during the task. Based on these findings, we discuss implications for user interfaces where users can collaborate with AI in creative works.

Stock market prediction was once considered to be infeasible. Recent studies on using text contents of information reporting platforms has opened up new ways of analyzing the stock market with machine learning. we propose using the Securities and Exchange Committee (SEC) mandated 10-Q form as a possible source of data for stock predictions. Using the 10-Q reports of S&P 500 companies, we create our corpus by extracting bag-of-words (BOW) of any additions made to the 10-Q documents. Then, we create feed-forward multilayer neural network on stock price ratios of different target prediction periods and achieve positive prediction rates. We demonstrate that text contents of 10-Q form may have information value to stock price prediction models.

In this paper, we present Touch+Finger, a new interaction technique that augments touch input with multi-finger gestures for rich and expressive interaction. The main idea is that while one finger is engaged in a touch event, a user can leverage the remaining fingers, the "idle" fingers, to perform a variety of hand poses or in-air gestures to extend touch-based user interface capabilities. To fully understand the use of these idle fingers, we constructed a design space based on conventional touch gestures (i.e., single- and multi-touch gestures) and inter- action period (i.e., before and during touch). Considering the design space, we investigated the possible movement of the idle fingers and developed a total of 20 Touch+Finger gestures. Using ring-like devices to track the motion of the idle fingers in the air, we evaluated the Touch+Finger gestures on both recognition accuracy and ease of use. They were classified with a recognition accuracy of over 99% and received positive and negative comments from 8 participants. We suggested 8 interaction techniques with Touch+Finger gestures that demonstrate extended touch-based user interface capabilities.

Various forms of artificial intelligence (AI), such as Apple's Siri and Google Now, have permeated our everyday lives. However, the advent of such "human-like" technology has stirred both awe and a great deal of fear. Many consider it a woe to have an unimaginable future where human intelligence is exceeded by AI. This paper investigates how people perceive and understand AI with a case study of the Google DeepMind Challenge Match, a Go match between Lee Sedol and AlphaGo, in March 2016. This study explores the underlying and changing perspectives toward AI as users experienced this historic event. Interviews with 22 participants show that users tacitly refer to AlphaGo as an "other" as if it were comparable to a human, while dreading that it would come back to them as a potential existential threat. Our work illustrates a confrontational relationship between users and AI, and suggests the need to prepare for a new kind of user experience in this nascent socio- technological change. It calls for a collaborative research effort from the HCI community to study and accommodate users for a future where they interact with algorithms, not just interfaces.

The widespread use of mobile devices has transformed casual places into meeting places. However, in these places, it is uncommon to have shared information workspace such as a beam projector, making it inconvenient and inefficient to exchange information and to get a direct feedback. To address this challenge, we present Ubi-jector, a mobile system that provides a shared information space that is equally distributed to each participant's mobile device and allows group members to share documents and collaborate real-time. We first characterized the information sharing patterns and identified the limitations of the current practice in meeting places without a shared workspace, by conducting qualitative user studies. Next, we implemented Ubi-jector with the design guidelines drawn in the prior stage. Also, we performed an evaluation study that showed the possibilities of Ubi-jector to facilitate an effective information sharing and foster an active participation even in poorly-equipped environments.

The use of car navigation system is very common nowadays. Most of the car navigation services are based on turn-by-turn instructions and distance calculations. Academic research in this field has focused on evaluating basic usability. However, such products and studies have not covered users' various needs that arise in specific driving situations. For example, in a complex city space, drivers often face burdensome problems, especially when picking up pedestrians. We conducted a semistructured online survey asking specific problems, work-arounds, and their suggestions in picking-up situations. We grouped responses into several issue points based on their similarities and induced design implications for a car navigation system supporting picking-up situations. Through this user-centered design approach, we developed "Gravity - Automatic Location Tracking System between a Car and a Pedestrian" as a prototype and evaluated its usability, and we received favorable feedback.

Little research exists on one of the most common, oldest, and most utilized forms of online social geographic information: the 'location' field found in most virtual community user profiles. We performed the first in-depth study of user behavior with regard to the location field in Twitter user profiles. We found that 34% of users did not provide real location information, frequently incorporating fake locations or sarcastic comments that can fool traditional geographic information tools. When users did input their location, they almost never specified it at a scale any more detailed than their city. In order to determine whether or not natural user behaviors have a real effect on the 'locatability' of users, we performed a simple machine learning experiment to determine whether we can identify a user's location by only looking at what that user tweets. We found that a user's country and state can in fact be determined easily with decent accuracy, indicating that users implicitly reveal location information, with or without realizing it. Implications for location-based services and privacy are discussed.

This paper presents a research project on the design of a cross-channel information management tool for knowledge workers: we focus on IT services professionals in a large enterprise who work in multiple ad hoc task forces. Through three rounds of investigation, we characterized their work practices and needs, specified their requirements for a cross-channel information management tool, and designed and evaluated a prototype to address these needs. We found that these workers shared the problem of managing information across multiple channels, requiring better support for aggregating, filtering, and organizing this information. We report the requirements elicited and the prototypes built during the design process.

Information overload is a growing threat to the productivity of today's knowledge workers, who need to keep track of multiple streams of information from various sources. RSS feed readers are a popular choice for syndicating information streams, but current tools tend to contribute to the overload problem instead of solving it. We introduce FeedWinnower, an enhanced feed aggregator that helps readers to filter feed items by four facets (topic, people, source, and time), thus facilitating feed triage. The combination of the four facets provides a powerful way for users to slice and dice their personal feeds. In addition, we present a formative evaluation of the prototype conducted with 15 knowledge workers in two different organizations.

Twitter streams are on overload: active users receive hundreds of items per day, and existing interfaces force us to march through a chronologically-ordered morass to find tweets of interest. We present an approach to organizing a user's own feed into coherently clustered trending topics for more directed exploration. Our Twitter client, called Eddi, groups tweets in a user's feed into topics mentioned explicitly or implicitly, which users can then browse for items of interest. To implement this topic clustering, we have developed a novel algorithm for discovering topics in short status updates powered by linguistic syntactic transformation and callouts to a search engine. An algorithm evaluation reveals that search engine callouts outperform other approaches when they employ simple syntactic transformation and backoff strategies. Active Twitter users evaluated Eddi and found it to be a more efficient and enjoyable way to browse an overwhelming status update feed than the standard chronological interface.

Wikipedia is an online encyclopedia which has undergone tremendous growth. However, this same growth has made it difficult to characterize its content and coverage. In this paper we develop measures to map Wikipedia using its socially annotated, hierarchical category structure. We introduce a mapping technique that takes advantage of socially-annotated hierarchical categories while dealing with the inconsistencies and noise inherent in the distributed way that they are generated. The technique is demonstrated through two applications: mapping the distribution of topics in Wikipedia and how they have changed over time; and mapping the degree of conflict found in each topic area. We also discuss the utility of the approach for other applications and datasets involving collaboratively annotated category hierarchies.

An experiment was conducted to study how credibility judgments about Wikipedia are affected by providing users with an interactive visualization (WikiDashboard) of article and author editing history. Overall, users who self-reported higher use of Internet information and higher rates of Wikipedia usage tended to produce lower credibility judgments about Wikipedia articles and authors. However, use of WikiDashboard significantly increased article and author credibility judgments, with effect sizes larger than any other measured effects of background media usage and attitudes on Wikiepedia credibility. The results suggest that increased exposure to the editing/authoring histories of Wikipedia increases credibility judgments.

User studies are important for many aspects of the design process and involve techniques ranging from informal surveys to rigorous laboratory studies. However, the costs involved in engaging users often requires practitioners to trade off between sample size, time requirements, and monetary costs. Micro-task markets, such as Amazon's Mechanical Turk, offer a potential paradigm for engaging a large number of users for low time and monetary costs. Here we investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks. Although micro-task markets have great potential for rapidly collecting user measurements at low costs, we found that special care is needed in formulating tasks in order to harness the capabilities of the approach.

Wikis are collaborative systems in which virtually anyone can edit anything. Although wikis have become highly popular in many domains, their mutable nature often leads them to be distrusted as a reliable source of information. Here we describe a social dynamic analysis tool called WikiDashboard which aims to improve social transparency and accountability on Wikipedia articles. Early reactions from users suggest that the increased transparency afforded by the tool can improve the interpretation, communication, and trustworthiness of Wikipedia articles.

Wikipedia has become one of the most important information resources on the Web by promoting peer collaboration and enabling virtually anyone to edit anything. However, this mutability also leads many to distrust it as a reliable source of information. Although there have been many attempts at developing metrics to help users judge the trustworthiness of content, it is unknown how much impact such measures can have on a system that is perceived as inherently unstable. Here we examine whether a visualization that exposes hidden article information can impact readers' perceptions of trustworthiness in a wiki environment. Our results suggest that surfacing information relevant to the stability of the article and the patterns of editor behavior can have a significant impact on users' trust across a variety of page types.

Wikipedia, a wiki-based encyclopedia, has become one of the most successful experiments in collaborative knowledge building on the Internet. As Wikipedia continues to grow, the potential for conflict and the need for coordination increase as well. This article examines the growth of such non-direct work and describes the development of tools to characterize conflict and coordination costs in Wikipedia. The results may inform the design of new collaborative knowledge systems.

The vision of ubiquitous digital photos has arrived. Yet, despite their broad popularity, significant shortfalls remain in the tools used to manage them. We believe that with a bit more creativity and effort, the photo industry can solve many of these problems, offering tools which better support accurate, rapid, and safe shared annotations with comfortable and efficient browsing and search. In this article, we review a number of projects of ours and others on interfaces for photo management. We describe the problems that we see in existing tools and our vision for improving them.

Managing a large number of digital photos is a challenging task for casual users. Personal photos often don’t have rich metadata, or additional information associated with them. However, available metadata can play a crucial role in managing photos. Labeling the semantic content of photos (i.e., annotating them), can increase the amount of metadata and facilitate efficient management. However, manual annotation is tedious and labor intensive while automatic metadata extraction techniques often generate inaccurate and irrelevant results. This paper describes a semi-automatic annotation strategy that takes advantage of human and computer strengths. The semi-automatic approach enables users to efficiently update automatically obtained metadata interactively and incrementally. Even though automatically identified metadata are compromised with inaccurate recognition errors, the process of correcting inaccurate information can be faster and easier than manually adding new metadata from scratch. In this paper, we introduce two photo clustering algorithms for generating meaningful photo groups: (1) Hierarchical event clustering; and (2) Clothing based person recognition, which assumes that people who wear similar clothing and appear in photos taken in one day are very likely to be the same person. To explore our semi-automatic strategies, we designed and implemented a prototype called SAPHARI (Semi-Automatic PHoto Annotation and Recognition Interface). The prototype provides an annotation framework which focuses on making bulk annotations on automatically identified photo groups. The prototype automatically creates photo clusters based on events, people, and file metadata so that users can easily bulk annotation photos. We performed a series of user studies to investigate the effectiveness and usability of the semi-automatic annotation techniques when applied to personal photo collections. The results show that users were able to make annotations significantly faster with event clustering using SAPHARI. We also found that users clearly preferred the semi-automatic approaches.

Thumbnail images provide users of image retrieval and browsing systems with a method for quickly scanning large numbers of images. Recognizing the objects in an image is important in many retrieval tasks, but thumbnails generated by shrinking the original image often render objects illegible. We study the ability of computer vision systems to detect key components of images so that automated cropping, prior to shrinking, can render objects more recognizable. We evaluate automatic cropping techniques 1) based on a general method that detects salient portions of images, and 2) based on automatic face detection. Our user study shows that these methods result in small thumbnails that are substantially more recognizable and easier to find in the context of visual search.

We present OZONE (Zoomable Ontology Navigator), for searching and browsing ontological information. OZONE visualizes query conditions and provides interactive, guided browsing for DAML (DARPA Agent Markup Language) ontologies on the Web. To visually represent objects in DAML, we define a visual model for its classes, properties and relationships between them. Properties can be expanded into classes for query refinement. The visual query can be formulated incrementally as users explore class and property structures interactively. Zoomable interface techniques are employed for effective navigation and usability.

We present an overview+detail document interface that draws on perceptual principles to help users work with documents. Central to our approach is the use of improved document overviews. Our approach also includes novel highlighting in the full representation of documents, as well as techniques to help users smoothly transition from the overview to the full representation of the document. We present a specific implementation of our design for Web browsing. We also present a qualitative user study that indicates that our perceptual design principles are effective and that users prefer our interface to traditional "find" and highlighting techniques. Our user study additionally reveals interesting tasks and strategies supported in our framework that have implications for overview+detail document interfaces in general