As users’ social media feeds have become increasingly driven by algorithmically recommended content, there is a need to understand the impact these recommendations have on users. People in recovery from eating disorders (ED) may try to avoid content that features severely underweight bodies or that encourages disordered eating. However, if recommender systems show them this type of content anyway, it may impact their recovery or even lead to relapse. In this study, we take a two-pronged approach to understanding the intersection of recommender systems, ED content, and users in recovery. We performed a content analysis of tweets about recommended ED content and conducted a small-scale study on Pinterest to show that ED content is recommended in response to interaction with posts about ED recovery. We discuss the implications for responsible recommendation and harm prevention.

Abstract Due to their confidence and dominance, narcissistic leaders oftentimes can be perceived favorably by followers, in particular during times of uncertainty. In this study, we propose and examine the relationship between narcissistic leaders and followers who are prone to experience uncertainty intensely and frequently in general, namely highly anxious followers. We do so by applying machine learning algorithms to account for personality traits in a large sample of leaders and followers on Twitter. We find that highly anxious followers are more likely to interact with narcissistic leaders in general, and male narcissistic leaders in particular. Finally, we also examined these interactions in the context of highly popular leaders and found that as leaders become more popular, they begin to attract less anxious followers, regardless of leader gender. We interpret and discuss these findings in relation to previous work and outline limitations and future research recommendations based on our approach.

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we present a structured and linguistic approach using hinge-loss Markov random fields (HL-MRFs) to understand recovery and relapse from AUD using social media data. We evaluate our models on AA-attending users extracted from: (i) the Twitter social network and predict recovery at two different points—90 days and 1 year after the user joins AA, respectively, and (ii) the Reddit AA recovery forums and predict whether the participating user is currently sober. The two datasets present two facets of the same underlying problem of understanding recovery and relapse in AUD users. We flesh out different characteristics in both these datasets: (i) In the Twitter dataset, we focus on the social aspect of the users and the relationship with recovery and relapse, and (ii) in the Reddit dataset, we focus on modeling the linguistic topics and dependency structure to understand users’ recovery journey. We design a unified modeling framework using HL-MRFs that takes the different characteristics of both these platforms into account. Our experiments reveal that our structured and linguistic approach is helpful in predicting recovery in users in both these datasets. We perform extensive quantitative analysis of different groups of features and dependencies among them in both datasets. The interpretable and intuitive nature of our models and analysis is helpful in making meaningful predictions and can potentially be helpful in identifying and preventing relapse early.

Twitter continues to be used increasingly for communication related advocacy, activism, and social change. This is also the case for the disability community. In light of the recently proposed ADA Education and Reform in the United States, we investigate factors for effectiveness of sharing or retweeting messages about topics affecting the rights of people with disabilities. We perform a multifaceted study of the #HandsOffMyADA campaign against the proposed H.R.620 bill to: (1) explore how communication via Twitter compares to previous disability rights movements; (2) characterize the campaign in terms of hashtags, user groups, and content such as accessible multimedia that contribute to dissemination of campaign messages; (3) identify major themes in tweets and responses, and their variation among user groups; and (4) understand how the disability community mobilized for this campaign compared to previous Twitter initiatives.

This paper presents an observational study of lexical propagation across online social networking platforms. By focusing on the highly followed @dog_rates Twitter account, we explore how a popular account's unique style of language propagates outside of the account's immediate follower community within Twitter. Initial results show a strong relationship between the prevalence of this account's language-specific features and the account's followership and popularity. Expanding this research across platforms, we demonstrate consistency in these results outside Twitter, as the @dog_rates vernacular shows a similarly strong relationship between use on Reddit and the account's followership over time.

Personalization, recommendations, and user modeling can be powerful tools to improve people's experiences with technology and to help them find information. However, we also know that people underestimate how much of their personal information is used by our technology and they generally do not understand how much algorithms can discover about them. Both privacy and ethical technology have issues of consent at their heart. While many personalization systems assume most users would consent to the way they employ personal data, research shows this is not necessarily the case. This talk will look at how to consider issues of privacy and consent when users cannot explicitly state their preferences, The Creepy Factor, and how to balance users' concerns with the benefits personalized technology can offer.

Alcoholism, also known as Alcohol Use Disorder (AUD), is a serious problem affecting millions of people worldwide. Recovery from AUD is known to be challenging and often leads to relapse at various points after enrolling in a rehabilitation program such as Alcoholics Anonymous (AA). In this work, we take a structured approach to understand recovery and relapse from AUD using social media data. To do so, we combine linguistic and psychological attributes of users with relational features that capture useful structure in the user interaction network. We evaluate our models on AA-attending users extracted from the Twitter social network and predict recovery at two different points---90 days and 1 year after the user joins AA, respectively. Our experiments reveal that our structured approach is helpful in predicting recovery in these users. We perform extensive quantitative analysis of different groups of features and dependencies among them. Our analysis sheds light on the role of each feature group and how they combine to predict recovery and relapse. Finally, we present a qualitative analysis of the different reasons behind users relapsing to AUD. Our models and analysis are helpful in making meaningful predictions in scenarios where only a subset of features are available and can potentially be helpful in identifying and preventing relapse early.

The microblogging platform, Twitter, has been an extremely valuable tool for politicians in sharing information, fostering broader communication to constituents, and promoting their political stances. This article follows up on previous research from 2009 on this subject. We reexamined tweets from the US Congress collected in early 2017. We found Congressional tweeting habits and content have changed very little in the last 8 years. Overall, they tended to use Twitter to pass along political information and links in addition to reporting on official and unofficial activities and meetings. We discuss future spaces for research that go beyond content analysis into issues of motivation, communication, and impact.

Personalization, recommendations, and user modeling can be pow- erful tools to improve people?s experiences with technology and to help them nd information. However, we also know that people underestimate how much of their personal information is used by our technology and they generally do not understand how much algorithms can discover about them. Both privacy and ethical tech- nology have issues of consent at their heart. This talk will look at how to consider issues of privacy and consent when users cannot explicitly state their preferences, The Creepy Factor, and how to balance users? concerns with the bene ts personalized technology can o er.

This paper introduces a general technique, called LABurst, for identifying key moments, or moments of high impact, in social media streams without the need for domain-specific information or seed keywords. We leverage machine learning to model temporal patterns around bursts in Twitter's unfiltered public sample stream and build a classifier to identify tokens experiencing these bursts. We show LABurst performs competitively with existing burst detection techniques while simultaneously providing insight into and detection of unanticipated moments. To demonstrate our approach's potential, we compare two baseline event-detection algorithms with our language-agnostic algorithm to detect key moments across three major sporting competitions: 2013 World Series, 2014 Super Bowl, and 2014 World Cup. Our results show LABurst outperforms a time series analysis baseline and is competitive with a domain-specific baseline even though we operate without any domain knowledge. We then go further by transferring LABurst's models learned in the sports domain to the task of identifying earthquakes in Japan and show our method detects large spikes in earthquake-related tokens within two minutes of the actual event.

Social recommendation takes advantage of the influence of social relationships in decision making and the ready availability of social data through social networking systems. Trust relationships in particular can be exploited in such systems for rating prediction and recommendation, which has been shown to have the potential for improving the quality of the recommender and alleviating the issue of data sparsity, cold start, and adversarial attacks. An appropriate trust inference mechanism is necessary in extending the knowledge base of trust opinions and tackling the issue of limited trust information due to connection sparsity of social networks. In this work, we offer a new solution to trust inference in social networks to provide a better knowledge base for trust-aware recommender systems. We propose using a semiring framework as a nonlinear way to combine trust evidences for inferring trust, where trust relationship is model as 2-D vector containing both trust and certainty information. The trust propagation and aggregation rules, as the building blocks of our trust inference scheme, are based upon the properties of trust relationships. In our approach, both trust and distrust (i.e., positive and negative trust) are considered, and opinion conflict resolution is supported. We evaluate the proposed approach on real-world datasets, and show that our trust inference framework has high accuracy, and is capable of handling trust relationship in large networks. The inferred trust relationships can enlarge the knowledge base for trust information and improve the quality of trust-aware recommendation.

Interruptions can have a significant impact on users working to complete a task. When people are collaborating, either with other users or with systems, coordinating interruptions is an important factor in maintaining efficiency and preventing information overload. Computer systems can observe user behavior, model it, and use this to optimize the interruptions to minimize disruption. However, current techniques often require long training periods that make them unsuitable for online collaborative environments where new users frequently participate.

There are many situations in which two or more agents (e.g., human or computer decision makers) interact with each other repeatedly in settings that can be modeled as repeated stochastic games. In such situations, each agent's performance may depend greatly on how well it can predict the other agents' preferences and behavior. For use in making such predictions, we adapt and extend the Social Value Orientation (SVO) model from social psychology, which provides a way to measure an agent's preferences for both its own payoffs and those of the other agents.

In recent years, cultural heritage institutions have increasingly used social tagging. To better understand the nature of these tags, we analyzed tags assigned to a collection of 100 images of art (provided by the steve.museum project) using subject matter categorization. Our results show that the majority of tags describe the people and objects in the image and are generic in nature. This contradicts prior subject matter analyses of queries, tags, and index terms of other image collections, suggesting that the nature of social tags largely depends on the type of collection and on user needs. This insight may help cultural heritage institutions improve their management and use of tags.

Personality is an important factor that influences people's decisions, actions, and tastes. While previous research has used surveys to establish a connection between personality and media preferences, to date there has been no research that connects these attributes to users' opinions of and use of recommender systems nor to their movie rating and viewing histories. In this paper, we present our results on the relationship between personality and users' movie preferences, and their opinions about, use of, and trust in recommender systems. Using surveys and analysis of system data for 73 Netflix users, we show correlations between personality and preferences for specific movie genres that replicate and extend previous results. Our most significant result is that the personality trait of Conscientious is consistently positively correlated with a higher opinion about the usefulness and trustworthiness of recommendations, self-reports of how frequently they were used, and ratings of recommended items. We discuss the implications these results have for evaluating and improving recommender systems.

Many people have pets such as dogs and cats that they would consider to be family. Along with this comes a need to stay aware of one's pet and, possibly, interact with it when away from home. There has even been a recent push by companies to create video-mediated communication (VMC) systems to connect pet owners and pets over distance. Yet the problem is that we do not know how such systems should be designed to meet the real needs of pet owners. To investigate this, we conducted a survey with dog and cat owners that explores their needs for remotely monitoring and interacting with their pets. Our results show that many family members would value being able to maintain an awareness of their pets and interact with them over distance using VMC systems. Such systems would be particularly valuable when pet owners are away from home for extended time periods. However, VMC systems for pets must be designed cautiously to avoid issues of owner disembodiment and other ethical challenges.

Visual overviews of tables of numerical and categorical data have been proposed for tables with a single value per cell. This article addresses the problem of exploring tables with columns that consist of cells that are distributions, for example, the distributions of movie ratings or trust ratings in recommender systems, age distributions in demographic data, usage distributions in logs of telephone calls, and so on. This article expands on heatmap approaches and proposes a novel way of displaying and interacting with distribution data. The usability study demonstrates the benefits of the heatmap interface in providing an overview of the data and facilitating the discovery of interesting clusters, patterns, outliers and relationships between columns.

In this paper, we examine to which degree behavioral measures can be used to predict personality. Personality is one factor that dictates people's propensity to trust and their relationships with others. In previous work, we have shown that personality can be predicted relatively accurately by analyzing social media profiles. We demonstrated this using public data from facebook profiles and text from Twitter streams. As social situations are crucial in the formation of one's personality, one's social behavior could be a strong indicator of her personality. Given most users of social media sites typically have a large number of friends and followers, considering only these aspects may not provide an accurate picture of personality. To overcome this problem, we develop a set of measures based on one's behavior towards her friends and followers. We introduce a number of measures that are based on the intensity and number of social interactions one has with friends along a number of dimensions such as reciprocity and priority. We analyze these features along with a set of features based on the textual analysis of the messages sent by the users. We show that behavioral features are very useful in determining personality and perform as well as textual features.

The microblogging service Twitter has become an important, and sometimes primary, source of information for many users. As a forum for sharing news and discussing events, it can provide instant access to the latest updates, but this is not always welcome. In the case of television shows or live sporting events, for example, tweets about them may reveal spoilers to users in different time zones or who are delaying their viewing until later. More broadly, because Twitter is a broadcast medium, users may often want to temporarily or permanently hide content about a very specific given topic.

The goal of this study is to compare social tagging patterns in two languages in image collections of art, while seeking exploitable strengths for the application of multilingual social tagging in digital libraries and museums. Crowdsourcing the annotation of digital image collections of artworks to different language communities has the potential to bridge language borders and reach wider audiences. This mixed methods study is based on a collection of digital images of paintings for which tags in Spanish and English were collected. The results show that the level of agreement in the vocabulary describing an image does not change significantly when adding a second language, but different cultural perspectives can be found for certain images when comparing less frequent tags across languages. Understanding and comparing tagging behaviors across languages is necessary for the design of user interfaces that support diversity and encourage sharing of perspectives about the artwork images.

This work provides a game theoretic framework through which one can study the different trust and mitigation strategies a decision maker can employ when soliciting advice or input from a potentially self-interested third-party. The framework supports a single decision maker's interacting with an arbitrary number of either honest or malicious (and malicious in varying ways) advisors. We include some preliminary results on the analysis of this framework in some constrained instances and propose several avenues of future work.

Users are able to arbitrarily make assertions about themselves online. In many spaces, it is valuable to both the users and information consumers that those statements can be validated and trusted. In this paper, we present the Publish Trust Framework. This leverages Semantic Web technologies to add provenance to the attributes a person wants to assert about themselves. That connects the statements back to their sources which are rated according to their trustworthiness. We discuss the structure of the framework, describe a pilot deployment, and present future directions for this research.

There is great interest in understanding media bias and political information seeking preferences. As many media outlets create online personas, we seek to automatically estimate the political preferences of their audience, rather than of the outlet itself. In this paper, we present a novel method for computing preference among an organization's Twitter followers. We present an application of this technique to estimate political preference of the audiences of U.S. media outlets. We also discuss how these results may be used and extended.

Social tags have become an important tool for improving access to online resources, particularly non-text media. With the dramatic growth of user-generated content, the importance of tags is likely to grow. However, while tagging behavior is well studied, the relationship between tagging behavior and features of the media being tagged is not well understood. In this paper, we examine the relationship between tagging behavior and image type. Through a lab-based study with 51 subjects and an analysis of an online dataset of image tags, we show that there are significant differences in the number, order, and type of tags that users assign based on their past experience with an image, the type of image being tagged, and other image features. We present these results and discuss the significant implications this work has for tag-based search algorithms, tag recommendation systems, and other interface issues.

Traditional network analysis tools support analysts in studying a single network. ManyNets offers these analysts a powerful new approach that enables them to work on multiple networks simultaneously. Several thousand networks can be presented as rows in a tabular visualization, and then inspected, sorted and filtered according to their attributes. The networks to be displayed can be obtained by subdivision of larger networks. Examples of meaningful subdivisions used by analysts include ego networks, community extraction, and time-based slices. Cell visualizations and interactive column overviews allow analysts to assess the distribution of attributes within particular sets of networks. Details, such as traditional node-link diagrams, are available on demand. We describe a case study analyzing a social network geared towards film recommendations by means of decomposition. A small usability study provides feedback on the use of the interface on a set of tasks issued from the case study.

Collection recommender systems suggest groups of items that work well as a whole. The interaction effects between items is an important consideration, but the vast space of possible collections makes it difficult to analyze. In this paper, we present a class of games with a purpose for building collections where users create collections and, using an output agreement model, they are awarded points based on the collections that match. The data from these games will help researchers develop guidelines for collection recommender systems among other applications. We conducted a pilot study of the game prototype which indicated that it was fun and challenging for users, and that the data obtained had the characteristics necessary to gain insights into the interaction effects among items. We present the game and these results followed by a discussion of the next steps necessary to bring games to bear on the problem of creating harmonious groups.

Abstract Twitter is a microblogging and social networking service with millions of members and growing at a tremendous rate. With the buzz surrounding the service have come claims of its ability to transform the way people interact and share information and calls for public figures to start using the service. In this study, we are interested in the type of content that legislators are posting to the service, particularly by members of the United States Congress. We read and analyzed the content of over 6,000 posts from all members of Congress using the site. Our analysis shows that Congresspeople are primarily using Twitter to disperse information, particularly links to news articles about themselves and to their blog posts, and to report on their daily activities. These tend not to provide new insights into government or the legislative process or to improve transparency; rather, they are vehicles for self‐promotion. However, Twitter is also facilitating direct communication between Congresspeople and citizens, though this is a less popular activity. We report on our findings and analysis and discuss other uses of Twitter for legislators.

In this article, we describe a new approach that gives an explicit probabilistic interpretation for social networks. In particular, we focus on the observation that many existing Web-based trust-inference algorithms conflate the notions of “trust” and “confidence,” and treat the amalgamation of the two concepts to compute the trust value associated with a social relationship. Unfortunately, the result of such an algorithm that merges trust and confidence is not a trust value, but rather a new variable in the inference process. Thus, it is hard to evaluate the outputs of such an algorithm in the context of trust inference. This article first describes a formal probabilistic network model for social networks that allows us to address that issue. Then we describe SUNNY, a new trust inference algorithm that uses probabilistic sampling to separately estimate trust information and our confidence in the trust estimate and use the two values in order to compute an estimate of trust based on only those information sources with the highest confidence estimates. We present an experimental evaluation of SUNNY. In our experiments, SUNNY produced more accurate trust estimates than the well-known trust inference algorithm TidalTrust, demonstrating its effectiveness. Finally, we discuss the implications these results will have on systems designed for personalizing content and making recommendations.

Recommender systems traditionally recommend individual items. We introduce the idea of collection recommender systems and describe a design space for them including 3 main aspects that contribute to the overall value of a collection: the value of the individual items, co-occurrence interaction effects, and order effects including placement and arrangement of items. We then describe an empirical study examining how people create mix tapes. The study found qualitative and quantitative evidence for order effects (e.g., first songs are rated higher than later songs; some songs go poorly together sequentially). We propose several ideas for research in this space, hoping to start a much longer conversation on collection recommender systems.

As the Web has shifted to an interactive environment where vast amounts of content is created by users, the question of whom to trust and what information to trust has become both more important and more difficult to answer. At the same time, social networks have become very popular with over a billion accounts shared across hundreds of networks. Social trust relationships, derived from social networks, are uniquely suited to speak to the quality of online information; recommender systems are designed to personalize, sort, aggregate, and highlight information. Merging social networks, trust, and recommender systems can improve the accuracy of recommendations and improve the user’s experience. In this tutorial, we will cover the use of social trust in recommender systems. Topics including the computation of trust in social networks, integration of trust into recommender systems, and a discussion of when trust offers benefits and the challenges it presents.

The World Wide Web has transformed into an environment where users both produce and consume information. In order to judge the validity of information, it is important to know how trustworthy its creator is. Since no individual can have direct knowledge of more than a small fraction of information authors, methods for inferring trust are needed. We propose a new trust inference scheme based on the idea that a trust network can be viewed as a random graph, and a chain of trust as a path in that graph. In addition to having an intuitive interpretation, our algorithm has several advantages, noteworthy among which is the creation of an inferred trust-metric space where the shorter the distance between two people, the higher their trust. Metric spaces have rigorous algorithms for clustering, visualization, and related problems, any of which is directly applicable to our results.

Interest in web-based syndication systems has been growing as information streams onto the web at an increasing rate. Technologies, like the standard Semantic Web languages RDF and OWL, make it possible to create expressive representations of the content of publications and subscriptions in a syndication framework. Because these languages are based in description logics, this representation allows the application to reasoning to make more precise matching of user interests with published information. A challenge to this approach is that the consistency of the underlying knowledge base must be maintained for these techniques to work. With the frequent addition of information from new publications, it is likely that inconsistencies will arise. There are many potential mechanisms for choosing which inconsistent information to discard from the KB to regain consistency; in the case of news syndication, we argue keeping the most trusted information is important for generating the most valuable matches. Thus, in this article, we present algorithms for belief-base revision, and specifically look at the user's trust in the information sources as a metric for deciding what to keep in the KB and what to remove.

Online social networks, where users maintain lists of friends and express their preferences for items like movies, music, or books, are very popular. The Web-based nature of this information makes it ideal for use in a variety of intelligent systems that can take advantage of the users' social and personal data. For those systems to be effective, however, it is important to understand the relationship between social and personal preferences. In this work we investigate features of profile similarity and how those relate to the way users determine trust. Through a controlled study, we isolate several profile features beyond overall similarity that affect how much subjects trust hypothetical users. We then use data from FilmTrust, a real social network where users rate movies, and show that the profile features discovered in the experiment allow us to more accurately predict trust than when using only overall similarity. In this article, we present these experimental results and discuss the potential implications for using trust in user interfaces.

Abstract Provenance is critically important for scientific workflow systems, as it allows users to verify data, repeat experiments, and discover dependencies. The Semantic Web is a natural fit for representing provenance, as it contains explicit support for representing and inferring connections between data and processes, as well as for adding annotations to data. In this article, we present a Semantic Web approach to the Provenance Challenge (Concurrency Computat.: Pract. Exper. 2007; DOI: 10.1002/cpe.1233). We use web services, ontologies, OWL reasoners, triple stores, and the SPARQL query language to implement the workflow, represent the data and the connections within it, and execute queries. We successfully implemented and answered all of the challenge queries. The flexibility of the Semantic Web also makes it quite easy to convert different provenance systems' data representation to a form we can work with. We illustrate this by integrating data from the PASS approach into our system, and successfully executing all of the challenge queries on it as well. Copyright © 2007 John Wiley & Sons, Ltd.

Abstract The first Provenance Challenge was set up in order to provide a forum for the community to understand the capabilities of different provenance systems and the expressiveness of their provenance representations. To this end, a functional magnetic resonance imaging workflow was defined, which participants had to either simulate or run in order to produce some provenance representation, from which a set of identified queries had to be implemented and executed. Sixteen teams responded to the challenge, and submitted their inputs. In this paper, we present the challenge workflow and queries, and summarize the participants' contributions. Copyright © 2007 John Wiley & Sons, Ltd.

FilmTrust is a website that integrates social networks with movie ratings and reviews. Using FOAF-based social networks augmented with trust ratings, the site computes predictive movie ratings based on the ratings of trusted people in the network. Preliminary results show these results to be significantly more accurate than other predictive ratings in certain situations. This demo will show the FilmTrust website, demonstrate cases where the predictive movie ratings are successful, and illustrate its RDF and OWL output for those interested in the backend.

In this paper, we present FilmTrust, a website that integrates Semantic Web-based social networks, augmented with trust, to create predictive movie recommendations. We show how these recommendations are more accurate than other techniques in certain cases, and discuss this technique as a mechanism of Semantic Web interaction.

The growth of Web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences. Our research looks particularly at assigning trust in Web-based social networks and investigates how trust information can be mined and integrated into applications. This article introduces a definition of trust suitable for use in Web-based social networks with a discussion of the properties that will influence its use in computation. We then present two algorithms for inferring trust relationships between individuals that are not directly connected in the network. Both algorithms are shown theoretically and through simulation to produce calculated trust values that are highly accurate.. We then present TrustMail, a prototype email client that uses variations on these algorithms to score email messages in the user's inbox based on the user's participation and ratings in a trust network.

The National Cancer Institute has developed the NCI Thesaurus, a biomedical vocabulary for cancer research, covering terminology across a wide range of cancer research domains. A major design goal of the NCI Thesaurus is to facilitate translational research. We describe: the features of Ontylog, a description logic used to build NCI Thesaurus; our methodology for enhancing the terminology through collaboration between ontologists and domain experts, and for addressing certain real world challenges arising in modeling the Thesaurus; and finally, we describe the conversion of NCI Thesaurus from Ontylog into Web Ontology Language Lite. Ontylog has proven well suited for constructing big biomedical vocabularies. We have capitalized on the Ontylog constructs Kind and Role in the collaboration process described in this paper to facilitate communication between ontologists and domain experts. The artifacts and processes developed by NCI for collaboration may be useful in other biomedical terminology development efforts.

The SlideBar is a physical linear input device for absolute position control of 1° of freedom, consisting of a physical slider with a graspable knob positioned near or attached to the keyboard. Its range of motion is directly mapped to a one dimensional input widget such as a scrollbar. The SlideBar provides absolute position control in one dimension, is usable in the non-dominant hand in conjunction with a pointing device, and offers constrained passive haptic feedback. These characteristics make the device appropriate for the common class of tasks characterized by one-dimensional input and constrained range of operation. An empirical study of three devices (SlideBar, mouse controlled scrollbar, and mousewheel) shows that for common scrolling tasks, the SlideBar has a significant advantage over a standard mouse controlled scrollbar in user preference. In addition, users tended to prefer it over the mousewheel (without statistical significance).

Implicit information embedded in semantic Web graphs, such as topography, clusters, and disconnected subgraphs is difficult to extract from text files. Visualizations of the graphs can reveal some of these features, but existing systems for visualizing metadata focus on aspects other than understanding the greater structure. We present a tool for generating visualizations of ontologies and metadata by using a modified spring embedder to achieve an automatic layout. Through a case study using a mid-sized ontology, we show that interesting information about the data relationships can be extracted through our visualization of the physical graph structure.