Shooting the information rapids

Terms such as 'navigation' and 'information orienteering' have been applied to users working in large information spaces such as the Web or digital libraries. Such terms – and their descriptions – imply that the user is in control of the interaction, moving deliberately through the information space. In practice, as recognised in the work on situated cognition, users often behave much more reactively than this, responding to external stimuli in a fluid way. In this paper we report on user behaviour when interacting with a collection of digital libraries, focusing particularly on situations where users were switching between multiple windows

The Impact of Sentiment Analysis Output on Decision Outcomes: An Empirical Evaluation

User-generated online content serves as a source of product- and service-related information that reduces the uncertainty in consumer decision making, yet the abundance of such content makes it prohibitively costly to use all relevant information. Dealing with this (big data) problem requires a consumer to decide what subset of information to focus on. Peer-generated star ratings are excellent tools for one to decide what subset of information to focus on as they indicate a review’s “tone”. However, star ratings are not available for all user-generated content and not detailed enough in other cases. Sentiment analysis, a text-analytic technique that automatically detects the polarity of text, provides sentiment scores that are comparable to, and potentially more refined than, star ratings. Despite its popularity as an active topic in analytics research, sentiment analysis outcomes have not been evaluated through rigorous user studies. We fill that gap by investigating the impact of sentiment scores on purchase decisions through a controlled experiment using 100 participants. The results suggest that, consistent with the effort-accuracy trade off and effort-minimization concepts, sentiment scores on review documents improve the efficiency (speed) of purchase decisions without significantly affecting decision effectiveness (confidence)

An evaluation of semantic fisheye views for opportunistic search in an annotated image collection

Visual interfaces are potentially powerful tools for users to explore a representation of a collection and opportunistically discover information that will guide them toward relevant documents. Semantic fisheye views (SFEVs) are focus + context visualization techniques that manage visual complexity by selectively emphasizing and increasing the detail of information related to the user's focus and deemphasizing or filtering less important information. In this paper we describe a prototype for visualizing an annotated image collection and an experiment to compare the effectiveness of two distinctly different SFEVs for a complex opportunistic search task. The first SFEV calculates relevance based on keyword-content similarity and the second based on conceptual relationships between images derived using WordNet. The results of the experiment suggest that semantic-guided search is significantly more effective than similarity-guided search for discovering and using domain knowledge in a collectio

Become a Fan: A Review of Restaurants\u27 Facebook Fan Pages

Social media has attracted increasing research interests in recent years. It can be used as an effective marketing tool. To the authors’ knowledge, no academic research has been conducted to conceptualize the approach for social media marketing, and the potential effect on customer attitude. Therefore, the current study sets out to propose a conceptual model that articulates the technological affordances of social media. In addition, heuristics triggered by corresponding affordances are introduced. Lastly, to identify the gap between an optimal strategy and the existing practice, this study examined 25 restaurant facebook fan pages. The results indicate that restaurant companies have not yet fully utilized the affordance of social media

Examining the Influence of Saliency in Mobile Interface Displays

Designers spend more resources to develop better mobile experiences today than ever before. Researchers commonly use visual search efficiency as a usability measure to determine the time or effort it takes someone to perform a task. Previous research has shown that a computational visual saliency model can predict attentional deployment in stationary desktop displays. Designers can use this salience awareness to co-locate important task information with higher salience regions. Research has shown that placing targets in higher salience regions in this way improves interface efficiency. However, researchers have not tested the model in key mobile technology design dimensions such as small displays and touch screens. In two studies, we examined the influence of saliency in a mobile application interface. In the first study, we explored a saliency model’s ability to predict fixations in small mobile interfaces at three different display sizes under free-viewing conditions. In the second study, we examined the influence that visual saliency had on search efficiency while participants completed a directed search for either an interface element associated with high or low salience. We recorded reaction time to touch the targeted element on the tablet. We experimentally blocked high and low saliency interactions and subjectively measured cognitive workload. We found that a saliency model predicted fixations. In the search task, participants found highly salient targets about 900 milliseconds faster than low salient targets. Interestingly, participants did not perceive a lighter cognitive workload associated with the increase in search efficiency

VisIRR: Interactive Visual Information Retrieval and Recommendation for Large-scale Document Data

Research areas: Machine learning, Data mining, Information visualization, Visual analytics, Text visualization.We present a visual analytics system called VisIRR, which is an interactive visual information retrieval and recommendation system for document discovery. VisIRR effectively combines both paradigms of passive pull through a query processes for retrieval and active push that recommends the items of potential interest based on the user preferences. Equipped with efficient dynamic query interfaces for a large corpus of document data, VisIRR visualizes the retrieved documents in a scatter plot form with their overall topic clusters. At the same time, based on interactive personalized preference feedback on documents, VisIRR provides recommended documents reaching out to the entire corpus beyond the retrieved sets. Such recommended documents are represented in the same scatter space of the retrieved documents so that users can perform integrated analyses of both retrieved and recommended documents seamlessly. We describe the state-of-the-art computational methods that make these integrated and informative representations as well as real time interaction possible. We illustrate the way the system works by using detailed usage scenarios. In addition, we present a preliminary user study that evaluates the effectiveness of the system

A framework for understanding user interaction with content-based image retrieval: model, interface and users

User interaction is essential to the communication between users and content-based image retrieval (CBIR) systems. User interaction covers three key elements: an interaction model, an interactive interface and users. The three key elements combine to enable effective interaction to happen. Many studies have investigated different aspects of user interaction. However, there is lack of research in combining all three elements in an integrated manner, especially through well-principled data analysis based on a systematic user study. In this thesis, we investigate the combination of all three elements for interactive CBIR. We first propose uInteract - a framework including a novel four-factor user interaction model (FFUIM) and an interactive interface. The FFUIM aims to improve interaction and search accuracy of the relevance feedback mechanism for CBIR. The interface delivers the FFUIM visually, aiming to support users in grasping how the interaction model functions and how best to manipulate it. The framework is tested in three task-based and user-oriented comparative evaluations, which involves 12 comparative systems, 12 real life scenario tasks and 50 subjects. The quantitative data analysis shows encouraging observations on ease of use and usefulness of the proposed framework, and also reveals a large variance of the results depending on different user types. Accordingly, based on Information Foraging Theory, we further propose a user classification model along three user interaction dimensions: information goals (I), search strategies (S) and evaluation thresholds (E) of users. To our best knowledge, this is the first principled user classification model in CBIR. The model is operated and verified by a systematic qualitative data analysis based on multi linear regression on the real user interaction data from comparative user evaluations. From final quantitative and qualitative data analysis based on the ISE model, we have established what different types of users like about the framework and their preferences for interactive CBIR systems. Our findings offer useful guidelines for interactive search system design, evaluation and analysis

Variations Foraging

Information Foraging Theory (IFT) has successfully explained how people seek information in various domains, in turn, informing the design of several tools and information-intensive environments. However, prior research has not explored foraging in the presence of several, very similar variants of the same artifact. Such variants are commonplace in several creative, exploratory tasks such as graphic design, writing and programming. In this thesis, we evaluate whether and how IFT applies to variants. Using empirical studies and computational models that predict programmers’ information foraging among variants, this thesis provides evidence for the applicability of IFT in variations situations and offers new insights for variations-support tools. Along the way, this thesis also demonstrates the benefits of computationally modeling: 1) the hierarchical organization of information environments, 2) variable costs of navigation in an information environment and 3) accounting for non-textual (graphical) information