Clarity and ease of interaction are critical for mobile devices that users rely on daily. As smartphone use time rises, manufacturers are exploring e-paper displays for their advantages, including longer battery life and reduced eye strain. Yet, e-paper technology comes with inherent design limitations, such as reduced responsiveness, constrained color ranges, and ghosting. To address these limitations, we present the first version of our E-Paper Design System comprising a set of design components and guidelines developed specifically for e-paper smartphones, with a particular focus on minimalist devices. The E-Paper Design System is released as a free, open resource on Zeroheight and documented in the Appendix for reference. Our design decisions are grounded in technical constraints, prior research and insights from designers and developers using this pilot version of the design system to create custom mobile applications. We also report findings from an exploratory in-house UX study (N=24) testing developed applications, highlight remaining e-paper-specific design challenges and outline future research directions.

• The foraging paradigm can be applied in research on Multi-List Recommender Interfaces (MLRIs). • Older users find fewer products that match their preferences in MLRIs than younger ones. • The decreased performance of older users in MLRIs is mediated by their reduced exploration, that is inability to switch to new carousels at the right time. • A digital nudge that is activated when a user browses a carousel without interacting with the products on that carousel improves the performance of both older and younger users in MLRIs. User interfaces composed of multiple carousels (Multi-List Recommender Interfaces, MLRIs) are today’s standard for recommender systems used in e-commerce and streaming or music platforms. User behavior in such systems can be compared to foraging, a research paradigm used in the natural sciences and psychology. Research on foraging points out several hypotheses that can apply to MLRI users. Results from the psychology of aging point out possible limitations of older adults in the use of MLRIs. We verify this hypothesis in an experiment that measures users’ objective performance ( N = 441). The experiment results confirm that older users are less effective in a task of searching for products in a MLRI interface. We propose an improvement of the carousel interface through a digital nudge that aims to prompt a user to leave a carousel that does not contain items matching the user’s preferences, and switch to another carousel. Our experimental results confirm the effectiveness of the proposed digital nudge in increasing the performance of both older and younger users.

Based on our research on the Wikipedia interface and crowdsourcing with older adults, we propose a conversational interface to streamline Wikipedia editing, engage new contributors and increase their well-being. The use of a conversational interface may mitigate the problem of a steep learning curve for new contributors to encourage more people to contribute to Wikipedia, and thus, little by little, make it more accurate, consistent and democratic. This solution can also negotiate some of the barriers apparent in older adults’ interaction with the Wikipedia interface. To achieve these goals, we conceptualized a friendly chatbot called "Gizmo" which inverts the human-chatbot interaction paradigm by making the user be the one to aid the chatbot. In doing so, we explored some of the requirements and challenges associated with the design of a conversational interface to enable Wiki contributions. These include the choice of the appropriate task to crowdsource, in our case the infobox translation verification, the initiation of the conversation as well as the motivational component with key disaffection indicators. At the same time, we discuss some opportunities within the domain of CSCW related to the design and applications of novel conversational crowdsourcing interfaces.

Citation analysis does not tell the whole story about the innovativeness of scientific papers. Works by prominent authors tend to receive disproportionately many citations, while publications by less well-known researchers covering the same topics may not attract as much attention. In this paper we address the shortcomings of traditional scientometric approaches by proposing a novel method that utilizes a classifier for predicting publication years based on latent topic distributions. We then calculate real-number innovation scores used to identify potential breakthrough papers and turnaround years. The proposed approach can complement existing citation-based measures of article importance and author contribution analysis; it opens as well novel research direction for time-based, innovation-centered research scientific output evaluation. In our experiments, we focus on two corpora of research papers published over several decades at two well-established conferences: The World Wide Web Conference (WWW) and the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), containing around 3500 documents in total. We indicate significant years and demonstrate examples of highly-ranked papers, thus providing a novel insight on the evolution of the two conferences. Finally, we compare our results to citation analysis and discuss how our approach may complement traditional scientometrics.

In this paper, we present our novel approach to recommender systems based on a neuro-fuzzy approach. The neuro-fuzzy approach allows for deciding to recommend or not to recommend processed items for a user. By using it, we can understand the decision through analyzing rules of decision paths. Our method gives a possibility to learn and simulate users decisions based on their actions in our test environment. Finally, a rank list of top-rated items is delivered to the user based on simulated rank for each of them. We develop our AI framework to perform tests with the use of CUDA technology. Additionally, we develop a user interface in the form of a web application. It gives the possibility to perform simulations of real users. To compare our approach with a deep learning based method, we perform tests on the MovieLens 20M Dataset. It should be noted that the architecture of the data module of our system allowed for reasonably easy integration with MovieLens data.

Globally observed trends in aging indicate that older adults constitute a growing share of the population and an increasing demographic in the modern technologies marketplace. Therefore, it has become important to address the issue of participation of older adults in the process of developing solutions suitable for their group. In this study, we approached this topic by organizing a hackathon involving teams of young programmers and older adult participants. In our paper we describe a case study of that hackathon, in which our objective was to motivate older adults to participate in software engineering processes. Based on our results from an array of qualitative methods, we propose a set of good practices that may lead to improved older adult participation in similar events and an improved process of developing apps that target older adults.

We investigated how product attributes, average consumer ratings, and single affect-rich positive or negative consumer reviews influenced hypothetical online purchasing decisions of younger and older adults. In line with previous research, we found that younger adults used all three types of information: they clearly preferred products with better attributes and with higher average consumer ratings. If making a choice was difficult because it involved trade-offs between product attributes, most younger adults chose the higher-rated product. The preference for the higher-rated product, however, could be overridden by a single affect-rich negative or positive review. Older adults were strongly influenced by a single affect-rich negative review and also took into consideration product attributes; however, they did not take into account average consumer ratings or single affect-rich positive reviews. These results suggest that older adults do not consider aggregated consumer information and positive reviews focusing on positive experiences with the product, but are easily swayed by reviews reporting negative experiences.

Abstract Globally observed trends in aging indicate that older adults constitute a growing share of the population and an increasing demographic in the modern technologies marketplace. Therefore, it has become important to address the issue of participation of older adults in the process of developing solutions suitable for their group. In this study, we approached this topic by organizing a hackathon involving teams of young programmers and older adults participants. Below we describe a case study of that hackathon, in which our objective was to motivate older adults to participate in software engineering processes. Based on our results from an array of qualitative methods, we propose a set of good practices that may lead to improved older adult participation in similar events and an improved process of developing apps that target older adults.

Controversy is a complex concept that has been attracting attention of scholars from diverse fields. In the era of Internet and social media, detecting controversy and controversial concepts by the means of automatic methods is especially important. Web searchers could be alerted when the contents they consume are controversial or when they attempt to acquire information on disputed topics. Presenting users with the indications and explanations of the controversy should offer them chance to see the “wider picture” rather than letting them obtain one-sided views. In this work we first introduce a formal model of controversy as the basis of computational approaches to detecting controversial concepts. Then we propose a classification based method for automatic detection of controversial articles and categories in Wikipedia. Next, we demonstrate how to use the obtained results for the estimation of the controversy level of search queries. The proposed method can be incorporated into search engines as a component responsible for detection of queries related to controversial topics. The method is independent of the search engine’s retrieval and search results recommendation algorithms, and is therefore unaffected by a possible filter bubble. Our approach can be also applied in Wikipedia or other knowledge bases for supporting the detection of controversy and content maintenance. Finally, we believe that our results could be useful for social science researchers for understanding the complex nature of controversy and in fostering their studies.

In this paper we describe the insights from an exploratory qualitative pilot study testing the feasibility of a solution that would encourage older adults to participate in online crowdsourcing tasks in a non-computer scenario. Therefore, we developed an Android TV application using Amara API to retrieve subtitles for TEDx talks which allows the participants to detect and categorize errors to support the quality of the translation and transcription processes. It relies on the older adults' innate skills as long-time native language users and the motivating factors of this socially and personally beneficial task. The study allowed us to verify the underlying concept of using Smart TVs as interfaces for crowdsourcing, as well as possible barriers, including the interface, configuration issues, topics and the process itself. We have also assessed the older adults' interaction and engagement with this TV-enabled online crowdsourcing task and we are convinced that the design of our setup addresses some key barriers to crowdsourcing by older adults. It also validates avenues for further research in this area focused on such considerations as autonomy and freedom of choice, familiarity, physical and cognitive comfort as well as building confidence and the edutainment value.

In this paper we provide a brief summary of development LivingLab PJAIT as an attempt to establish a comprehensive and sustainable ICT-based solution for empowerment of elderly communities towards better urban participation of seniors. We report on our various endeavors for better involvement and participation of older adults in urban life by lowering ICT barriers, encouraging social inclusion, intergenerational interaction, physical activity and engaging older adults in the process of development of ICT solutions. We report on a model and assumptions of the LivingLab PJAIT as well as a number of activities created and implemented for LivingLab participants: from ICT courses, both traditional and e-learning, through on-line crowdsourcing tasks, to blended activities of different forms and complexity. We also provide conclusions on the lessons learned in the process and some future plans, including solutions for better senior urban participation and citizen science.

Understanding of interaction between people and urban spaces is crucial for inclusive decision making process. Smartphones and social media can be a rich source of behavioral and declarative data about urban space, but it threatens to exclude voice of older adults. The platform proposed in the paper attempts to address this issue. A universal tagging mechanism based on the Pluchik Wheel of Emotion is proposed. Usability of the platform was tested and prospect studies are proposed.

Success of Wikipedia would not be possible without the contributions of millions of anonymous Internet users who edit articles, correct mistakes, add links or pictures. At the same time Wikipedia editors are currently overworked and there is always more tasks waiting to be completed than people willing to volunteer. The paper explores the possibility of involving the elderly in the Wikipedia editing process. Older adults were asked to complete various tasks on Wikipedia. Based on the observations made during these activities as well as in-depth interviews, a list of recommendation has been crafted. It turned out that older adults are willing to contribute to Wikiepdia but substantial changes have to be made in the Wikipedia editor.

The goal of our research is to create a predictive model of Web content credibility evaluations, based on human evaluations. The model has to be based on a comprehensive set of independent factors that can be used to guide user’s credibility evaluations in crowdsourced systems like WOT, but also to design machine classifiers of Web content credibility. The factors described in this article are based on empirical data. We have created a dataset obtained from an extensive crowdsourced Web credibility assessment study (over 15 thousand evaluations of over 5000 Web pages from over 2000 participants). First, online participants evaluated a multi-domain corpus of selected Web pages. Using the acquired data and text mining techniques we have prepared a code book and conducted another crowdsourcing round to label textual justifications of the former responses. We have extended the list of significant credibility assessment factors described in previous research and analyzed their relationships to credibility evaluation scores. Discovered factors that affect Web content credibility evaluations are also weakly correlated, which makes them more useful for modeling and predicting credibility evaluations. Based on the newly identified factors, we propose a predictive model for Web content credibility. The model can be used to determine the significance and impact of discovered factors on credibility evaluations. These findings can guide future research on the design of automatic or semi-automatic systems for Web content credibility evaluation support. This study also contributes the largest credibility dataset currently publicly available for research: the Content Credibility Corpus (C3).

GitHub is one of the most commonly used web-based code repository hosting service. Majority of projects hosted on GitHub are really small but, on the other hand, developers spend most of their time working in medium to large repositories. Developers can freely join and leave projects following their current needs and interests. Based on real data collected from GitHub we have tried to predict which developer will join which project. A mix of carefully selected list of features and machine learning techniques let us achieve a precision of 0.886, in the best case scenario, where there is quite a long history of a user and a repository in the system. Even when proposed classifier faces a cold start problem, it delivers precision equal to 0.729 which is still acceptable for automatic recommendation of noteworthy projects for developers.

Irony is something most people can tell is therewhen they see it, but it is not so easy to define, let alone detectautomatically. In this paper we describe the construction of abalanced corpus of ironic vs. serious watch reviews and show thepromising results achieved by classifiers trained on this corpusin predicting the presence of irony or lack thereof in productreviews from a manually labeled corpus. We try to find commonfeatures in the two corpora and outline our next steps towardsa model which would detect ironic utterances in more general contexts.

Machine learning algorithms and recommender systems trained on human ratings are widely in use today. However, human ratings may be associated with a high level of uncertainty and are subjective, influenced by demographic or psychological factors. We propose a new approach to the design of object classes from human ratings: the use of entire distributions to construct classes. By avoiding aggregation for class definition, our approach loses no information and can deal with highly volatile or conflicting ratings. The approach is based the concept of the Earth Mover's Distance (EMD), a measure of distance for distributions. We evaluate the proposed approach based on four datasets obtained from diverse Web content or movie quality evaluation services or experiments. We show that clusters discovered in these datasets using the EMD measure are characterized by a consistent and simple interpretation. Quality classes defined using entire rating distributions can be fitted to clusters of distributions in the four datasets using two parameters, resulting in a good overall fit. We also consider the impact of the composition of small samples on the distributions that are the basis of our classification approach. We show that using distributions based on small samples of 10 evaluations is still robust to several demographic and psychological variables. This observation suggests that the proposed approach can be used in practice for quality evaluation, even for highly uncertain and subjective ratings.

By leveraging crowdsourcing, Web credibility evaluation systems (WCESs) have become a promising tool to assess the credibility of Web content, e.g., Web pages. However, existing systems adopt a passive way to collect users' credibility ratings, which incurs two crucial challenges: (1) a considerable fraction of Web content have few or even no ratings, so the coverage (or effectiveness) of the system is low; (2) malicious users may submit fake ratings to damage the reliability of the system. In order to realize a highly effective and robust WCES, we propose to integrate recommendation functionality into the system. On the one hand, by fusing Matrix Factorization and Latent Dirichlet Allocation, a personalized Web content recommendation model is proposed to attract users to rate more Web pages, i.e., the coverage is increased. On the other hand, by analyzing a user's reaction to the recommended Web content, we detect imitating attackers, which have recently been recognized as a particular threat to WCES to make the system more robust. Moreover, an adaptive reputation system is designed to motivate users to more actively interact with the integrated recommendation functionality. We conduct experiments using both real datasets and synthetic data to demonstrate how our proposed recommendation components significantly improve the effectiveness and robustness of existing WCES.