With this article, we survey the research performed in the domain of browser fingerprinting, while providing an accessible entry point to newcomers in the field. We explain how this technique works and where it stems from. We analyze the related work in detail to understand the composition of modern fingerprints and see how this technique is currently used online. We systematize existing defense solutions into different categories and detail the current challenges yet to overcome.

The explosive growth of e-commerce has led to the development of the recommendation system. The recommendation system aims to provide a set of items that meet users’ personalized needs through analyzing users’ consumption records. However, the timeliness of purchasing data and the implicity of feedback data pose severe challenges for the existing recommendation methods. To alleviate these challenges, we exploit the user’s consumption records from the perspectives of user and item, by modeling the data on both item and user level, where the item-level value reflects the grade of item, and the user-level value reflects the user’s purchase intention. In this article, we collect the description information and the reviews of the items from public websites, then adopt sentiment analysis techniques to model the similarities on user level and item level, respectively. In particular, we extend the traditional latent factor model and propose two novel methods— I tem L evel Similarity M atrix F actorization (ILMF) and U ser L evel Similarity M atrix F actorization (ULMF)—by introducing two novel similarity measure methods. In ILMF and ULMF, the consistency between latent factors and explicit aspects is naturally incorporated into learning latent factors of the users and items, such that we can predict the users’ preferences on different items more accurately. Moreover, we propose I tem- U ser L evel Similarity M atrix F actorization (IULMF), which combines these two methods to study their contributions on the final performance. Experimental evaluations on the real datasets show that our methods outperform the baseline approaches in terms of both the precision and NDCG.

Context: End-user service composition (EUSC) is a service-oriented paradigm that aims to empower end users and allow them to compose their own web applications from reusable service components. User studies have been used to evaluate EUSC tools and processes. Such an approach should benefit software development, because incorporating end users’ feedback into software development should make software more useful and usable. Problem: There is a gap in our understanding of what constitutes a user study and how a good user study should be designed, conducted, and reported. Goal: This article aims to address this gap. Method: The article presents a systematic review of 47 selected user studies for EUSC. Guided by a review framework, the article systematically and consistently assesses the focus, methodology and cohesion of each of these studies. Results: The article concludes that the focus of these studies is clear, but their methodology is incomplete and inadequate, their overall cohesion is poor. The findings lead to the development of a design framework and a set of questions for the design, reporting, and review of good user studies for EUSC. The detailed analysis and the insights obtained from the analysis should be applicable to the design of user studies for service-oriented systems as well and indeed for any user studies related to software artifacts.

Offensive or antagonistic language targeted at individuals and social groups based on their personal characteristics (also known as cyber hate speech or cyberhate) has been frequently posted and widely circulated via the World Wide Web. This can be considered as a key risk factor for individual and societal tension surrounding regional instability. Automated Web-based cyberhate detection is important for observing and understanding community and regional societal tension—especially in online social networks where posts can be rapidly and widely viewed and disseminated. While previous work has involved using lexicons, bags-of-words, or probabilistic language parsing approaches, they often suffer from a similar issue, which is that cyberhate can be subtle and indirect—thus, depending on the occurrence of individual words or phrases, can lead to a significant number of false negatives, providing inaccurate representation of the trends in cyberhate. This problem motivated us to challenge thinking around the representation of subtle language use, such as references to perceived threats from “the other” including immigration or job prosperity in a hateful context. We propose a novel “othering” feature set that utilizes language use around the concept of “othering” and intergroup threat theory to identify these subtleties, and we implement a wide range of classification methods using embedding learning to compute semantic distances between parts of speech considered to be part of an “othering” narrative. To validate our approach, we conducted two sets of experiments. The first involved comparing the results of our novel method with state-of-the-art baseline models from the literature. Our approach outperformed all existing methods. The second tested the best performing models from the first phase on unseen datasets for different types of cyberhate, namely religion, disability, race, and sexual orientation. The results showed F-measure scores for classifying hateful instances obtained through applying our model of 0.81, 0.71, 0.89, and 0.72, respectively, demonstrating the ability of the “othering” narrative to be an important part of model generalization.

Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect usage only indirectly. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app, have been suggested to predict successful app lifecycles. We conduct the first independent and large-scale study of retention rates and usage trends on a dataset of app-usage data from a community of 339,842 users and more than 213,667 apps. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. It also reveals, however, that many applications have more complex usage behaviour patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. Analysis of our data using this trend filter shows that roughly 40% of all apps never gain more than a handful of users ( Marginal apps). Less than 0.1% of the remaining 60% are constantly popular ( Dominant apps), 1% have a quick drain of usage after an initial steep rise ( Expired apps), and 6% continuously rise in popularity ( Hot apps). From these, we can distinguish, for instance, trendsetters from copycat apps. We conclude by demonstrating that usage behaviour trend information can be used to develop better mobile app recommendations.

Social network research has begun to take advantage of fine-grained communications regarding coordination, decision-making, and knowledge sharing. These studies, however, have not generally analyzed how external events are associated with a social network’s structure and communicative properties. Here, we study how external events are associated with a network’s change in structure and communications. Analyzing a complete dataset of millions of instant messages among the decision-makers with different roles in a large hedge fund and their network of outside contacts, we investigate the link between price shocks, network structure, and change in the affect and cognition of decision-makers embedded in the network. We also analyze the communication dynamics among specialized teams in the organization. When price shocks occur the communication network tends not to display structural changes associated with adaptiveness such as the activation of weak ties to obtain novel information. Rather, the network “turtles up.” It displays a propensity for higher clustering, strong tie interaction, and an intensification of insider vs. outsider and within-role vs. between-role communication. Further, we find changes in network structure predict shifts in cognitive and affective processes, execution of new transactions, and local optimality of transactions better than prices, revealing the important predictive relationship between network structure and collective behavior within a social network.

Recent research has shown a substantial active presence of bots in online social networks (OSNs). In this article, we perform a comparative analysis of the usage and impact of bots and humans on Twitter—one of the largest OSNs in the world. We collect a large-scale Twitter dataset and define various metrics based on tweet metadata. Using a human annotation task, we assign “bot” and “human” ground-truth labels to the dataset and compare the annotations against an online bot detection tool for evaluation. We then ask a series of questions to discern important behavioural characteristics of bots and humans using metrics within and among four popularity groups. From the comparative analysis, we draw clear differences and interesting similarities between the two entities.

In our prior work, we presented a novel approach to the evaluation of quality in use of corporate web sites based on an original quality model (QM-U) and a related methodology (EQ-EVAL). This article focuses on two research questions. The first one aims at investigating whether expected quality obtained through the application of EQ-EVAL methodology by employing a small panel of evaluators is a good approximation of actual quality obtained through experimentation with real users. To answer this research question, a comparative study has been carried out involving 5 evaluators and 50 real users. The second research question aims at demonstrating that the adoption of the EQ-EVAL methodology can provide useful information for web site improvement. Three original indicators, namely coherence, coverage and ranking have been defined to answer this question, and an additional study comparing the assessments of two panels of 5 and 10 evaluators, respectively, has been carried out. The results obtained in both studies are largely positive and provide a rational support for the adoption of the EQ-EVAL methodology.

The Semantic Web is commonly interpreted under the open-world assumption, meaning that information available (e.g., in a data source) captures only a subset of the reality. Therefore, there is no certainty about whether the available information provides a complete representation of the reality. The broad aim of this article is to contribute a formal study of how to describe the completeness of parts of the Semantic Web stored in RDF data sources. We introduce a theoretical framework allowing augmentation of RDF data sources with statements, also expressed in RDF, about their completeness. One immediate benefit of this framework is that now query answers can be complemented with information about their completeness. We study the impact of completeness statements on the complexity of query answering by considering different fragments of the SPARQL language, including the RDFS entailment regime, and the federated scenario. We implement an efficient method for reasoning about query completeness and provide an experimental evaluation in the presence of large sets of completeness statements.

In this article, we address the problem of text passage alignment across interlingual article pairs in Wikipedia. We develop methods that enable the identification and interlinking of text passages written in different languages and containing overlapping information. Interlingual text passage alignment can enable Wikipedia editors and readers to better understand language-specific context of entities, provide valuable insights in cultural differences, and build a basis for qualitative analysis of the articles. An important challenge in this context is the tradeoff between the granularity of the extracted text passages and the precision of the alignment. Whereas short text passages can result in more precise alignment, longer text passages can facilitate a better overview of the differences in an article pair. To better understand these aspects from the user perspective, we conduct a user study at the example of the German, Russian, and English Wikipedia and collect a user-annotated benchmark. Then we propose MultiWiki, a method that adopts an integrated approach to the text passage alignment using semantic similarity measures and greedy algorithms and achieves precise results with respect to the user-defined alignment. The MultiWiki demonstration is publicly available and currently supports four language pairs.