We study the news reading behavior of several hundred thousand users on 65 highly visited news sites. We focus on a specific phenomenon: users reading several articles related to a particular news development, which we call story‐focused reading. Our goal is to understand the effect of story‐focused reading on user engagement and how news sites can support this phenomenon. We found that most users focus on stories that interest them and that even casual news readers engage in story‐focused reading. During story‐focused reading, users spend more time reading and a larger number of news sites are involved. In addition, readers employ different strategies to find articles related to a story. We also analyze how news sites promote story‐focused reading by looking at how they link their articles to related content published by them, or by other sources. The results show that providing links to related content leads to a higher engagement of the users, and that this is the case even for links to external sites. We also show that the performance of links can be affected by their type, their position, and how many of them are present within an article.

Click-through rate (CTR) is the most common metric used to assess the performance of an online advert; another performance of an online advert is the user post-click experience. In this paper, we describe the method we have implemented in Yahoo Gemini to measure the post-click experience on Yahoo mobile news streams via an automatic analysis of advert landing pages. We measure the post-click experience by means of two well-known metrics, dwell time and bounce rate. We show that these metrics can be used as proxy of an advert post-click experience, and that a negative post-click experience has a negative effect on user engagement and future ad clicks. We then put forward an approach that analyses advert landing pages, and show how these can affect dwell time and bounce rate. Finally, we develop a prediction model for advert quality based on dwell time, which was deployed on Yahoo mobile news stream app running on iOS. The results show that, using dwell time as a proxy of post-click experience, we can prioritise higher quality ads. We demonstrate the impact of this on users via A/B testing.

Wikidata promises to reduce factual inconsistencies across all Wikipedia language versions. It will enable dynamic data reuse and complex fact queries within the world's largest knowledge database. Studies of the existing participation patterns that emerge in Wikidata are only just beginning. What delineates most of the contributions in the system has not yet been investigated. Is it an inheritance from the Wikipedia peer-production system or the proximity of tasks in Wikidata that have been studied in collaborative ontology engineering? As a first step to answering this question, we performed a cluster analysis of participants' content editing activities. This allowed us to blend our results with typical roles found in peer-production and collaborative ontology engineering projects. Our results suggest very specialised contributions from a majority of users. Only a minority, which is the most active group, participate all over the project. These users are particularly responsible for developing the conceptual knowledge of Wikidata. We show the alignment of existing algorithmic participation patterns with these human patterns of participation. In summary, our results suggest that Wikidata rather supports peer-production activities caused by its current focus on data collection. We hope that our study informs future analyses and developments and, as a result, allows us to build better tools to support contributors in peer-production-based ontology engineering.

Many large online providers offer a variety of content sites (e.g. news, sport, e-commerce). These providers endeavor to keep users accessing and interacting with their sites, that is to engage users by spending time using their sites and to return regularly to them. They do so by serving users the most relevant content in an attractive and enticing manner. Due to their highly varied content, each site is usually studied and optimized separately. However, these online providers aim not only to engage users with individual sites, but across all sites in their network. In these cases, site engagement should be examined not only within individual sites, but also across the entire content provider network. This paper investigates intersite engagement, that is, site engagement within a network of sites, by defining a global measure of engagement that captures the effect sites have on the engagement on other sites. As an application, we look at the effect of web page layout and structure, which we refer to as web page stylistics, on intersite engagement on Yahoo! properties. Through the analysis of 50 popular Yahoo! sites and a sample of 265,000 users and 19.4M online sessions, we demonstrate that the stylistic components of a web page on a site can be used to predict inter-site engagement across the Yahoo! network of sites. Intersite engagement is a new big data problem as overall it implies analyzing dozen of sites visited by hundreds of millions of people generating billions of sessions.

Users often access and re-access more than one site during an online session, effectively engaging in multitasking. In this paper, we study the effect of online multitasking on two widely used engagement metrics designed to capture users browsing behavior with a site. Our study is based on browsing data of 2.5M users across 760 sites encompassing diverse types of services such as social media, news and mail. To account for multitasking we need to redefine how user sessions are represented and we need to adapt the metrics under study. We introduce a new representation of user sessions: tree-streams -- as opposed to the commonly used click-streams -- present a more accurate picture of the browsing behavior of a user that includes how users switch between sites (e.g., hyperlinking, teleporting, backpaging). We then discuss a number of insights on multitasking patterns, and show how these help to better understand how users engage with sites. Finally, we define metrics that characterize multitasking during online sessions and show how they provide additional insights to standard engagement metrics.

Micro-blogging systems such as Twitter expose digital traces of social discourse with an unprecedented degree of resolution of individual behaviors. They offer an opportunity to investigate how a large-scale social system responds to exogenous or endogenous stimuli, and to disentangle the temporal, spatial and topical aspects of users' activity. Here we focus on spikes of collective attention in Twitter, and specifically on peaks in the popularity of hashtags. Users employ hashtags as a form of social annotation, to define a shared context for a specific event, topic, or meme. We analyze a large-scale record of Twitter activity and find that the evolution of hashtag popularity over time defines discrete classes of hashtags. We link these dynamical classes to the events the hashtags represent and use text mining techniques to provide a semantic characterization of the hashtag classes. Moreover, we track the propagation of hashtags in the Twitter social network and find that epidemic spreading plays a minor role in hashtag popularity, which is mostly driven by exogenous factors.

Researchers of computer science and social science are increasingly interested in the Social Web and its applications. To improve existing infrastructures, to evaluate the success of available services, and to build new virtual communities and their applications, an understanding of dynamics and evolution of inherent social and informational structures is essential. One key question is how communities which exist in these applications are structured in terms of author contributions. Are there similar contribution patterns in different applications? For example, does the so called onion model revealed from open source software communities apply to Social Web applications as well? In this study, author contributions in the open content project Wikipedia are investigated. Previous studies to evaluate author contributions mainly concentrate on editing activities. Extending this approach, the added significant content and investigation of which author groups contribute the majority of content in terms of activity and significance are considered. Furthermore, the social information space is described by a dynamic collaboration network and the topic coverage of authors is analyzed. In contrast to existing approaches, the position of an author in a social network is incorporated. Finally, a new composite calculation to evaluate author contributions in Wikis is proposed. The action, the content contribution, and the connectedness of an author are integrated into one equation in order to evaluate author activity.