Social media platforms have democratized the process of web content creation allowing mere consumers to become creators and distributors of content. But this has also contributed to an explosive growth of information and has intensified the online competition for users attention, since only a small number of items become popular while the rest remain unknown. Understanding what makes one item more popular than another, observing its popularity dynamics, and being able to predict its popularity has thus attracted a lot of interest in the past few years. Predicting the popularity of web content is useful in many areas such as network dimensioning (e.g., caching and replication), online marketing (e.g., recommendation systems and media advertising), or real-world outcome prediction (e.g., economical trends). In this survey, we review the current findings on web content popularity prediction. We describe the different popularity prediction models, present the features that have shown good predictive capabilities, and reveal factors known to influence web content popularity.

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International audienceUnderstanding user participation is fundamental in anticipating the popularity of online content. In this paper, we explore how the number of users' comments during a short observation period after publication can be used to predict the expected popularity of articles published by a countrywide online newspaper. We evaluate a simple linear prediction model on a real dataset of hundreds of thousands of articles and several millions of comments collected over a period of four years. Analyzing the accuracy of our proposed model for different values of its basic parameters we provide valuable insights on the potentials and limitations for predicting content popularity based on early user activity

Abstract-Advanced input queuing is an attractive, promising architecture for high-speed ATM switches, because it combines the low cost of input queuing with the high performance of output queuing. The need for scalable schedulers for advanced input queuing switch architectures has led to the development of efficient distributed scheduling algorithms.We introduce a new distributed scheduling algorithm, FIRM, which provides improved performance characteristics over alternative distributed algorithms. FIRM achieves saturation throughput 1 with lower delay than the most efficient alternative (up to 50% at high load). Furthermore, it provides improved fairness (it approximates FCFS) and tighter service guarantee than others. FIRM provides a basis for a class of distributed scheduling algorithms, many of which provide even more improved performance characteristics.

News articles are an engaging type of online content that captures the attention of a significant amount of Internet users. They are particularly enjoyed by mobile users and massively spread through online social platforms. As a result, there is an increased interest in discovering the articles that will become popular among users. This objective falls under the broad scope of content popularity prediction and has direct implications in the development of new services for online advertisement and content distribution. In this paper, we address the problem of predicting the popularity of news articles based on user comments. We formulate the prediction task as a ranking problem, where the goal is not to infer the precise attention that a content will receive but to accurately rank articles based on their predicted popularity. Using data obtained from two important news sites in France and Netherlands, we analyze the ranking effectiveness of two prediction models. Our results indicate that popularity prediction methods are adequate solutions for this ranking task

Index Terms-Community networks, sustainability, incentive mechanisms.Abstract-Community network (CN) initiatives have been around for roughly two decades, evangelizing a distinctly different paradigm for building, maintaining, and sharing network infrastructure but also defending the basic human right to Internet access. Over this time they have evolved into a mosaic of systems that vary widely with respect to their network technologies, their offered services, their organizational structure, and the way they position themselves in the overall telecommunications' ecosystem. Common to all these highly differentiated initiatives is the sustainability challenge. We approach sustainability as a broad term with an economical, political, and cultural context. We first review the different perceptions of the term. These vary both across and within the different types of stakeholders involved in CNs and are reflected in their motivation to join such initiatives. Then, we study the diverse ways that CN operators pursue the sustainability goal. Depending on the actual context of the term, these range all the way from mechanisms to fund their activities and synergistic approaches with commercial service providers, to organizational structures and social activities that serve as incentives to maximize the engagement of their members. Finally, we iterate and discuss theoretical concepts of incentive mechanisms that have been proposed in the literature for these networks as well as implemented tools and processes designed to set the ground for CN participation. While, theoretical mechanisms leverage game theory, reputation frameworks, and social mechanisms, implemented mechanisms focus on organizational matters, education and services, all aiming to motivate the active and sustained participation of users and other actors in the CN.

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In the Web 2.0 era, users share and discover interesting content via a network of relationships created in various social networking or content sharing sites. They can become for example contacts, followers or friends and express their appreciation of specific content uploaded by their peers by faving, retweeting or liking them depending on whether they are in Flickr, Twitter or Facebook respectively. Then they can discover additional content of interest through the lists of favorites of their contacts and so on. This faving (or favoring) functionality becomes thus a central part of content sharing communities for two purposes: (a) it helps the propagation of content amongst users and (b) it stimulates users' participation and activity. In this paper, we make a first step to understand users' faving behavior in content sharing communities in terms of reciprocity using publicly available datasets from Flickr and Twitter. Do users favor content only when they really appreciate it or they often feel the need to reciprocate when their content is appreciated by one of their contacts or even by a stranger? Do people take advantage of this process to gain popularity? What is the impact of the design, the social software, of a specific community and the type of content shared? These are some of the questions that our first results help to answer.