The Untold Story of the Clones: Content-agnostic Factors that Impact YouTube Video Popularity

Video dissemination through sites such as YouTube can have widespread impacts on opinions, thoughts, and cultures. Not all videos will reach the same popularity and have the same impact. Popularity differences arise not only because of differences in video content, but also because of other "content-agnostic" factors. The latter factors are of considerable interest but it has been difficult to accurately study them. For example, videos uploaded by users with large social networks may tend to be more popular because they tend to have more interesting content, not because social network size has a substantial direct impact on popularity. In this paper, we develop and apply a methodology that is able to accurately assess, both qualitatively and quantitatively, the impacts of various content-agnostic factors on video popularity. When controlling for video content, we observe a strong linear "rich-get-richer" behavior, with the total number of previous views as the most important factor except for very young videos. The second most important factor is found to be video age. We analyze a number of phenomena that may contribute to rich-get-richer, including the first-mover advantage, and search bias towards popular videos. For young videos we find that factors other than the total number of previous views, such as uploader characteristics and number of keywords, become relatively more important. Our findings also confirm that inaccurate conclusions can be reached when not controlling for content.Comment: Dataset available at: http://www.ida.liu.se/~nikca/papers/kdd12.htm

Catalog Dynamics: Impact of Content Publishing and Perishing on the Performance of a LRU Cache

The Internet heavily relies on Content Distribution Networks and transparent caches to cope with the ever-increasing traffic demand of users. Content, however, is essentially versatile: once published at a given time, its popularity vanishes over time. All requests for a given document are then concentrated between the publishing time and an effective perishing time. In this paper, we propose a new model for the arrival of content requests, which takes into account the dynamical nature of the content catalog. Based on two large traffic traces collected on the Orange network, we use the semi-experimental method and determine invariants of the content request process. This allows us to define a simple mathematical model for content requests; by extending the so-called "Che approximation", we then compute the performance of a LRU cache fed with such a request process, expressed by its hit ratio. We numerically validate the good accuracy of our model by comparison to trace-based simulation.Comment: 13 Pages, 9 figures. Full version of the article submitted to the ITC 2014 conference. Small corrections in the appendix from the previous versio

Examination of Primary and Secondary School Teachers’ Aspects Towards Educational Use of Video Sharing Websites

Daily use of video has increased by televisions, but lately people have been using video sharing websites most frequently. This extended use of video sharing websites has emerged a new era for education; teachers and learners can use them to enhance learning in education.  Hence, the purpose of this study is to examine primary and secondary school teachers’ aspects towards educational use of video sharing websites. This research is conducted as a survey model and carried out with the participation of 114 teachers in total, 48 teachers from Taşkent and 66 teachers from Kulu districts of Konya province in Turkey. Quantitative research method has been adopted as the model of this research. Data is obtained by a 19-item questionnaire and analyzed by SPSS (Statistical Package for the Social Sciences) program by using descriptive statistics and basic correlation. Results of this study revealed that teachers have positive attitudes towards using video sharing websites as an educational tool which improves learning. Keywords: primary-secondary school teachers, video sharing websites, educational vide

Students’ Acceptance On Educational Video Sharing Sit: A Proposed Research Model

Video sharing site is becoming increasingly popular and is used as a platform for video-based learning and teaching. In line with the development of new media technologies nowadays, learning through video sharing sites has become a choice of preference amongst students to get access to learning materials in the form of videos such as screencast tutorials, video presentations, recordings of learning video, animations and so forth. However, the existence of video sharing sites that have a social media characteristic in them is negatively affecting the students' learning performance. Thus, a provision of video sharing site with a more formal educational characteristic should be established in order to facilitate a safer learning environment. This study was conducted to analyse the acceptance of students towards educational video sharing sites. By using Technology Acceptance Model (TAM) as the basic model for this study, the original attributes in TAM model such as perceived usefulness and perceived ease of use were put to the test in order to determine their effects on attitudes and intentions of the students to use educational video sharing sites. In addition to that, TAM model was also expanded by adding other factors such as psychological factors (enjoyment and motivation), social factors (social influence and subjective norm), technological factors (system performance and system accessibility) and organisational factors (facilitating condition and technical support). This conceptual paper was prepared to tested on how they affect students' acceptance towards educational video sharing sit

End-to-end resource management for federated delivery of multimedia services

Recently, the Internet has become a popular platform for the delivery of multimedia content. Currently, multimedia services are either offered by Over-the-top (OTT) providers or by access ISPs over a managed IP network. As OTT providers offer their content across the best-effort Internet, they cannot offer any Quality of Service (QoS) guarantees to their users. On the other hand, users of managed multimedia services are limited to the relatively small selection of content offered by their own ISP. This article presents a framework that combines the advantages of both existing approaches, by dynamically setting up federations between the stakeholders involved in the content delivery process. Specifically, the framework provides an automated mechanism to set up end-to-end federations for QoS-aware delivery of multimedia content across the Internet. QoS contracts are automatically negotiated between the content provider, its customers, and the intermediary network domains. Additionally, a federated resource reservation algorithm is presented, which allows the framework to identify the optimal set of stakeholders and resources to include within a federation. Its goal is to minimize delivery costs for the content provider, while satisfying customer QoS requirements. Moreover, the presented framework allows intermediary storage sites to be included in these federations, supporting on-the-fly deployment of content caches along the delivery paths. The algorithm was thoroughly evaluated in order to validate our approach and assess the merits of including intermediary storage sites. The results clearly show the benefits of our method, with delivery cost reductions of up to 80 % in the evaluated scenario

A cheap feature selection approach for the K -means algorithm

The increase in the number of features that need to be analyzed in a wide variety of areas, such as genome sequencing, computer vision or sensor networks, represents a challenge for the K-means algorithm. In this regard, different dimensionality reduction approaches for the K-means algorithm have been designed recently, leading to algorithms that have proved to generate competitive clusterings. Unfortunately, most of these techniques tend to have fairly high computational costs and/or might not be easy to parallelize. In this work, we propose a fully-parellelizable feature selection technique intended for the K-means algorithm. The proposal is based on a novel feature relevance measure that is closely related to the K-means error of a given clustering. Given a disjoint partition of the features, the technique consists of obtaining a clustering for each subset of features and selecting the m features with the highest relevance measure. The computational cost of this approach is just O(m · max{n · K, log m}) per subset of features. We additionally provide a theoretical analysis on the quality of the obtained solution via our proposal, and empirically analyze its performance with respect to well-known feature selection and feature extraction techniques. Such an analysis shows that our proposal consistently obtains results with lower K-means error than all the considered feature selection techniques: Laplacian scores, maximum variance, multi-cluster feature selection and random selection, while also requiring similar or lower computational times than these approaches. Moreover, when compared to feature extraction techniques, such as Random Projections, the proposed approach also shows a noticeable improvement in both error and computational time.BERC 2014-201