Forecasting Popularity of Videos using Social Media

This paper presents a systematic online prediction method (Social-Forecast) that is capable to accurately forecast the popularity of videos promoted by social media. Social-Forecast explicitly considers the dynamically changing and evolving propagation patterns of videos in social media when making popularity forecasts, thereby being situation and context aware. Social-Forecast aims to maximize the forecast reward, which is defined as a tradeoff between the popularity prediction accuracy and the timeliness with which a prediction is issued. The forecasting is performed online and requires no training phase or a priori knowledge. We analytically bound the prediction performance loss of Social-Forecast as compared to that obtained by an omniscient oracle and prove that the bound is sublinear in the number of video arrivals, thereby guaranteeing its short-term performance as well as its asymptotic convergence to the optimal performance. In addition, we conduct extensive experiments using real-world data traces collected from the videos shared in RenRen, one of the largest online social networks in China. These experiments show that our proposed method outperforms existing view-based approaches for popularity prediction (which are not context-aware) by more than 30% in terms of prediction rewards

On Factors Affecting the Usage and Adoption of a Nation-wide TV Streaming Service

Using nine months of access logs comprising 1.9 Billion sessions to BBC iPlayer, we survey the UK ISP ecosystem to understand the factors affecting adoption and usage of a high bandwidth TV streaming application across different providers. We find evidence that connection speeds are important and that external events can have a huge impact for live TV usage. Then, through a temporal analysis of the access logs, we demonstrate that data usage caps imposed by mobile ISPs significantly affect usage patterns, and look for solutions. We show that product bundle discounts with a related fixed-line ISP, a strategy already employed by some mobile providers, can better support user needs and capture a bigger share of accesses. We observe that users regularly split their sessions between mobile and fixed-line connections, suggesting a straightforward strategy for offloading by speculatively pre-fetching content from a fixed-line ISP before access on mobile devices.Comment: In Proceedings of IEEE INFOCOM 201

Investigating the use of podcasts to support basic and intermediary skills development, in excel, at undergraduate and foundation levels

Audio or video podcasts can offer students different ways of learning and can add value by providing access to learning materials ‘on the move’. This paper presents an initial investigation into the use of excel podcasts for undergraduate and foundation level students in different faculties at a single university. The podcasts were shown in lecture and seminar settings and uploaded to an online server for all students to access in their own time; either via a web browser or portable video player such as an iPod. Results for the on-going study were positive with the majority of students using them for developing their understanding of excel and exam revision. Students expressed an interest in having similar supplementary learning materials for other modules. Such conclusions drawn from this study highlight the need to investigate the use of podcasts further in the teaching and learning environment