Crowdsourced real-world sensing: sentiment analysis and the real-time web

The advent of the real-time web is proving both challeng- ing and at the same time disruptive for a number of areas of research, notably information retrieval and web data mining. As an area of research reaching maturity, sentiment analysis oers a promising direction for modelling the text content available in real-time streams. This paper reviews the real-time web as a new area of focus for sentiment analysis and discusses the motivations and challenges behind such a direction

Real-time inflation forecasting in a changing world

This paper revisits inflation forecasting using reduced form Phillips curve forecasts, i.e., inflation forecasts using activity and expectations variables. We propose a Phillips curve-type model that results from averaging across different regression specifications selected from a set of potential predictors. The set of predictors includes lagged values of inflation, a host of real activity data, term structure data, nominal data and surveys. In each of the individual specifications we allow for stochastic breaks in regression parameters, where the breaks are described as occasional shocks of random magnitude. As such, our framework simultaneously addresses structural change and model certainty that unavoidably affects Phillips curve forecasts. We use this framework to describe PCE deflator and GDP deflator inflation rates for the United States across the post-WWII period. Over the full1960-2008 sample the framework indicates several structural breaks across different combinations of activity measures. These breaks often coincide with, amongst others, policy regime changes and oil price shocks. In contrast to many previous studies, we find less evidence for autonomous variance breaks and inflation gap persistence. Through a \\textit{real-time} out-of-sample forecasting exercise we show that our model specification generally provides superior one-quarter and one-year ahead forecasts for quarterly inflation relative to a whole range of forecasting models that are typically used in the literature.Bayesian model averaging;structural breaks;real-time data;model uncertainty;Phillips correlations;inflation forecasting

Real-Time, Real World Learning—Capitalising on Mobile Technology

This chapter explores the adoption of Web 2.0 technologies to promote active learning by students and to both mediate and enhance classroom instruction. Web 2.0 refers to open source, web-enabled applications (apps) that are driven by user-manipulated and user-generated content (Kassens-Noor, 2012). These apps are often rich in user participation, have dynamic content, and harness the collective intelligence of users (Chen, Hwang, & Wang, 2012). As such, these processes create “active, context based, personalised learning experiences” (Kaldoudi, Konstantinidis, & Bamidis, 2010, p. 130) that prioritise learning ahead of teaching. By putting the learner at the centre of the education process educators can provide environments that enhance employability prospects and spark a passion for learning that, hopefully, lasts a lifetime. As such, we critique an active learning approach that makes use of technology such as mobile applications (apps), Twitter, and augmented reality to enhance students’ real world learning. Dunlap and Lowenthal (2009) argue that social media can facilitate active learning as they recreate informal, free-flowing communications that allow students and academics to connect on a more emotional level. Furthermore, their use upskills students in the technical complexities of the digital world and also the specialised discourses that are associated with online participation, suitable for real world learning and working (Fig. 16.1). Three case studies explore the benefits of Web 2.0 processes. The first details the use of Twitter chats to connect students, academics, and industry professionals via online synchronous discussions that offer a number of benefits such as encouraging concise writing from students and maintaining on-going relationships between staff, students, and industry contacts. The second details a location-based mobile app that delivers content to students when they enter a defined geographical boundary linked to an area of a sports precinct. Finally, we explore the use of augmented reality apps to enhance teaching in Human Geography and Urban Studies

Dynamic scaling approach to study time series fluctuations

We propose a new approach for properly analyzing stochastic time series by mapping the dynamics of time series fluctuations onto a suitable nonequilibrium surface-growth problem. In this framework, the fluctuation sampling time interval plays the role of time variable, whereas the physical time is treated as the analog of spatial variable. In this way we found that the fluctuations of many real-world time series satisfy the analog of the Family-Viscek dynamic scaling ansatz. This finding permits to use the powerful tools of kinetic roughening theory to classify, model, and forecast the fluctuations of real-world time series.Comment: 25 pages, 7 figures, 1 tabl

A Real-World Negotiation Task for Real-time (Synchronous) Learning

This paper describes a negotiation task designed to introduce negotiation concepts to business students experientially in a manner that also has real consequences for the students. Students negotiate for a variety of actual items that they can take home or use in other ways. Furthermore, the task, previously done only in face-to-face classes, was recently reorganized to include both in person and remotely attending students. Both in person and remote students participate in the process and debriefing, and typically report that they enjoyed the exercise and felt engaged. This activity helps answer the need to provide complex and messy experiences during synchronous instruction that can help achieve the same gains typically provided by experiential learning within the in person classroom