Media use predictors of online political efficacy among Internet users in five Arab countries

Informed by research on the uses and gratifications of the Internet for political activity, this study examines predictors of online political efficacy among Internet users in five Arab countries (N?=?4029): Saudi Arabia, Tunisia, Lebanon, Qatar, and the U.A.E. As predicted, variables presumed to be associated with political dissidence in Arab countries – unemployment, distrust of mainstream media, young age, and feeling country is on the wrong track – were not positively associated with online political efficacy. Counter to what was hypothesized, however, Internet reliance and social media use were not strongly or consistently associated with online political efficacy in the five countries. Rather, the strongest predictors of efficacy were a belief in news media credibility, print media use (newspapers, magazines, and books), belief in the reliability of online information, and tolerance of free speech online. Findings align with some prior research on predictors of online political efficacy, but not with common presumptions of the role the Internet plays among Arab publics.     

Online learning for quantile regression and support vector regression

We consider for quantile regression and support vector regression a kernel-based online learning algorithm associated with a sequence of insensitive pinball loss functions. Our error analysis and derived learning rates show quantitatively that the statistical performance of the learning algorithm may vary with the quantile parameter τ$\tau$. In our analysis we overcome the technical difficulty caused by the varying insensitive parameter introduced with a motivation of sparsity.     

The Impact of the Online Channel on Retailers' Performances: An Empirical Evaluation

The use of the Internet as an additional sales channel offers traditional retailers opportunities to reach expanded markets while improving the efficiency of their operations. Although the potential benefits of the online channel are clear, there are significant variations in the scope and depth of online channel use among retailers. Drawing from data on more than 100 publicly traded companies, this study examines the impact of online‐channel use on retailers’ performance. The results suggest that the online channel provides significant improvements in sales, cost, inventory, and return on investments. In addition, we find that the timing of online‐channel adoption does not play a significant role in performance improvement, but having a local store presence does.     

Selective responsiveness: Online public demands and government responsiveness in authoritarian China

The widespread use of information and communication technology (ICT) has reshaped the public sphere in the digital era, making online forums a new channel for political participation. Using big data analytics of full records of citizen-government interactions from 2008 to early 2014 on a nationwide political forum, we find that authoritarian China is considerably responsive to citizens' demands with a rapid growth of response rate; however, government responsiveness is highly selective, conditioning on actors' social identities and the policy domains of their online demands. Results from logistic and duration models suggest that requests which made by local citizens, expressed collectively, focused on the single task issue, and are closely related to economic growth are more likely to be responded to. These strategies adopted by Chinese provincial leaders reveal the scope and selectivity of authoritarian responsiveness.     

A general modeling approach to online optimization with lookahead

A vast number of real world problems are coined by an information release over time and the related need for repetitive decision making over time. Optimization problems arising in this context are called online since decisions have to be made although not all data is known. Due to technological advances, algorithms may also resort to a limited preview (lookahead) on future events. We first embed the paradigm of online optimization with lookahead into the theory of optimization and develop a concise understanding of lookahead. We further find that the effect of lookahead can be decomposed into an informational and a processual component. Based on analogies to discrete event systems, we then formulate a generic modeling framework for online optimization with lookahead and derive a classification scheme which facilitates a thorough categorization of different lookahead concepts. After an assessment of performance measurement approaches with relevance to practical needs, we conduct a series of computational experiments which illustrate how the general concept of lookahead applies to specific instantiations and how a knowledge pool on lookahead effects in applications can be built up using the general classification scheme.     

Explaining and Forecasting Online Auction Prices and Their Dynamics Using Functional Data Analysis

Online auctions have become increasingly popular in recent years, and as a consequence there is a growing body of empirical research on this topic. Most of that research treats data from online auctions as cross-sectional, and consequently ignores the changing dynamics that occur during an auction. In this article we take a different look at online auctions and propose to study an auction's price evolution and associated price dynamics. Specifically, we develop a dynamic forecasting system to predict the price of an ongoing auction. By dynamic, we mean that the model can predict the price of an auction “in progress” and can update its prediction based on newly arriving information. Forecasting price in online auctions is challenging because traditional forecasting methods cannot adequately account for two features of online auction data: (1) the unequal spacing of bids and (2) the changing dynamics of price and bidding throughout the auction. Our dynamic forecasting model accounts for these special features by using modern functional data analysis techniques. Specifically, we estimate an auction's price velocity and acceleration and use these dynamics, together with other auction-related information, to develop a dynamic functional forecasting model. We also use the functional context to systematically describe the empirical regularities of auction dynamics. We apply our method to a novel set of Harry Potter and Microsoft Xbox data and show that our forecasting model outperforms traditional methods.     

Online organization–public relationships: An experience-centered approach

This paper advocates an experience-centered approach to public relations websites. A conceptual framework of the public relations website experience is proposed. The framework is grounded in communication and reader-response theory, and integrates organization–public relationship research as well as website usability findings. A new research protocol, website experience analysis, is derived from the conceptual framework. Applications of website experience analysis in public relations scholarship and practice are discussed.     

Mapping social dynamics on Facebook: The Brexit debate

Nowadays users get informed and shape their opinion through social media. However, the disintermediated access to contents does not guarantee quality of information. Selective exposure and confirmation bias, indeed, have been shown to play a pivotal role in content consumption and information spreading. Users tend to select information adhering (and reinforcing) their worldview and to ignore dissenting information. This pattern elicits the formation of polarized groups – i.e., echo chambers – where the interaction with like-minded people might even reinforce polarization. In this work we address news consumption around Brexit in UK on Facebook. In particular, we perform a massive analysis on more than 1 million users interacting with Brexit related posts from the main news providers between January and July 2016. We show that consumption patterns elicit the emergence of two distinct communities of news outlets. Furthermore, to better characterize inner group dynamics, we introduce a new technique which combines automatic topic extraction and sentiment analysis. We compare how the same topics are presented on posts and the related emotional response on comments finding significant differences in both echo chambers and that polarization influences the perception of topics. Our results provide important insights about the determinants of polarization and evolution of core narratives on online debating.     

Online monitoring with local smoothing methods and adaptive ridging

We consider online monitoring of sequentially arising data as e.g. met in clinical information systems. The general focus thereby is to detect breakpoints, i.e. timepoints where the measurement series suddenly changes the general level. The method suggested is based on local estimation. In particular, local linear smoothing is combined by ridging with local constant smoothing. The procedure is demonstrated by examples and compared with other available online monitoring routines.