Structurally embedded news consumption on mobile news applications

Seek for Success: A Visualization Approach for Understanding the Dynamics of Academic Careers

How to achieve academic career success has been a long-standing research question in social science research. With the growing availability of large-scale well-documented academic profiles and career trajectories, scholarly interest in career success has been reinvigorated, which has emerged to be an active research domain called the Science of Science (i.e., SciSci). In this study, we adopt an innovative dynamic perspective to examine how individual and social factors will influence career success over time. We propose ACSeeker, an interactive visual analytics approach to explore the potential factors of success and how the influence of multiple factors changes at different stages of academic careers. We first applied a Multi-factor Impact Analysis framework to estimate the effect of different factors on academic career success over time. We then developed a visual analytics system to understand the dynamic effects interactively. A novel timeline is designed to reveal and compare the factor impacts based on the whole population. A customized career line showing the individual career development is provided to allow a detailed inspection. To validate the effectiveness and usability of ACSeeker, we report two case studies and interviews with a social scientist and general researchers.

BoRe

News recommendation has become an essential way to help readers discover interesting stories. While a growing line of research has focused on modeling reading preferences for news recommendation, they neglect the instability of reader consumption behaviors, i.e., consumption behaviors of readers may be influenced by other factors in addition to user interests, which degrades the recommendation effectiveness of existing methods. In this article, we propose a probabilistic generative model, BoRe, where user interests and crowd effects are used to adapt to the instability of reader consumption behaviors, and reading sequences are utilized to adapt user interests evolving over time. Further, the extreme sparsity problem in the domain of news severely hinders accurately modeling user interests and reading sequences, which discounts BoRe’s ability to adapt to the instability. Accordingly, we leverage domain-specific features to model user interests in the situation of extreme sparsity. Meanwhile, we consider groups of users instead of individuals to capture reading sequences. Besides, we study how to reduce the computation to allow online application. Extensive experiments have been conducted to evaluate the effectiveness and efficiency of BoRe on real-world datasets. The experimental results show the superiority of BoRe, compared with the state-of-the-art competing methods.

Role of Personalization in Continuous Use Intention of Mobile News Apps in India: Extending the UTAUT2 Model

The aim of this study was to empirically examine the extended unified theory of acceptance and use of technology 2 (UTAUT2) model by adding “personalization” as one of the antecedents, as well as a moderator to determine the key factors for the continuous use intention of mobile news applications (apps). For this study, an online and manual sample survey of 309 respondents, who had used the news app earlier, was collected and analyzed, using quantitative methods such as explanatory and confirmatory factor analysis, structural equation modeling, and Hayes process for finding moderating effects among variables. The findings of the direct effect demonstrated that performance expectancy (PE) has the most influential effect on continuous use intention, followed by habit (HT), hedonic motivation (HM), and facilitating conditions (FC). Furthermore, the outcome of tests for the moderating effect of personalization between UTAUT2 constructs and continuous use intention (CUI) showed that personalization has a significant moderating effect on performance expectancy and habit. Therefore, this research establishes the key role of PE, HT, HM, and FC as main factors that trigger the users’ continuous use intention of news apps and provides an integrated framework to assess the moderating effect of personalization on technology acceptance. The findings of the research expand the existing literature on news applications and provide foundation for future research studies in the area of mobile news apps.

University students’ mobile news consumption activities and evaluative/affective reactions to political news during election campaigns: A diary study

Smartphones have now become routinely used tools for people’s everyday life news consumption. This article presents a diary study involving 49 university students in the United States documenting their process of consuming political news via smartphones. Participants reported the information about the news and used 23 pairs of semantic differential scales to evaluate and express their affective reactions to the news. Among 176 political news items submitted, the highest proportion was election news. Significant demographic differences were found in participants’ choices of semantic adjectives. Differences were also found in proportions of election and non-election news submitted. A higher proportion of election news was marked as ‘light’, ‘stale’, ‘shallow’, ‘worthless’, ‘dishonest’ and ‘harmful’ than those labelled for non-election news. Findings provide valuable insights into university students’ political news mobile consumption activities and their assessment and sentiment surrounding election news and general political news during the 2016 US Presidential election campaign.