Audience management,online turbulence and lurking in social networking services: A transactional process of stress perspective

In social networking services (SNSs), users’ unclear understanding of the large and invisible audience increases the chances of online turbulence, which is a key source of SNS-induced stress. This growing phenomenon has gained increasing attention in academia and industry due to the undesirable consequences for users and SNS platforms. In this study, we draw from the transactional model of stress to examine how audience management strategies impact online turbulence and lead to neglected unintended audience concern and lurking. We also investigate the role of self-monitoring as a stress inhibitor. We test our model with data collected from 301 SNS users. The results show that the four types of audience management strategies have different effects on online turbulence, which significantly impacts neglected unintended audience concern especially when users have high self-monitoring skills. We believe that this work contributes, both from scientific and practical standpoints, to the understanding of the interventions and stressful responses of online turbulence in SNSs.     

科学数据共享研究现状与展望

[目的/意义] 分析科学数据共享的研究现状及其发展趋势，为促进国内相关理论研究与实践进展提供参考。[方法/过程] 通过对国内外文献进行梳理和归纳，提炼出与科学数据共享研究相关的三大类主题：科学数据共享发展需求、科学数据管理共享模式与机制和科学数据服务共享度与服务效果，并构建科学数据共享管理分析框架，该框架体现上述三类主题之间的因果关联，强调科学数据共享管理与服务的合理面、重要度、关键性。[结果/结论] 国内外现有研究尚存不足，未来研究在加强实证研究、着眼管理机制完善的同时，还需注重共享服务模式的创新。     

Tracking user-role evolution via topic modeling in community question answering

Community question answering (CQA) services that enable users to ask and answer questions are popular on the internet. Each user can simultaneously play the roles of asker and answerer. Some work has aimed to model the roles of users for potential applications in CQA. However, the dynamic characteristics of user roles have not been addressed. User roles vary over time. This paper explores user representation by tracking user-role evolution, which could enable several potential applications in CQA, such as question recommendation. We believe this paper is the first to track user-role evolution and investigate its influence on the performance of question recommendation in CQA. Moreover, we propose a time-aware role model (TRM) to effectively track user-role evolution. With different independence assumptions, two variants of TRM are developed. Finally, we present the TRM-based approach to question recommendation, which provides a mechanism to naturally integrate the user-role evolution with content relevance between the answerer and the question into a unified probabilistic framework. Experiments using real-world data from Stack Overflow show that (1) the TRM is valid for tracking user-role evolution, and (2) compared with baselines utilizing role based methods, our TRM-based approach consistently and significantly improves the performance of question recommendation. Hence, our approach could enable several potential applications in CQA.     

Data Visualization as Participatory Research: A Model for Digital Collections to Inspire User-Driven Research

The emerging creative practices of digital collections are increasing in prominence thanks to the access to, and appeal of, data visualizations. Interactive data visualizations, such as a map, timeline, chart, graph, or artist’s rendering of data, enable users to pursue their own research interests within a collection and participate in new areas of research. Use of these data visualizations enhances the scope of the collection and enables a modern and diverse thematic collection to emerge. This article considers the question: Does the addition of data visualizations affect how users participate with a research collection? Impacts on collection development, application of user-centered design, and analysis of users’ exploratory strategies are discussed. Examples of data visualizations used in digital humanities projects and digital collections are presented as case studies.     

Digital Module 16: Longitudinal Data Analysis

In this digital ITEMS module, Dr. Jeffrey Harring and Ms. Tessa Johnson introduce the linear mixed effects (LME) model as a flexible general framework for simultaneously modeling continuous repeated measures data with a scientifically defensible function that adequately summarizes both individual change as well as the average response. The module begins with a nontechnical overview of longitudinal data analyses drawing distinctions with cross-sectional analyses in terms of research questions to be addressed. Nuances of longitudinal designs, timing of measurements, and the real possibility of missing data are then discussed. The three interconnected components of the LME model—(1) a model for individual and mean response profiles, (2) a model to characterize the covariation among the time-specific residuals, and (3) a set of models that summarize the extent that individual coefficients vary—are discussed in the context of the set of activities comprising an analysis. Finally, they demonstrate how to estimate the linear mixed effects model within an open-source environment (R). The digital module contains sample R code, diagnostic quiz questions, hands-on activities in R, curated resources, and a glossary.     

Digital Module 05: Diagnostic Measurement—The G‐DINA Framework https://ncme.elevate.commpartners.com

In this ITEMS module, we introduce the generalized deterministic inputs, noisy “and” gate (G‐DINA) model, which is a general framework for specifying, estimating, and evaluating a wide variety of cognitive diagnosis models. The module contains a nontechnical introduction to diagnostic measurement, an introductory overview of the G‐DINA model, as well as common special cases, and a review of model‐data fit evaluation practices within this framework. We use the flexible GDINA R package, which is available for free within the R environment and provides a user‐friendly graphical interface in addition to the code‐driven layer. The digital module also contains videos of worked examples, solutions to data activity questions, curated resources, a glossary, and quizzes with diagnostic feedback.     

大数据时代个人信息保护研究综述

[目的/意义]通过梳理大数据时代个人信息保护的研究文献，为未来研究个人信息保护提供借鉴。[方法/过程]利用CNKI数据库，系统收集关于大数据时代个人信息保护的国内核心期刊论文，对文献的内容进行分析，归纳研究主题，并进行论述。[结果/结论]将现有的研究成果按主题分为5类：国内外实践经验研究、基本理论问题研究、个人信息保护机制探索、个人信息安全风险与应对、相关法律法规研究，评述现有研究的不足之处并对未来的研究方向提出展望。     

新疆生产建设兵团普通高校校园足球推广与发展经验探讨

校园足球运动是夯实足球人才的根基,对学生智力发展及身体健康水平的改善与提升具有重要作用。本文以新疆生产建设兵团所属的塔里木大学为具体研究对象,梳理了塔里木大学校园足球运动的发展模式,并在此基础上分析、探讨了塔里木大学校园足球运动推广、管理与发展的成功经验与模式,以及制约塔里木大学校园足球进一步发展的瓶颈。     

Using the Information Structure Model to Compare Profile-Based Information Filtering Systems

In the IR field it is clear that the value of a system depends on the cost and benefit profiles of its users. It would seem obvious that different users would prefer different systems. In the TREC-9 filtering track, systems are evaluated by a utility measure specifying a given cost and benefit. However, in the study of decision systems it is known that, in some cases, one system may be unconditionally better than another. In this paper we employ a decision theoretic approach to find conditions under which an Information Filtering (IF) system is unconditionally superior to another for all users regardless of their cost and benefit profiles.It is well known that if two IF systems have equal precision the system with better recall will be preferred by all users. Similarly, with equal recall, better precision is universally preferred. We confirm these known results and discover an unexpected dominance relation in which a system with lower recall will be universally preferred provided its precision is sufficiently higher.     

青年女子排球运动员弹跳力模型的研制

为了探索发展青年女子排球运动员弹跳力的有效途径 ,以四川青年女子排球队 9名运动员和重庆女子排球队 13名运动员为研究对象 ,对有关弹跳力的多种因素进行了相关、多元回归分析 ,进而制定出发展弹跳力的回归模型 ,为青年女子排球运动员弹跳力训练提供参考