Conceptualizing and Measuring the Social Uses of the Internet: The Case of Personal Web Sites

The researchers working on information society measures have recently started to consider how people use the Internet not only as a tool but also as a platform for social relations. As the ultimate objective of information society measures is to provide reliable guidelines for social policy, finding criteria for separating “positive” uses of Internet from “negative” ones is an important task. This article employs the concepts of communicative and strategic action as defined by Habermas for that purpose. A general conceptual typology of three communicative and three strategic social uses of Internet is offered, which can be applied to measure uses of any of the Internet's interactive services. A specific empirical application to social uses of personal web sites demonstrates that the deduced measurement instruments achieve satisfying levels of validity and reliability.     

Supervised sentiment analysis in Czech social media

This article describes in-depth research on machine learning methods for sentiment analysis of Czech social media. Whereas in English, Chinese, or Spanish this field has a long history and evaluation datasets for various domains are widely available, in the case of the Czech language no systematic research has yet been conducted. We tackle this issue and establish a common ground for further research by providing a large human-annotated Czech social media corpus. Furthermore, we evaluate state-of-the-art supervised machine learning methods for sentiment analysis. We explore different pre-processing techniques and employ various features and classifiers. We also experiment with five different feature selection algorithms and investigate the influence of named entity recognition and preprocessing on sentiment classification performance. Moreover, in addition to our newly created social media dataset, we also report results for other popular domains, such as movie and product reviews. We believe that this article will not only extend the current sentiment analysis research to another family of languages, but will also encourage competition, potentially leading to the production of high-end commercial solutions.     

新冠肺炎疫情期间大学生居家体育锻炼行为与促进研究

居家体育锻炼作为全民健身和终身体育的重要形式,是新冠肺炎疫情期间和特殊天气条件下大众的主要锻炼方式。通过对新冠肺炎疫情期间47所高校4 416名大学生居家体育锻炼情况的调查,分析了疫情期间大学生居家体育锻炼的行为,结果显示:(1)82.98%的大学生达不到每天锻炼一小时的标准,男生居家体育锻炼频率、强度、时长均显著高于女生,受疫情影响严重的华中地区大学生居家体育锻炼频率和时长均最低,力量练习、跑步、跳绳是大学生主要的居家体育锻炼项目;(2)大学生居家体育锻炼认知与行为之间存在正向相关关系,但知行不一的现象仍普遍存在;(3)内部动机是大学生居家体育锻炼的主要动机,外在因素是阻碍大学生居家体育锻炼的主要因素,学校体育教学、网络媒体、运动APP和公众号是大学生居家体育锻炼认知的主要途径,基于网络课堂的居家体育学习方式中,通过教学视频进行学习的效果最好,网络直播效果最差;(4)父母与子女间的居家体育锻炼行为具有较强的代际互动关系。建议建立基于网络平台的家校社联动机制,促进大学生居家体育锻炼可持续发展。     

The reasons students choose teaching professions

The purpose of the paper is to present the reasons students at the Faculty of Education in Maribor, Slovenija, chose pedagogy for their study direction, and therefore becoming a teacher. A total 237 second‐year students of the academic year 2003/04 were included in the research. Of the five groups of reasons for choosing this program (altruistic, material, self‐realization and alternative, and reasons arising from aspiration stereotype), students most often asserted self‐realization, including: teaching provides a useful public function for the whole society; as a teacher, I can be an example to children and young people; this profession will give me a chance for professional development during my whole career; and teaching will enable me to use all my abilities (and talents—for example, music, verbal, dancing, etc.) Important differences were found to exist between students of different disciplines.     

A Tutorial in Bayesian Potential Outcomes Mediation Analysis

Statistical mediation analysis is used to investigate intermediate variables in the relation between independent and dependent variables. Causal interpretation of mediation analyses is challenging because randomization of subjects to levels of the independent variable does not rule out the possibility of unmeasured confounders of the mediator to outcome relation. Furthermore, commonly used frequentist methods for mediation analysis compute the probability of the data given the null hypothesis, which is not the probability of a hypothesis given the data as in Bayesian analysis. Under certain assumptions, applying the potential outcomes framework to mediation analysis allows for the computation of causal effects, and statistical mediation in the Bayesian framework gives indirect effects probabilistic interpretations. This tutorial combines causal inference and Bayesian methods for mediation analysis so the indirect and direct effects have both causal and probabilistic interpretations. Steps in Bayesian causal mediation analysis are shown in the application to an empirical example.