Eye-tracking methodology: exploring the processing of question formats in web surveys

In social research, the use of agree/disagree (A/D) questions is a popular method for measuring attitudes. Research has shown that A/D questions require complex cognitive processing and are susceptible to response bias. Thus, some researchers recommend the use of item-specific (IS) questions. This study examines the processing of A/D and IS questions, using eye-tracking methodology. By recording respondents’ eye movements, how respondents process survey questions can be evaluated. The results reveal that IS questions cause more and longer fixations. However, this only applies to the response categories. There are no differences regarding the question stems. Altogether, it seems that IS response categories trigger deeper cognitive processing than A/D response categories.     

The Semantic Web and the BIBFRAME Initiative

The implementation of the semantic web based on linked data principles represents a movement toward transforming the World Wide Web into a database of linked resources, where data may be widely reused and shared. Web services can be enhanced by drawing on semantically aware data made available by a variety of providers. The Bibliographic Framework (BIBFRAME) Initiative seeks to expose rich library metadata to the semantic web, configured so that semantic data from other sources can be incorporated in meeting library user needs.     

新目录学派志:英国文学考据的兴起与没落

通过介绍以沃特·威尔逊·格雷格爵士为代表的目录派三杰，在对文学文本基本物质形态从手稿到印刷文本辨伪和考证批评的基础上，经过厘定文本讹误和四次重要的学术论战，确立了作者的神圣地位，让文学成为绝对科学化的大学学科，但新目录派最终却退出了现代西方文论思潮。观照几乎同时产生于印刷术所带来的学术分裂期的中国乾嘉派，它能够客观对待经学作者，并形成了系统的治学方法，从而稳步发展为今天的朴学大潮，在此视阈下分析并阐明新目录派无法形成体系的原因，恰在于其过分强调科学权威性而漠视了文学本身的独特因素造成的方枘圆凿，藉此提出中国治学方法借鉴于西方文论批评的可行性。     

网红直播带货的伦理审视

随着网络直播的兴起和网红经济的发展,"网红直播带货"成为一种新的重要营销方式,它不仅能满足人们个性化的物质需求,而且能满足个体的社交化情感诉求,但其中所潜藏的诚信缺失和个体消费成瘾等伦理失范问题较为突出。加强个人义利观教育、完善社会诚信监督机制,丰盈个体精神生活、净化社会消费风气,则是解决这些伦理问题的可行性路径。     

Investigating the impact of violations of the “left and top means first” heuristic on response behavior and data quality

ABSTRACT

Web surveys are an established data collection mode that use written language to provide information. The written language is accompanied by visual elements, such as presentation formats and shapes. However, research has shown that visual elements influence response behavior because respondents sometimes use interpretive heuristics to make sense of the visual elements. One such heuristic is the ‘left and top means first’ (LTMF) heuristic, which suggests that respondents tend to believe that a response scale consistently runs from left to right or from top to bottom. We conducted a web survey experiment to investigate how violations of the LTMF heuristic affect response behavior and data quality. For this purpose, a random half of respondents received response options that followed a consistent order and the other half received response options that followed an inconsistent order. The results reveal significantly different response distributions between the two groups. We also found that inconsistently ordered response options significantly increase response times and decrease data quality in terms of criterion validity. We, therefore, recommend using options that follow the design strategies of the LTMF heuristic.     

COWORDS: a probabilistic model for multiple word clouds

Word clouds constitute one of the most popular statistical tools for the visual analysis of text documents because they provide users with a quick and intuitive understanding of the content. Despite their popularity for visualizing single documents, word clouds are not appropriate to compare different text documents. Independently generating word clouds for each document leads to configurations where the same word is typically located in widely different positions. This makes it very difficult to compare two or more word clouds. This paper introduces COWORDS, a new stochastic algorithm to create multiple word clouds, including one for each document. The shared words in multiple documents are placed in the same position in all clouds. Similar documents produce similar and compact clouds, making it easier to simultaneously compare and interpret several word clouds. The algorithm is based on a probability distribution in which the most probable configurations are those with a desirable visual aspect, such as a low value for the total distance between the words in all clouds. The algorithm output is a set of word clouds that are randomly selected from this probability distribution. The selection procedure uses a Markov chain Monte Carlo simulation method. We present several examples that illustrate the performance and visual results that can be obtained by our algorithm.     

Collecting text messages from college students: Evaluating a novel methodology

Social interaction, particularly in older adolescents, increasingly involves computer‐mediated communication. Although studies of public computer‐mediated communication are increasingly common, studies of private text messaging remain rare. As approaches for obtaining such data evolve with technological advances, developmental scientists need designs in which to use such approaches that reduce sampling biases in both participants and text messages. In this study (n = 854; 46% male; 22% African American, 60% European American), we examined selection biases in the participant sample (i.e., factors associated with actual participation), procedural biases in the participant sample (i.e., factors related to failed data capture due to technological or procedural issues), and selection biases in the sample of text messages (i.e., based on self‐reported reasons for texting). Findings from our study suggest that studying human interaction directly through analysis of text message data is not only feasible, but also may be successfully undertaken with minimal biases regarding sample selection and text message selection among those who are engaged in research and engaged in text messaging outside of the study context. However, biases may occur depending on the type of platform (iPhone vs. Android) used by participants for texting.     

热奈特的类文本理论及其效应

杰拉德·热奈特的类文本理论受到中西方学界的关注与重视,一些学者对该理论进行了一定的补充和修正工作,也有不少学者将其用于文学研究。类文本理论视阈下,文学研究的对象为环绕于文本边缘的类文本要素,如作者的署名、序言、献词、题记、版权页信息等。将作者重新纳入批评视野,类文本理论有效修正了文本理论将文本与作者相割裂的不当做法;将出版商引入批评框架,类文本理论不仅拓展了传统文学批评体系,还起到限制读者过度阐释的目的。同时,类文本理论促进了叙事学的进一步发展,使文学研究从文本中心走向文本边缘,并将文学的内部研究和外部研究相结合,从而推进了文学批评理论的发展。鉴于类文本与文本的互存关系,某些类文本和文本之间的界限模糊不清,它们在具备类文本属性的同时,又不乏文本的属性,而这一特性又恰恰佐证了类文本之于文本而存续的重要性。     

iConstruct: virtual disabilities in online settings

The present study focused on cultural constructions of disability in virtual settings, examining specifically how cultural constructions of disability were mediated through the use of online technology. This was accomplished by identifying and analyzing Greek Cypriot online news media articles and online readers’ posts in response to these articles. Findings indicated that disability in these online settings mainly reproduced traditional models of disability, with the charity model being more prevalent. However, there were instances in which dominant discourses of disability were ruptured through the introduction of counter-narratives in users’ posts which echoed the human rights model of disability. Implications for the fields of media, education and disability studies are discussed in relation to the possibility of using digital media to contribute to the challenge of critical media literacy to address issues of diversity and disability.     

新闻美文概念产生的原因及发展趋势

在传统媒体向新兴媒体融合的过程中，以新华社为代表的一批富有创新精神和精品意识的新闻人，在新闻实践中对通讯报道的文本进行文学尝试，作品获中国新闻奖特别奖引发全国关注。据此，提出新闻美文的概念，分析了原因和启示，揭示了新闻美文下一步的趋势，认为新闻美文的时代业已到来。