Engagement with news on Twitter: insights from Australia and Korea

ABSTRACT

This study examines the patterns of news engagement among news consumers with different political affiliation and cultural background. We use computational methods and data from Twitter in a cross-country comparison of engagement with six online news sources in Australia and South Korea. For our analysis, we used a subset of Twitter users who retweeted at least one political story during the period of collection, and for whom we were able to predict political affiliation using correspondence analysis and data on Twitter follower ties to politicians. We find that right-wing Australian retweeters are more intense in their news engagement, compared with their left-wing counterparts, whereas in South Korea it was the opposite. Australian right-wing political retweeters have more diverse information sources, while there was no difference in information diversity between the right and left in South Korea. We discuss how the political situation in South Korea at the time of data collection may have affected our analysis. We emphasise the methodological contributions of our research and its connection to on-going research into the behavioural foundations of ‘filter bubbles’.     

Dating in the time of “relational filter bubbles”: exploring imaginaries,perceptions and tactics of Italian dating app users

ABSTRACT

In this paper, we address algorithmic imaginary, perception and tactics of Italian dating apps users. Little attention has hitherto been devoted to the ways in which the algorithms employed by mobile dating platforms (to rate users, to manage user visibility, to arrange results) might contrast, or enhance, people’s homophily. Our goal is to explore whether and how mobile dating algorithms modify the perception of what we define as “relational filter bubbles”; and whether, and how, users believe dating algorithms reshape (extend or limit) the heterogeneity of their intimate interactions. The paper builds on literature addressing online dating, the datafication of society, the rise of the so-called quantified self, and of the algorithmic culture. We organized 4 focus groups involving Italian dating apps users, who reported a variety of sexual orientations and of dating apps usage. Overall, while dating apps’ algorithms operate in an opaque way, participants developed an “algorithmic imaginary”. Moreover, they appreciate the role of mobile dating apps in reinforcing their relational homophily (their tendency to like people that are “similar” to them), whilst, at the same time, mainly using these apps for increasing the diversity of their intimate interactions in terms of extending their preexisting networks.     

图书馆网络安全解决方案

提出了一个采用免费而功能强大的linux系统，来架设包过滤防火墙，确保网络安全，预防黑客入侵的解决方案。     

The NICE OECD countries’ geographic search filters: Part 1—methodology for developing the draft MEDLINE and Embase (Ovid) filters

Objective:There are no existing validated search filters for the group of 37 Organisation for Economic Co-operation and Development (OECD) countries. This study describes how information specialists from the United Kingdom''s National Institute for Health and Care Excellence (NICE) developed and evaluated novel OECD countries’ geographic search filters for MEDLINE and Embase (Ovid) to improve literature search effectiveness for evidence about OECD countries.Methods:We created the draft filters using an alternative approach to standard filter construction. They are composed entirely of geographic subject headings and are designed to retain OECD country evidence by excluding non-OECD country evidence using the NOT Boolean operator. To evaluate the draft filters’ effectiveness, we used MEDLINE and Embase literature searches for three NICE guidelines that retrieved >5,000 search results. A 10% sample of the excluded references was screened to check that OECD country evidence was not inadvertently excluded.Results:The draft MEDLINE filter reduced results for each NICE guideline by 9.5% to 12.9%. In Embase, search results were reduced by 10.7% to 14%. Of the sample references, 7 of 910 (0.8%) were excluded inadvertently. These references were from a guideline about looked-after minors that concerns both OECD and non-OECD countries.Conclusion:The draft filters look promising—they reduced search result volumes while retaining most OECD country evidence from MEDLINE and Embase. However, we advise caution when using them in topics about both non-OECD and OECD countries. We have created final versions of the search filters and will validate them in a future study.     

基于协同过滤算法的高校图书馆图书推荐系统研究

针对当前高校图书馆主动式图书推荐服务存在的对服务对象信息需求挖掘、分析不足的问题,提出构建基于协同过滤算法的个性化图书推荐系统。通过引入读者专业、角色、学历、借阅记录等影响和反映读者信息需求的因素构建读者特征模型,基于该模型采用优化的协同过滤算法挖掘读者信息需求并产生个性化图书推荐信息,并通过实验证明该方法的有效性和实用性。     

基于旁路监听的数字资源评估系统的设计与实现

[目的/意义]为解决图书馆数字资源群的数据统计与效益分析问题,设计基于网络旁路监听方式进行数据使用统计和分析的评估系统.[方法/过程]引入旁路监听技术方式探讨图书馆数字资源绩效分析的设计思路,以大连理工大学图书馆数字资源评估系统为例,阐述系统的技术支撑原理、设计思路及功能模块解决方案.[结果/结论]通过具体实践证明,基于旁路监听方式的数字资源评估系统可以对数字资源群组实施准确监控,在不影响原有的网络拓扑和使用习惯的前提之下,准确识别并记录读者对数字资源的访问下载情况,最终根据数据库中的访问日志对数字资源的服务绩效进行有效评估.     

稳定的反Q滤波方法及实际应用

在反Q滤波中．普遍存在着两个重要的问题．一个是数字的不稳定．一个是效率不高。Y-ghua Wang在他的《一种稳定高效的反Q滤波方法》中．提出一种稳定的反Q滤波方法。本文对他方法进行了跟踪研究，在从算法上实现的基础上．通过对实际生产数据进行验证，证明了谈方法的稳定性和提高资料质量的效果。     

The Form of Content Personalisation at Mainstream,Transatlantic News Outlets: 2010–2016

ABSTRACT

In October 2016 the New York Times announced new content personalisation features for its websites and mobile apps, with the promise of “much more to come”. This article examines such developments via a quantitative content analysis of personalisation features at 15 major news outlets in the United Kingdom, the United States, and Germany and via interviews with senior editorial staff at some of those outlets. Both websites and mobile apps were studied, allowing comparisons to be made across platforms as well as geographically. Longitudinal comparisons were also made against an equivalent survey conducted in 2010. The results reveal significant changes over the last six years with, for example, rises in personalisation on mobile platforms, falls in the use of recommendations by friends, and convergence and commodification in content recommendation platforms. This study provides evidence to support continued debates on classical concerns such as selective exposure, but also raises new concerns about the effects of personalisation, including data protection and security issues.     

融合统计学习和语义过滤的ADR信号抽取模型构建研究

[目的/意义]社交媒体的出现为医疗健康数据的收集提供了新的途径,应用自然语言处理技术从社交媒体中抽取患者报告的ADR（Adverse Drug Reaction,药物不良反应）信号对于改善药物不良反应监测的临床和科学知识具有很大的潜力。然而,从社会媒体中提取患者报告的ADR信号仍然面临重大挑战。为此,开发一个利用高级自然语言处理技术从健康主题社交媒体中抽取ADR信号的研究模型。[方法/过程]该模型首先采用基于多词典源匹配的方法,从嘈杂的社交媒体中识别医学实体;然后采用最短依存路径核函数为基础的统计学习方法提取药物不良事件;并利用药品安全数据库的语义知识过滤药物的治疗和适用症信息以及否定的药物不良事件;最后,对报告源进行分类剔除传闻等噪音信息。[结果/结论]通过收集糖尿病论坛上的数据对模型的有效性进行验证,结果显示该模型的每一部分都有助于其整体性能的提升。     

基于共引分析及Fisher判别法的网站过滤及扩展研究

从大量动态无序的非结构化网络资源中发现主题或学科核心网站,是学科导航、个性化服务及信息推送的基础。但在核心网站评选过程中,存在两个问题,一是通过搜索引擎获得的相关网站集合中,有大量非相关网站,直接影响核心网站评选的效率;二是当前核心网站研究中均未考虑到网站的扩展问题。为此,本文将Web共引分析技术引入非相关网站过滤,提出利用Fisher判别法实现核心网站扩展,并给出了扩展可靠性检验方法。