SNS model for providing social network channels through queries

Users can obtain the information through a basic web searching and find the answer to the questions directly,but maybe the expected answer does not exist.Besides,we do not know the update of new information in time.The online social networking services spread quickly and store many user data,but these data are worth less and may be unreliable answer to users’ questions.Users can obtain the simple answer but can not expect more additional information in knowledge question-answering(QA)system.In this paper,we design the system with the advantages of knowledge QA system,web searching and characteristics of social networking service for providing social network channel based on the query and answer without users’ contact network.The user can obtain real-time answers by the user network interested in users’ querires through the network channel of this system,get the additional information effectively and share it with others in the social network channel in this system.

The Impact of Social Media Networks Websites Usage on Students’ Academic Performance

Social Networks Sites (SNSs) are dominating all internet users’ generations, especially the students’ communities. Consequently, academic institutions are increasingly using SNSs which leads to emerge a crucial question regarding the impact of SNSs on students’ academic performance. This research investigates how and to what degree the use of SNSs affects the students’ academic performance. The current research’s data was conducted by using drop and collect surveys on a large population from the University of Jordan. 366 undergraduate students answered the survey from different faculties at the university. In order to study the impact of SNSs on student’s academic performance, the research hypotheses was tested by using descriptive analysis, T-test and ANOVA. Research results showed that there was a significant impact of SNS on the student’s academic performance. Also, there was a significant impact of SNS use per week on the student’s academic performance, whereas no differences found in the impact of use of SNSs on academic performance due to age, academic achievement, and use per day to most used sites. The findings of this research can be used to suggest future strategies in enhancing student’s awareness in efficient time management and better multitasking that can lead to improving study activities and academic achievements.

Privacy disclosure in Chinese social networking sites:An investigation of Renren users

Purpose:This study was conducted to investigate the current situation of privacy disclosure(in the Chinese social networking sites.Design/methodology/approach:Data analysis was based on profiles of 240 college students on Renren.com,a popular college-oriented social networking site in China.Users’ privacy disclosure behaviors were studied and gender difference was analyzed particularly.Correlation analysis was conducted to examine the relationships among evaluation indicators involving user name,image,page visibility,message board visibility,completeness of education information and provision of personal information.Findings:A large amount of personal information was disclosed via social networking sites in China.Greater percentage of male users than female users disclosed their personal information.Furthermore,significantly positive relationships were found among page visibility,message board visibility,completeness of education information and provision of personal information.Research limitations:Subjects were collected from only one social networking website.Meanwhile,our survey involves subjective judgments of user name reliability,category of profile images and completeness of information.Practical implications:This study will be of benefit for college administrators,teachers and librarians to design courses for college students on how to use social networking sites safely.Originality /value:This empirical study is one of the first studies to reveal the current situation of privacy disclosure in the Chinese social networking sites and will help the research community gain a deeper understanding of privacy disclosure in the Chinese social networking sites.

The Impacts of Social Network Sites on English Teaching and Learning in China

This article mainly studies the impacts of social network sites on English teaching and learning from several aspects,such as the background knowledge of social network sites(SNSs),definition and types,as well as the common use of SNSs in China.The article uses a special view to analyze the possibility of using social network sites on English education.

Improved Key Node Recognition Method of Social Network Based on PageRank Algorithm

The types and functions of social networking sites are becoming more abundant with the prevalence of self-media culture,and the number of daily active users of social networking sites represented by Weibo and Zhihu continues to expand.There are key node users in social networks.Compared with ordinary users,their influence is greater,their radiation range is wider,and their information transmission capabilities are better.The key node users playimportant roles in public opinion monitoring and hot event prediction when evaluating the criticality of nodes in social networking sites.In order to solve the problems of incomplete evaluation factors,poor recognition rate and low accuracy of key nodes of social networking sites,this paper establishes a social networking site key node recognition algorithm(SNSKNIS)based on PageRank(PR)algorithm,and evaluates the importance of social networking site nodes in combination with the influence of nodes and the structure of nodes in social networks.This article takes the Sina Weibo platform as an example,uses the key node identification algorithm system of social networking sites to discover the key nodes in the social network,analyzes its importance in the social network,and displays it visually.

Anomaly Detection in Social Media Texts Using Optimal Convolutional Neural Network

Social Networking Sites(SNSs)are nowadays utilized by the whole world to share ideas,images,and valuable contents by means of a post to reach a group of users.The use of SNS often inflicts the physical and the mental health of the people.Nowadays,researchers often focus on identifying the illegal beha-viors in the SNS to reduce its negative influence.The state-of-art Natural Language processing techniques for anomaly detection have utilized a wide anno-tated corpus to identify the anomalies and they are often time-consuming as well as certainly do not guarantee maximum accuracy.To overcome these issues,the proposed methodology utilizes a Modified Convolutional Neural Network(MCNN)using stochastic pooling and a Leaky Rectified Linear Unit(LReLU).Here,each word in the social media text is analyzed based on its meaning.The stochastic pooling accurately detects the anomalous social media posts and reduces the chance of overfitting.The LReLU overcomes the high computational cost and gradient vanishing problem associated with other activation functions.It also doesn’t stop the learning process when the values are negative.The MCNN computes a specified score value using a novel integrated anomaly detection tech-nique.Based on the score value,the anomalies are identified.A Teaching Learn-ing based Optimization(TLBO)algorithm has been used to optimize the feature extraction phase of the modified CNN and fast convergence is offered.In this way,the performance of the model is enhanced in terms of classification accuracy.The efficiency of the proposed technique is compared with the state-of-art techni-ques in terms of accuracy,sensitivity,specificity,recall,and precision.The proposed MCNN-TLBO technique has provided an overall architecture of 97.85%,95.45%,and 97.55%for the three social media datasets namely Facebook,Twitter,and Reddit respectively.

浅析使用Opensocial开发SNS应用

由Facebook引发的SNS（Social Network Service,社会化网络服务）风靡全球,让我们看到了在新一轮互联网革命中人的力量。＂草根文化＂、＂病毒式传播＂让我们越来越多地感受到,人、社会、网络的结合所产生的巨大价值和无限可能。如何将人的力量最大化,让更多的人参与价值的积聚过程,开放平台无疑是最好的方式。该文阐述了什么是SNS开放平台、SNS产业链中的各个角色以及OpenSocial产生的背景与现状。

Factors influencing knowledge contribution:An empirical investigation of social networking website users

Purpose: In the Web 2.0 era,leveraging the collective power of user knowledge contributions has become an important part of the study of collective intelligence. This research aims to investigate the factors which influence knowledge contribution behavior of social networking sites(SNS) users.Design/methodology/approach: The data were obtained from an online survey of 251 social networking sites users. Structural equation modeling analysis was used to validate the proposed model.Findings: Our survey shows that the individuals' motivation for knowledge contribution,their capability of contributing knowledge,interpersonal trust and their own habits positively influence their knowledge contribution behavior,but reward does not significantly influence knowledge contribution in the online virtual community.Research limitations: Respondents of our online survey are mainly undergraduate and graduate students. A limited sample group cannot represent all of the population. A larger survey involving more SNS users may be useful.Practical implications: The results have provided some theoretical basis for promoting knowledge contribution and user viscosity.Originality/value: Few studies have investigated the impact of social influence and user habits on knowledge contribution behavior of SNS users. This study can make a theoretical contribution by examining how the social influence processes and habits affect one's knowledge contribution behavior using online communities.

社交网站(SNS)中在线社会支持的研究现状与趋势分析

[目的/意义]随着互联网的高速发展,社交网络从无到有,在全球迅速发展起来,人们开始更多的依赖从SNS中获得在线社会支持。与传统社会支持相比,在线渠道尤其是SNS对于人们给予和接受社会支持变得越来越重要。因此,对这一领域的中外文献进行整体分析,帮助研究人员了解这方面研究的发展现状和趋势是有必要的。[方法/过程]通过对2018年前社交网络环境下在线社会支持的中英文研究的期刊文献进行收集并对文献进行内容分析。通过对205篇中外研究文献进行多维编码分析,总结了目前的研究现状并探讨了未来研究的可能方向。[结果/结论]文章总结了SNS环境下在线社会支持的研究重点以及研究方法等研究现状,探讨了该领域在研究对象、研究方法、社会网络的结构与作用等方面的未来潜在研究方向和途径,并分析了中外研究者在研究方法等方面的差异。

SNS社交网络结构实证研究——以豆瓣网为例

为了揭示豆瓣网的社交网络结构特性,使用网络爬虫,获取了豆瓣网的社交网络数据。利用复杂网络分析库NetworkX研究了豆瓣网的社交网络结构特性。与现实社会网络相比,它不仅表现出无标度和小世界特性等共性,而且还表现出度异配性的特性。

SNS采纳后阶段用户持续使用行为研究——基于情感依恋与ECM-IS的整合模型

SNS采纳后阶段的持续使用行为是SNS成功的关键,本研究提出情感依恋在用户持续使用意向形成过程中起关键性作用,用户的持续使用意向将受到情感与认知的双重作用。在整合ECM-ISC模型以及情感依恋相关理论、依恋强度ARC模型、情感扩建相关理论的基础上,构建了概念模型,以微信朋友圈经验用户为调研对象,进行了实证研究。研究结果表明:改进后的模型具有更高的解释力,感知有用性、满意和情感依恋对持续使用意向有显著影响,并间接影响用户的持续使用行为。用户关联、能力以及自主需求的满足会促使用户产生情感依恋并在情感扩大机制的作用下促使用户产生持续使用意向,同时满意度对于情感依恋也具有显著的正向作用。

SNS用户活跃行为研究：集成承诺、社会支持、沉没成本和社会影响理论的观点

社交网络服务(SNS)用户的活跃行为对社交网站的成功以及社会化商务的发展都至关重要。本研究集成承诺理论、社会支持理论、沉没成本理论和社会影响理论,实证研究了决定用户总体活跃行为的心智模式以及影响这些心智模式的环境因素。通过对1239位人人网用户的数据收集和分析,模型结果显示,情感承诺和持续承诺是决定SNS用户总体活跃行为的主要心智模式,情感支持和信息支持构建情感承诺,声誉资本和关系资本构建持续承诺,社会规范和从众构建规范承诺。研究结果扩展了相关理论应用,丰富了SNS领域研究视角,并对业界有指导意义。

基于SNS社交网络的增长模型

随着网络信息技术的发展,社交网络(SNS)成为目前最热门的休闲交友平台。文章研究表明,这类网络具有3个典型的拓扑特征:幂律度分布、小平均距离和大聚集系数。为了进一步研究此类网络的拓扑结构及其动力学行为,文中基于社交网络的增长机制提出了一种二步式增长模型。运用平均场理论及数值仿真验证发现,该模型同时满足上述3个拓扑特征,符合社交网络的基本结构特性。

社交网络(SNS)中的自我呈现及其影响因素

基于符号互动论而提出的自我呈现概念及其理论,以往常被用于解释人们现实生活中的面对面人际互动行为。近些年,社交网络逐渐成为人际互动行为发生的重要场域。由于社交网络自身的独特之处,人们在社交网络中的自我呈现,与现实面对面、传统计算机媒介交流中的自我呈现有诸多不同。社交网络中的自我呈现更受到目标观众、动机、人格特征、性别和文化等方面的显著影响。西方的"富者更富理论"和"社会补偿理论",在解释社交网络中自我呈现现象本质时各执一词,而国内的"补充我/补偿我理论"或许能较好地解决这一理论问题。未来的SNS自我呈现研究应在扩展被试群体范围、SNS网站种类和影响因素变量几方面继续前行。

基于社会认知理论和大五人格模型的SNS用户内容生成行为实证研究

论文基于社会认知理论和大五人格模型,探讨了社交网站(SNS)用户的人格特质和认知因素以及SNS的信任氛围对用户在网站中的内容生成行为的影响作用。研究表明,大五人格中只有外向性和尽责性显著影响SNS用户的内容生成行为;此外,结果期望和信任氛围对SNS用户的内容生成行为也有显著影响。

SNS中结合声誉与主观逻辑的信任网络分析

提出一种结合声誉与主观逻辑的信任网络分析方法,结合声誉与朋友间的信任关系来获得信任值。由于推荐来自信任的朋友也基于普遍的社会声誉,信任计算的结果更能反映真实情况。仿真实验及分析表明,该方法具有较高的效率和准确度。