新媒体时代新闻众筹在中国的发展与对策

新闻众筹作为一种在全球范围内方兴未艾的众筹类型,在中国刚刚崭露头角.本文介绍了新闻众筹的概念及影响,提出了新闻众筹的发展瓶颈,最后针对新闻众筹的持续性发展提出了建议.     

Optimal Quad Channel Long Short-Term Memory Based Fake News Classification on English Corpus

The term ‘corpus’ refers to a huge volume of structured datasets containing machine-readable texts. Such texts are generated in a natural communicative setting. The explosion of social media permitted individuals to spread data with minimal examination and filters freely. Due to this, the old problem of fake news has resurfaced. It has become an important concern due to its negative impact on the community. To manage the spread of fake news, automatic recognition approaches have been investigated earlier using Artificial Intelligence (AI) and Machine Learning (ML) techniques. To perform the medicinal text classification tasks, the ML approaches were applied, and they performed quite effectively. Still, a huge effort is required from the human side to generate the labelled training data. The recent progress of the Deep Learning (DL) methods seems to be a promising solution to tackle difficult types of Natural Language Processing (NLP) tasks, especially fake news detection. To unlock social media data, an automatic text classifier is highly helpful in the domain of NLP. The current research article focuses on the design of the Optimal Quad Channel Hybrid Long Short-Term Memory-based Fake News Classification (QCLSTM-FNC) approach. The presented QCLSTM-FNC approach aims to identify and differentiate fake news from actual news. To attain this, the proposed QCLSTM-FNC approach follows two methods such as the pre-processing data method and the Glove-based word embedding process. Besides, the QCLSTM model is utilized for classification. To boost the classification results of the QCLSTM model, a Quasi-Oppositional Sandpiper Optimization (QOSPO) algorithm is utilized to fine-tune the hyperparameters. The proposed QCLSTM-FNC approach was experimentally validated against a benchmark dataset. The QCLSTM-FNC approach successfully outperformed all other existing DL models under different measures.     

Networked Press Freedom and Social Media: Tracing Historical and Contemporary Forces in Press‐Public Relations

This paper analyzes how mainstream, online news organizations understand press autonomy in their relationships to audiences. I situate the press in terms of neo‐institutional sociology, seeing its autonomy as a distributed, field‐level phenomenon involving “boundary work” among distributed actors. I then trace press‐audience relations through two historical examples (letters to the editor and ombudsmen), showing how the press has historically both separated itself from and relied upon audiences. Examining eight news organizations' social media policies, I analyze the “inside‐out” and “outside‐in” forces through which the press distinguishes itself from audiences, concluding with a discussion of how such guidelines structure the types of control that news organizations have, or might have, as they use social network sites in their news work.     

基于改进边权重的成对马尔可夫随机场模型的社交异常账号检测方法

社交媒体系统为人们提供了便利的共享、交流和协作平台。人们在享受社交媒体的开放性和便利性时,可能会发生许多恶意行为,例如欺凌、恐怖袭击计划和欺诈信息传播。因此,尽可能准确、及早地发现这些异常活动,以防止灾难和袭击,是非常重要的。近年来,随着在线社交网络(OSN)如Twitter,Facebook,Google+,LinkedIN等的成功,丰厚的利益资源使得它们成为了攻击者的目标。社交网络的开放性,使其特别容易受到异常账号攻击的威胁。现有基于图形的最先进分类模型大多使用首先为图的边分配权重,在加权图中迭代地传播节点的信誉分数,并使用最终的后验分数来对节点进行分类的方法。边权重的分配是其中一项重要的任务,此参数将直接影响检测结果的准确度。为此,文中针对社交媒体中异常账号的检测任务,分析了基于社交图全局结构的方法,通过在成对马尔可夫随机场模型中改进边权重的计算方法,使其能够在迭代过程中自适应优化,提出了准确度更高的GANG+LW,GANG+LOGW和GANG+PLOGW算法。这3种算法使用了不同的改进边权重的方法。实验证明,新提出的方法相对于基本的成对马尔可夫随机场模型,可取得更准确的异常账号...     

因果关系及其在社会媒体上的应用研究综述

诸如物理学、行为学、社会学和生物学中许多研究的中心问题是对因果的阐述,即变量或事件之间直接作用关系的阐述。由于人们的日常行为和语言越来越多地映射到互联网上,或者根本就是互联网引起了大量新的行为和语言,致使社会媒体上存在大量的因果问题。与相关关系分析相比,社会媒体上的因果关系分析更加必要和迫切,首先,任何相关性的背后都隐藏着因果关系;其次,相关性分析得到的结论有时是不可靠的甚至是错误的;再次,基于相关性的方法无法用于管理、控制和干预变量或事件。论述了因果关系分析的必要性、重要性和社会媒体上存在的因果问题;综述了目前的因果分析与推断的基本理论、存在的问题和研究现状;通过比较现有因果关系分析的研究思路,预测未来的研究方向和因果分析理论及方法在社会媒体上的应用。     

基于多传递影响力的社交媒体谣言检测方法

社交媒体谣言检测是当前研究的热点问题,现有方法多数通过获取大量用户属性学习用户特征,但不适用于谣言的早期检测,忽略了用户之间的潜在关系对信息传播的影响。提出一种基于多传递影响力的谣言检测方法,根据源微博及其对应转发(评论)之间的关系构建文本信息传播图,并通过图卷积神经网络来捕获、学习文本信息的传播特征。利用文本信息和用户传播过程中的影响力,丰富可用于谣言检测早期的检测信息。将存在转发关系的用户构成用户影响力传播图,构建一种用户节点影响力学习方法,获取用户节点影响力,以增强用户特征信息。在此基础上,将文本特征与用户特征融合以进行谣言检测,从而提升检测效果。在3个真实社交媒体数据集上的实验结果表明,该方法在谣言自动检测以及早期检测的效果都有显著提升,与目前最好的基准方法相比,在微博、Twitter15、Twitter16数据集上的正确率分别提高了2.8%、6.9%和3.4%。     

Formation and Effect of Social Media Usage Habit

The widespread of social media (SM) provides information system (IS) research with a great opportunity to examine IS usage habit. This study adapts habit perspectives of social psychology into social media use and contextualizes findings from both service marketing and IS usage literature for developing a model of SM usage habit. The model suggests a set of behavior-, goal-, affect-, and expectation-based factors that drive the formation of habit for SM use, which in turn influences future usage behavior. By testing the model with a longitudinal field survey of 518 social networking service (SNS) users, this study provides an integrative context-specific view of SM usage habit and addresses unconscious habitual IS use beyond the traditional perspectives of conscious intentional behavior. The findings have important implications for promoting sustainability of social media applications and services.     

The Impact of Social Media in Review

Today, the impact of social media (SM) and social network services are undeniable. Social web applications are particularly so pervasive that finding innovative ways to incorporate them into our everyday lives for profit or educational use is a constant activity for some. The purpose of this study is to identify best practices acknowledged through scholarly literature for professionals and to highlight the common ways to improve the use of social web tools. Through a comprehensive literature review, this study illustrates how an effective communication synergy can be developed through the use of various social mediums for sustainable user engagement online.     

Increasing Attributional Certainty via Social Media: Learning About Others One Bit at a Time

This study presents a novel examination of changes in attributions about individuals via information obtained from online sources within the context of hiring decisions. An experiment had 127 participants examine dossiers collected about a job applicant, in some conditions containing either positively or negatively valenced information about the applicant, obtained from the Internet. Results indicated online information significantly increases attributional certainty and positively valenced online information led to more favorable impressions of the applicant's perceived fit and employability. Surprisingly, results also violate assumptions of the increased magnitude of attributions due to negative information. Findings are discussed with respect to uncertainty reduction strategies, the negativity effect of online information, and implications for job seekers and employers.     

Social Media Based Transportation Research: the State of the Work and the Networking

Recently, there has been an increased interest in the use of social media data as important traffic information sources. In this paper, we review social media based transportation research with social network analysis methods.We summarize main research topics in this field, and report collaboration patterns at levels of researchers, institutions, and countries, respectively. Finally, some future research directions are identified.      

从脸书用户个人信息泄露事件谈我国社交媒体用户隐私保护

The rapid development of information technology has promoted the popularization of social media applications. Social me- dia has become a platform for users to search for real-time information and communicate and interact. Privacy protection is an im- portant symbol of the progress of human modern civilization. While enjoying the benefits brought by social media, human rights al- so face unprecedented challenges. Therefore, the protection of privacy highlights its importance. This paper analyzes the information leakage of Facebook users in order to discover the various risks faced by the privacy protection of social media users. Then it re- views the user privacy protection measures of major social media platforms at home and abroad. Finally, it is recommended that the government level should speed up the improvement of the social media platform user privacy supervision system, the enterprise lev- el should strengthen the implementation of the social media platform user privacy protection subject responsibility, and the user must improve personal information security literacy and protect personal information and privacy together.     

The Effects of Pranks in Social Media on Brands

Practical jokes belong to the category of disparagement humor and they have been extensively investigated by social scientists for decades. Nevertheless, the available empirical results pertain mostly to human-to-human situations and little is known on the effects of pranks in the computer-mediated environment (CME). As CME encourages different patterns of reactions and behaviors than non-CME, the present study provides an experimental evaluation of consumer responses to brands that use practical jokes in their social media communication. The results reveal that high-disparagement pranks may significantly hurt brand perceptions and decrease brand affect. This effect is better predicted by prior brand attitudes than usage (i.e., the more favorable predisposition to a brand, the higher probability that a high-disparagement prank will produce a lower brand affect). No significant impact of low-disparagement pranks was found on subsequent brand responses. The findings will help scholars and practitioners in understanding this new phenomenon and in maximizing returns from humor online.     

The Hyperlinked World: A Look at How the Interactions of News Frames and Hyperlinks Influence News Credibility and Willingness to Seek Information

Prior research has identified the influence of using hyperlinks in online information gathering. This study attempts to understand first, how hyperlinks can influence individuals' perceptions of news credibility and information‐seeking behavior. Second, the paper extends previous research by examining the interaction of hyperlinks with the content of the story. In doing so, the paper examines the influence of hyperlinks on news frames. The data for the study were collected using 2 experiments embedded in web‐based survey of participants. Findings show that hyperlinks in news stories can increase perceptions of credibility as well as information‐seeking. Results reveal the interaction of news frames in the process; hyperlinks increase participants' perception of news credibility; but only in the value‐framed condition. Implications are discussed.     

基于社交媒体的事件脉络挖掘研究进展

随着Web 2.0的兴起以及移动互联网与智能终端的蓬勃发展,以微博为代表的社交媒体迅速发展壮大。基于社交媒体的事件脉络挖掘技术在突发事件检测、事件走势分析、舆情预测等诸多方面发挥着重要作用,受到学术界的广泛关注。该文在最新研究成果与文献的基础上,以事件脉络挖掘的实现为出发点,概括总结了核心步骤中存在的关键技术,并归纳提出了目前事件脉络挖掘与分析过程中存在的4个关键性的技术问题与挑战,分别如下: 多模态信息融合条件下的事件脉络生成、跨媒介异构数据协同下的事件挖掘与事件脉络生成、层次化多粒度复杂事件的关系映射和实时数据条件下动态事件的快速识别与脉络生成。同时,针对上述关键问题与技术挑战进行了理论探讨、工作进展与趋势分析以及实际应用介绍,从而为深入研究和解决基于社交媒体的事件脉络挖掘技术提供了新的研究线索与方向。     

Preserving Social Media: The Problem of Access

This article is part of a 12-month study commissioned by the UK Data Service as part of the “Big Data Network” program funded by the Economic and Social Research Council (ESRC). The larger study focuses on the potential uses and accompanying challenges of data generated by social networking applications. This article, “Preserving Social Media: The Problem of Access,” comprises an excerpt of that longer study, allowing the authors a space to explore in closer detail the issue of making social media archives accessible to researchers and students now and in the future.     

社交媒体内容可信性分析与评价

近年来社交媒体在拓宽人们获取信息渠道的同时,也方便了虚假信息的传播,并造成了严重的负面影响.与传统互联网媒体相比,社交媒体包含的信息更加复杂多样,为内容可信性的判断带来了新的挑战.已有研究在分析社交媒体内容可信性时,对挖掘可信性影响因素进行了很多工作,但缺乏对噪音数据的处理,大量的无用推文会对推文可信性判断造成干扰,进而会影响事件层面的可信性判断,从大量噪音数据中筛选出真正有用的推文数据就显得尤为重要.在推文层面同时考虑用户的主题因素和从众行为,减少了从众转发等噪音数据在可信性判断过程中的作用,对社交媒体内容的可信性进行研究,采用贝叶斯网络建立了社交媒体内容可信性评价模型,并通过新浪微博公开数据集验证了模型的有效性.     

Social Media Burnout: Definition,Measurement Instrument,and Why We Care

Social media users have shown several burnout symptoms lately. In this research, we study the social media burnout issue from three perspectives, including a user’s ambivalence, emotional exhaustion, and depersonalization. Building on the Maslach Burnout Inventory (MBI), we designed a new scale to measure a user’s social media burnout. Using social media continuance as an example, we investigated the effects of social media burnout on a user’s post-adoption intention. We find that ambivalence, emotional exhaustion, and depersonalization can all significantly negatively influence a user’s social media continuance.     

基于信号理论的社交媒体平台安全和信任评估度量方法

开放的社交媒体平台给用户带来使用便捷的同时,也存在不少恶意站点、信息欺骗和信任缺失等安全性和可信性问题。社交平台的安全性和可信性作为社会交互的基础,在信息共享与交流中至关重要;传统的安全和信任评估仅关注于用户间的信任关系以及安全实现,而针对社交媒体平台的评估和度量方法还不健全;因此提出了一种基于信息管理信号理论的在线社交网络平台安全和信任度量方法;首先,对平台安全性和可信性信号进行分类,并采用OWL语言和时态逻辑形式化描述了平台静态属性和动态行为特征;其次,使用FAHP确定此类信号的指标权重并进行系统的评价,并结合群体计算思想提出了一个平台安全和信任的综合评估计算模型;最后,在一个现实的多媒体社交网络平台(CyVOD.net)上进行了评估实验;实验结果显示,该方法能够准确地获得社交平台的各安全和信任要素的评估值,并有效地指导社交媒体网络平台的功能进化和版本更新。     

A Framework for the E-Discovery of Social Media Content in the United States

People are using social networking sites (SNS) for day-to-day communications, outpacing email and texts. SNS are evidence gold mines subject to discovery in investigations, audits, and litigation. This paper examines the Federal Rules of Civil Procedure (FRCP), which specifically address electronically stored information (ESI), but not social content, as a unique form of evidence. A framework is presented to categorize legal cases according to the degree of e-discovery access granted. One conclusion is caveat usor.     

浅析社会化媒体中个人虚拟形象的设计

今天,社会化媒体日益深入网络用户的生活,在这个虚拟社会中,视觉化的个人虚拟形象使网友的形象更加有血有肉,得到了众多用户的喜爱,同时也成为了网络虚拟财产的重要组成部分。作为一个新的视觉语言形式,个人虚拟形象设计理应得到更多的重视,设计师们也应该进一步地探索其创新性的设计理念,设计出个性化和多样化的个人虚拟形象来。