融合社交信息的局部潜在空间推荐方法

随着社交网络的发展,越来越多的研究利用社交信息来改进传统推荐算法的性能,然而现有的推荐算法大多忽略了用户兴趣的多样化,未考虑用户在不同社交维度中关心的层面不同,导致推荐质量较差.为了解决这个问题,提出了一种同时考虑全局潜在因子和不同子集特定潜在因子的推荐方法LSFS,使得推荐过程既考虑了用户共享偏好又考虑了用户在不同子...     

Exploring rape culture in social media forums

Current research has yet to examine the phenomenon of rape culture, particularly within social media forums. The present study investigated the attitudes about rape, rapists, and gender-based violence within the comments section of newspaper articles reporting about rape and sexual assault. Naturalistic observation was used in order to gather statements within the comment sections following newspaper articles posted on either the periodical website or the periodical’s Facebook page. Four themes and various sub-themes emerged from the data. The major themes include, Victim Blaming and Questioning, Survivor Support, Perpetrator Support, and Trolling Statements about Law and Society. Notable findings were found in the amount of victim blaming statements made in the comments responding to articles (25.8 percent) and perpetrator support comments were found responding to every article collected, except for one. The authors discuss the implications of rape culture within and outside social media and suggest future research to be conducted to further understand the impacts of rape culture within the online sphere.     

LGA: latent genre aware micro-video recommendation on social media

Social media has evolved into one of the most important channels to share micro-videos nowadays. The sheer volume of micro-videos available in social networks often undermines users’ capability to choose the micro-videos that best fit their interests. Recommendation appear as a natural solution to this problem. However, existing video recommendation methods only consider the users’ historical preferences on videos, without exploring any video contents. In this paper, we develop a novel latent genre aware micro-video recommendation model to solve the problem. First, we extract user-item interaction features, and auxiliary features describing both contextual and visual contents of micro-videos. Second, these features are fed into the neural recommendation model that simultaneously learns the latent genres of micro-videos and the optimal recommendation scores. Experiments on real-world dataset demonstrate the effectiveness and the efficiency of our proposed method compared with several state-of-the-art approaches.     

Between control and creativity: challenging co-creation and social media use in a museum context

Abstract

This article addresses the challenges and limitations of co-creation processes in museums based on an exemplary case of the design of a Danish museum-related campaign on Facebook. The article adds to the ongoing discussion on the paradigm shift in the museum by analysing potential problematical issues of including multiple agents in creative processes in museums. It concludes that it is arduous to establish a truly creative co-creative process, when the design team needs to accommodate to a well-established artistic vision as well as to the limited resources of the museum and the other participants. We argue that one cannot in advance predict or ascertain that social media users will find a campaign compelling and want to participate and engage with the content, even if substantial resources have gone into its creation. Furthermore, the complexity of many art projects is likely to make their dissemination on social media difficult.     

Multimedia summarization using social media content

In this work, we propose a novel multimedia summarization technique from Online Social Networks (OSNs). In particular, we model each Multimedia Social Network (MSN)—i.e. an OSN focusing on the management and sharing of multimedia information—using an hypergraph based approach and exploit influence analysis methodologies to determine the most important multimedia objects with respect to one or more topics of interest. Successively, we obtain from the list of candidate objects a multimedia summary using a summarization model together with an heuristics that aims to generate summaries with priority (with respect to some user keywords), continuity, variety and not receptiveness features. The performed experiments on Flickr shows the effectiveness of proposed approach.     

Contribution and consumption of content in enterprise social media

While many enterprises adopt social media to foster collaboration and communication between employees, the question remains whether individual employees adopt it and if so, how they use it. In this study, we distinguish the two major types of use to account for active and passive of social media usage: content contribution and content consumption. Both types of use are modeled as dependent variables in an adapted technology adoption model and tested in a field study.     

Quantifying the trustworthiness of social media content

The growing popularity of social media in recent years has resulted in the creation of an enormous amount of user-generated content. A significant portion of this information is useful and has proven to be a great source of knowledge. However, since much of this information has been contributed by strangers with little or no apparent reputation to speak of, there is no easy way to detect whether the content is trustworthy. Search engines are the gateways to knowledge but search relevance cannot guarantee that the content in the search results is trustworthy. A casual observer might not be able to differentiate between trustworthy and untrustworthy content. This work is focused on the problem of quantifying the value of such shared content with respect to its trustworthiness. In particular, the focus is on shared health content as the negative impact of acting on untrustworthy content is high in this domain. Health content from two social media applications, Wikipedia and Daily Strength, is used for this study. Sociological notions of trust are used to motivate the search for a solution. A two-step unsupervised, feature-driven approach is proposed for this purpose: a feature identification step in which relevant information categories are specified and suitable features are identified, and a quantification step for which various unsupervised scoring models are proposed. Results indicate that this approach is effective and can be adapted to disparate social media applications with ease.     

基于社会化媒体情境的多维协同智能推荐

针对传统协同智能推荐技术的冷启动、数据稀缺性问题,为提高推荐算法的效率和准确性,提出一种基于社会化媒体情境的多维智能推荐算法模型。该模型将目标用户的属性特征、行为特征考虑到社会化媒体情境信息中,并动态实时捕捉用户在不同社会化媒体情境下的偏好倾向,利用联机分析处理(OLAP)技术对多维数据进行处理。该模型将用户间的社会化关系和所处的政治经济环境视为衡量用户相似的重要指标,同时使用皮尔森系数和云模型来计算用户间各特征的相似度,并以此为推荐基础向用户呈现更个性化和定制化的推荐结果。实验结果表明,该模型的推荐结果的平均绝对误差明显小于传统的协同智能推荐和单纯的基于云模型推荐技术。     

基于群体行为的社交媒体中水军分析

社交媒体是当下非常热门的一种互联网应用。在发生公共事件时,许多当事人雇佣公关公司在社交媒体上发布虚假信息来影响舆情。这部分为公关公司所服务的社交媒体账号称为水军。水军在当下的中国社交媒体中有泛滥的趋势。基于水军的群体行为分析社交媒体中的水军集团。定义了情感社会网络和情感社会网络中的团的概念;设计了一个分析框架,包括提出了情感社会网络中发现社区,以及从社区中发现对立的派别（称为团）的算法。从微博的评论数据构建情感社会网络,并发现情感社会网络中的团,可以识别水军,而且可以考察社交媒体中的水军集团的特性。采用人工标注的数据集进行了实验。     

Perceived risks and risk management of social media in an organizational context

The use of new electronic media for marketing communication is gaining in popularity with organizations, and the adoption of social media (SOME) is enjoying particularly rapid growth. However, organizations are uncertain about using SOME and perceived risks are limiting their use. This study explores the key types of risks that business organizations associate with using SOME in their marketing communication. We also explore the effectiveness of procedural control and proactive focusing processes in managing these risks. The results show that organizations perceive three types of risks that deterred companies from increasing the use of SOME. Companies applied procedural control mechanisms to manage time-loss risks. An organization’s familiarity with SOME was found to have a strong effect on time-loss as well as on other types of risks. Research revealed that the role that proactive focus and procedural control played in managing SOME-related risks was less than had been anticipated.     

Constructing user requirements: a social process for a social context

We discuss our approach to constructing user requirements whereby users build requirements models themselves. The approach, termed user-led requirements construction (ULRC), is a social process that addresses a major problem: the user-developer culture gap. An important feature of the approach is that we have conducted, with users, an interpretivist empirical evaluation study of its constituent event flow diagram (EFD) modelling method and associated training. The ULRC has also been used in a live environment. The results are that users regard the EFD as effective for the requirements process, the EFD may be used for further studies to deepen our knowledge of the requirements process and the results contribute towards a set of public results for general evaluation and improvement.     

Exploring virtual classrooms: network communication in a cross-cultural context

Abstract

Using only desktop computers linked to gateway networks, students from around the world can communicate in a ‘virtual classroom'. (A term which is similar in concept to, but in no way associated with the Virtual Classroom Project, a registered trademark of the New Jersey Institute of Technology (Hiltz 1990).) One such network, the Apple Global Education network (AGE), ? ? Developed by Apple Computer Inc., Cupertino, CA, USA. links over one hundred classes from over 20 countries. This network was evaluated with respect to its role in facilitating cross-cultural communication activities.

The nature of communication on the network is described by classifying transmissions sent during a major international crisis: the Gulf War. A taxonomy is proposed containing four levels of network function: audience, diffuser, repository and facilitator. Following the evaluation of the AGE network, recommendations are given to educators regarding network activities in relation to the age and experience of the learner.     

Exploring sharing patterns for video recommendation on YouTube-like social media

YouTube-like video sharing sites (VSSes) have gained increasing popularity in recent years. Meanwhile, Face-book-like online social networks (OSNs) have seen their tremendous success in connecting people of common interests. These two new generation of networked services are now bridged in that many users of OSNs share video contents originating from VSSes with their friends, and it has been shown that a significant portion of views of VSS videos are attributed to this sharing scheme of social networks. To understand how the video sharing behavior, which is largely based on social relationship, impacts users’ viewing pattern, we have conducted a long-term measurement with RenRen and YouKu, the largest online social network and the largest video sharing site in China, respectively. We show that social friends have higher common interest and their sharing behaviors provide guidance to enhance recommended video lists. In this paper, we take a first step toward learning OSN video sharing patterns for video recommendation. An autoencoder model is developed to learn the social similarity of different videos in terms of their sharing in OSNs. We, therefore, propose a similarity-based strategy to enhance video recommendation for YouTube-like social media. Evaluation results demonstrate that this strategy can remarkably improve the precision and recall of recommendations, as compared to other widely adopted strategies without social information.     

Adoption of travel information in user-generated content on social media: the moderating effect of social presence

Travel patterns have gradually changed from group travel to individual travel. An increasing number of people acquire travel information through various types of media. One of the alternative information sources is social media, which enables users to exchange information among members. However, one of the characteristics of social media is information sharing, not information search, which involves both giving (i.e. posting) and taking (i.e. selective reading, forwarding, replying, linking, and liking) information. Compared to the ‘giving’ side of information-sharing research, less effort has been spent on the ‘taking’ side of information research. Therefore, we investigate travel information adoption in social media as well as how individuals communicate with each other. We use the elaboration likelihood model, which measures the impact of central (e.g. argument quality) and peripheral (e.g. credibility) cues on traveller information-sharing behaviour corresponding with social presence on social media. The results of an empirical analysis of 527 respondents, who were experienced in travel information adoption via social media, were examined. Our findings revealed that argument quality had a positive effect on perceived usefulness and source credibility positively affected perceived usefulness and social relationships. Perceived usefulness had a significant positive effect on social relationships. Both perceived usefulness and social relationships affected travel information adoption. Lastly, the levels of argument quality and source credibility perceived by social media members were found to differ according to the level of social presence.