基于信息内容和拓扑关系的社会媒体用户兴趣分类

随着社会的发展,信息已经成为社会发展越来越重要的部分,人类的信息传播活动越来越明显地展示出分众特征,对用户的分类成为人类信息活动的一个重要研究课题.从这一目标出发,分别基于信息内容、拓扑关系和两者综合的方法,按兴趣主题对社会媒体用户进行分类.对于基于信息内容的用户分类,采用LDA主题模型从用户所发布的内容中提取其主题分布,基于这一分布,采用支持向量机、决策树、贝叶斯等多种模型按兴趣主题对用户进行分类.对于基于拓扑关系的分类,依据相同兴趣主题的用户倾向于拥有共同的粉丝这一发现,构建分类模型来按兴趣主题对用户进行分类.然后提出综合信息内容和拓扑关系的分类方法来对用户进行分类.最后基于大规模Twitter数据的实验发现,采用综合方法对用户进行的兴趣分类性能明显高于采用单一信息内容或粉丝拓扑方法的性能.     

Exploring the effect of user engagement in online brand communities: Evidence from Twitter

Social media such as forums, blogs and microblogs has been increasingly used for public information sharing and opinions exchange nowadays. It has changed the way how online community interacts and somehow has led to a new trend of engagement for online retailers especially on microblogging websites such as Twitter. In this study, we investigated the impact of online retailers' engagement with the online brand communities on users' perception of brand image and service. Firstly, we analysed the overall sentiment trends of different brands and the patterns of engagement between companies and customers using the collected tweets posted on a popular social media platform, Twitter. Then, we studied how different types of engagements affect customer sentiments. Our analysis shows that engagement has an effect on sentiments that associate with brand image, perception and customer service of the online retailers. Our findings indicate that the level, length, type and attitude of retailers' engagement with social media users have a significant impact on their sentiments. Based on our results, we derived several important managerial and practical implications.     

Integrating truth bias and elaboration likelihood to understand how political polarisation impacts disinformation engagement on social media

Political polarisation has become an increasingly alarming issue in society, exacerbated by the widespread use of social media and the development of filter bubbles among social media users. This environment has left users susceptible to disinformation, especially those with whom a user is politically aligned. In this research, we integrate truth bias, elaboration likelihood model and new media literacy into a model for explaining social media engagement (with both disinformation and factual information) and analysing how political polarisation (operationalised as political alignment between users) influences perceptions and behaviours. Using an experimental design, we analyse the model separately for posts containing disinformation and factual information, highlighting key differences. Political alignment positively moderates truth bias's effect on engagement with disinformation. For both disinformation and factual information, political alignment moderates the effect of generalised communicative suspicion (GCS) on truth bias, such that GCS's effect on truth bias flips from negative to positive as political alignment increases. Issue involvement and political alignment appear to be the primary drivers of disinformation engagement, with critical consuming media literacy failing to mitigate engagement. Our findings contribute to the understanding of persuasion, conviction, amplification, polarisation and aversion related to fake news on social media.     

Brand interactions and social media: Enhancing user loyalty through social networking sites

This paper aims to investigate how user loyalty can be achieved and maintained through social networking sites. More specifically, we intend to test the relationships between brands, user loyalty and social media. The research thus provides insights into user-brand relationships through social media and argues how loyal customers can be through social networking websites. Although there are considerable numbers of studies about loyalty; there exists very limited work studying user loyalty through social networking websites. This research presents clearly the reasons for engaging with brands online and examines user behaviors and loyalty. Research provided strong evidence that majority of the social network users follow brand fan pages via social media, even though they have different reasons to do so. The study also measures users’ behavioral and attitudinal loyalty behaviors. Their level of trust to the information they obtained about brands through social media is also established. The hypotheses tested show that brands and customer satisfaction are both positively related to users’ behavioral loyalty.     

Not Even Past: Information Aging and Temporal Privacy in Online Social Networks

Online social networks (OSNs) make information accessible for unlimited periods and provide easy access to past information by arranging information in time lines or by providing sophisticated search mechanisms. Despite increased concerns over the privacy threat that is posed by digital memory, there is little knowledge about retrospective privacy: the extent to which the age of the exposed information affects sharing preferences. In this article, we investigate how information aging impacts users’ sharing preferences on Facebook. Our findings are based on a between-subjects experiment (n = 272), in which we measured the impact of time since first publishing an OSN post on its sharing preferences. Our results quantify how willingness to share is lower for older Facebook posts and show that older posts have lower relevancy to the user’s social network and are less representative of the user’s identity. We show that changes in the user’s social circles, the occurrence of significant life changes and a user’s young age are correlated with a further decrease in the willingness to keep sharing past information. We discuss our findings by juxtaposing digital memory theories and privacy theories and suggest a vision for mechanisms that can help users manage longitudinal privacy.     

Assessing flow experience in social networking site based brand communities

The sustenance of the Social Networking Site (SNS)-based brand communities relies on user retention and their active participation. Therefore, understanding the intrinsic aspects of user behavior in such communities is important for devising strategies to ensure user retention and active participation. Especially, information about the elements that induce flow experiences—the intrinsically enjoyable and immersive experiences—of users in SNS has become important for organizations that host online communities. In our empirical study, we chose to focus especially on SNS-based brand communities, as they are increasingly interesting from an organization-community interaction perspective, but they lack the instruments needed for measuring user experience. The present study addresses this gap by developing an instrument aimed at measuring the user’s flow experience on SNS-based brand communities. A cross-sectional survey with 577 Facebook brand community users was carried out. The findings show that enjoyment, concentration, and social interaction are the components that constitute a user’s flow experience. In addition to providing a valuable tool for business practitioners, the developed instrument offers several theoretical and practical implications for improving user experience of social media.     

社交网络中用户隐私推理与保护研究综述

如今微博和Twitter等社交网络平台被广泛地用于交流、创建在线社区并进行社交活动。用户所发布的内容可以被推理出大量隐私信息,这导致社交网络中针对用户的隐私推理技术的兴起。利用用户的文本内容及在线行为等知识可以对用户进行推理攻击,社交关系推理和属性推理是对社交网络用户隐私的两种基本攻击。针对推理攻击保护机制和方法的研究也在日益增加,对隐私推理和保护技术相关的研究和文献进行了分类并总结,最后进行了探讨和展望。     

Social media and relationship development: The effect of valence and intimacy of posts

Social media is becoming an increasingly common part of everyday life. Many social media sites (e.g. Facebook, Twitter and LinkedIn) support new interpersonal interaction methods, some of which are neither directed nor reciprocated. For example, social media users can read online 'posts' (self-disclosures) of their friends without interacting with those friends. This is vastly different to traditional face-to-face communication. Our study investigated how reading online 'posts' affects relationship development. Using a longitudinal design sampling 243 participants, we focused on the effect of the posts' valence and intimacy. We found that high intimacy posts or negative posts decreased the social attractiveness of the self-discloser. The perception of the posts and the receiver's feelings of homophily to the self-discloser mediated this relationship. Studies of offline interpersonal interaction have found similar results. In offline communication, self-disclosure perception and homophily also mediate relationship outcomes. This suggests that reading posts on social media and interacting in real life trigger similar or identical relationship formation pathways. These results support the argument that passive consumption is a new method of interaction that does not fundamentally change human psychology. While novel, passive consumption is still based on the same principles as offline communication.     

基于异构社交上下文的多视图微博主题检测

社交媒体主题检测旨在从大规模短帖子中挖掘潜在的主题信息. 由于帖子形式简短、表达非正规化, 且社交媒体中用户交互复杂多样, 使得该任务具有一定的挑战性. 前人工作仅考虑了帖子的文本内容, 或者同时对同构情境下的社交上下文进行建模, 忽略了社交网络的异构性. 然而, 不同的用户交互方式, 如转发, 评论等, 可能意味着不同的行为模式和兴趣偏好, 其反映了对主题的不同的关注与理解; 此外, 不同用户对同一主题的发展和演化具有不同影响, 社区中处于引领地位的权威用户相对于普通用户对主题推断会产生更重要的作用. 因此, 提出一种新的多视图主题模型(multi-view topic model, MVTM), 通过编码微博会话网络中的异构社交上下文来推断更加完整、连贯的主题. 首先根据用户之间的交互关系构建一个属性多元异构会话网络, 并将其分解为具有不同交互语义的多个视图; 接着, 考虑不同交互方式与不同用户的重要性, 借助邻居级注意力和交互级注意力机制, 得到特定视图的嵌入表示; 最后, 设计一个多视图驱动的神经变分推理方法, 以捕捉不同视图之间的深层关联, 并自适应地平衡它们的一致性和独立性, 从而产生更连贯的主题. 在3个月新浪微博数据集上的实验结果证明所提方法的有效性.     

Strategic self-presentation online: A cross-cultural study

Contemporary social networking sites (SNSs) make idealized self-presentation and image maintenance difficult because users’ audiences are becoming more diverse and individual users must negotiate often unanticipated other-provided information in the form of text posts and digital images on their profile pages. This cross-cultural study examines how audience-related variables affect a range of strategic self-presentation and image management behaviors online. Results from samples of Singaporean and American SNS users (N = 411) show that while Americans update their profiles with text-based wall posts more frequently, Singaporeans share significantly more photos. Audience diversity is positively associated with active management of other-provided information, and females share more photos and actively manage unwanted photo tagging. Cultural identity and the tendency to ‘friend’ unknown others interact on managing other-provided wall posts; individualistic cultural identity exhibited positive relationships with these reactions for those less likely to friend unknown others but negative ones for those more likely to friend unknown others. Implications for the theoretical understanding of and practical suggestions about self-presentation online are discussed.     

Understanding the mechanism of social tie in the propagation process of social network with communication channel

The propagation of information in online social networks plays a critical role in modern life, and thus has been studied broadly. Researchers have proposed a series of propagation models, generally, which use a single transition probability or consider factors such as content and time to describe the way how a user activates her/his neighbors. However, the research on the mechanism how social ties between users play roles in propagation process is still limited. Specifically, comprehensive summary of factors which affect user’s decision whether to share neighbor’s content was lacked in existing works, so that the existing models failed to clearly describe the process a user be activated by a neighbor. To this end, in this paper, we analyze the close correspondence between social tie in propagation process and communication channel, thus we propose to exploit the communication channel to describe the information propagation process between users, and design a social tie channel (STC) model. The model can naturally incorporate many factors affecting the information propagation through edges such as content topic and user preference, and thus can effectively capture the user behavior and relationship characteristics which indicate the property of a social tie. Extensive experiments conducted on two real-world datasets demonstrate the effectiveness of our model on content sharing prediction between users.     

Learning from context: A mutual reinforcement model for Chinese microblog opinion retrieval

This study addresses the problem of Chinese microblog opinion retrieval, which aims to retrieve opinionated Chinese microblog posts relevant to a target specified by a user query. Existing studies have shown that lexicon-based approaches employed online public sentiment resources to rank sentimentwords relying on the document features. However, this approach could not be effectively applied to microblogs that have typical user-generated content with valuable contextual information: “user–user” interpersonal interactions and “user–post/comment” intrapersonal interactions. This contextual information is very helpful in estimating the strength of sentiment words more accurately. In this study, we integrate the social contextual relationships among users, posts/comments, and sentiment words into a mutual reinforcement model and propose a unified three-layer heterogeneous graph, on which a random walk sentiment word weighting algorithm is presented to measure the strength of opinion of the sentiment words. Furthermore, the weights of sentiment words are incorporated into a lexicon-based model for Chinese microblog opinion retrieval. Comparative experiments are conducted on a Chinese microblog corpus, and the results show that our proposed mutual reinforcement model achieves significant improvement over previous methods.     

一种基于用户播放行为序列的个性化视频推荐策略

本文针对在线视频服务网站的个性化推荐问题,提出了一种基于用户播放行为序列的个性化推荐策略.该策略通过深度神经网络词向量模型分析用户播放视频行为数据,将视频映射成等维度的特征向量,提取视频的语义特征.聚类用户播放历史视频的特征向量,建模用户兴趣分布矩阵.结合用户兴趣偏好和用户观看历史序列生成推荐列表.在大规模的视频服务系统中进行了离线实验,相比随机算法、基于物品的协同过滤和基于用户的协同过滤传统推荐策略,本方法在用户观看视频的Top-N推荐精确率方面平均分别获得22.3%、30.7%和934%的相对提升,在召回率指标上分别获得52.8%、41%和1065%的相对提升.进一步地与矩阵分解算法SVD++、基于双向LSTM模型和注意力机制的Bi-LSTM+Attention算法和基于用户行为序列的深度兴趣网络DIN比较,Top-N推荐精确率和召回率也得到了明显提升.该推荐策略不仅获得了较高的精确率和召回率,还尝试解决传统推荐面临大规模工业数据集时的数据要求严苛、数据稀疏和数据噪声等问题.     

Users joining multiple sites: Friendship and popularity variations across sites

Our social media experience is no longer limited to a single site. We use different social media sites for different purposes and our information on each site is often partial. By collecting complementary information for the same individual across sites, one can better profile users. These profiles can help improve online services such as advertising or recommendation across sites. To combine complementary information across sites, it is critical to understand how information for the same individual varies across sites. In this study, we aim to understand how two fundamental properties of users vary across social media sites. First, we study how user friendship behavior varies across sites. Our findings show how friend distributions for individuals change as they join new sites. Next, we analyze how user popularity changes across sites as individuals join different sites. We evaluate our findings and demonstrate how our findings can be employed to predict how popular users are likely to be on new sites they join.     

The influence of user affect in online information disclosure

Personal consumer data is the fuel for information driven programs that may differentiate a firm from its competitors and create strategic advantages. However, a tension exists between the user’s desire to protect personal information and the needs of online businesses for consumer data that drive customer relationship and business intelligence applications. This study explores the roles of positive and negative affect on users’ trust and privacy beliefs that relate to the online disclosure of personal information. A model is tested using the responses of 301 Internet users who visited one of two commercial websites. The results indicate that positive affect has a significant effect on users’ website trust and privacy beliefs that motivate online information disclosure and this effect is more pronounced for users with high Internet security concerns. The idea that positive mood-inducing website features can motivate user behavior has the potential to guide the development of websites for effective information disclosure and data collection.     

Representative reviewers for Internet social media

Our many various relationships with persons from home, work and school give rise to our social networks. In a social network, people receive, provide, and pass a great deal of information. In this process, we often observe that certain individuals have especially strong influences on others. We call these highly influential people opinion leaders. Since the late 20th century, the number of Internet users has increased rapidly, and a huge number of people now interact with each other in online social networks. In this way, the Web community has become similar to real-world society. Internet users receive information not only from the mass media, but also from opinion leaders. For example, online articles posted by influential bloggers are often used as marketing tools or political advertisements, due to their huge influence on other users. Therefore, it is important and useful to identify the influential users in an online society. We thus propose a simple yet reliable algorithm that identifies opinion leaders in a cyber social network. In this paper, we first describe our algorithm for identifying influential users in an online society. We then demonstrate the validity of the selection of representative reviewers using the Yahoo! music and GroupLens movie databases and performing 10-fold cross-validation and z-tests.     

The influence of information cascades on online purchase behaviors of search and experience products

Online users usually observe or refer to others’ behaviors and discount their own information when purchasing products online. This research employed a fixed-effect regression model to elucidate how information cascades could influence online purchase behaviors and how they moderated the influence of online word-of-mouth and product prices. To uncover the underlying mechanisms behind informational cascades, we compare search products and experience products. In particular, we utilize publicly available data from a B2C e-commerce site in China, i.e., Tmall.com. Our results indicate that online users’ choice of products was heavily driven by changes in product rankings after controlling for cumulative sales, online user reviews and product price, as predicted by informational cascades theory. Due to the information cascades effect, review volume had no impact on online users’ choice of products with high rankings, whereas it did exert a significant positive impact on consumer purchase decisions of products with low rankings. User rating had no impact on online users’ purchase decisions. Product price had a significant and negative impact for products with high rankings, but had a significant and positive influence on users’ choice for products with low rankings. Moreover, information cascades were more prominent for experience goods than for search goods.     

When ‘friends’ collide: Social heterogeneity and user vulnerability on social network sites

The present study examines how the use of social network sites (SNS) increases the potential of experiencing psychological, reputational and physical vulnerability online. From our theoretical perspective, concerns over the use of social network sites and online vulnerability stem from the ease with which users can amass large and diverse sets of online social connections and the associated maintenance costs. To date most studies of online vulnerability have relied on self-report measures, rarely combining such information with user's validated digital characteristics. Here, for a stratified sample of 177 UK-based Facebook users aged 13 to 77, digitally derived network data, coded for content and subjected to structural analysis, were integrated with self-report measures of social network heterogeneity and user vulnerability. Findings indicated a positive association between Facebook network size and online vulnerability mediated by both social diversity and structural features of the network. In particular, network clustering and the number of non-person contacts were predictive of vulnerability. Our findings support the notion that connecting to large networks of online ‘friends’ can lead to increasingly complex online socialising that is no longer controllable at a desirable level.     

位置服务社交网络用户行为相似性分析

基于位置的社交网络(LBSN)能够支持用户分享地理位置信息,网站中保存用户访问真实世界地理位置的记录构成用户的行为轨迹,但LBSN用户相似性的分析并没有从用户的地理位置轨迹上加以考虑。为此,提出基于划分层次,在不同的邻域半径下密度聚类的方法,探索基于位置的服务(LBS)平台上用户地理位置上相似性的度量。该方法在不同空间位置比例尺下观察用户访问各个聚类区域的次数,进而利用向量空间模型(VSM)计算用户在各个层级的相似性,最终以不同权重叠加各层级的用户相似性值,得出用户在地理空间行为上的相似性。基于国内某大型位置社交网站真实用户数据的实验结果表明,该方法能有效识别出访问地理位置相似的用户。     

To tweet or ‘subtweet’?: Impacts of social networking post directness and valence on interpersonal impressions

Subtweeting denotes using an SNS post to talk about another user behind his or her back in a public forum. This study investigates how others in the online environment view subtweeters and their messages. The experiment involved manipulating SNS posts in terms of directness of reference to another user (tweets versus subtweets) and valence of information (face-threatening versus face-giving messages). Results demonstrated that, overall, subtweets were perceived as lower in message competence, which led to less favorable interpersonal impressions of their sources. However, directness and valence interacted. Subtweeting led to less favorable impressions of face-giving posts, but more favorable impressions of face-threatening posts. Sources of direct, face-giving posts were rated most favorably. We present implications for Face Theory and politeness, and practical guidance for SNS users.