Hamshahri: A standard Persian text collection

The Persian language is one of the dominant languages in the Middle East, so there are significant amount of Persian documents available on the Web. Due to the different nature of the Persian language compared to the other languages such as English, the design of information retrieval systems in Persian requires special considerations. However, there are relatively few studies on retrieval of Persian documents in the literature and one of the main reasons is the lack of a standard test collection. In this paper, we introduce a standard Persian text collection, named Hamshahri, which is built from a large number of newspaper articles according to TREC specifications. Furthermore, statistical information about documents, queries and their relevance judgments are presented in this paper. We believe that this collection is the largest Persian text collection, so far.     

“Newbies” and “Celebrities”: Detecting social roles in an online network of teachers via participation patterns

The advent of social networking tools allows teachers to create online networks and share information. While some virtual networks have a formal structure and defined boundaries, many do not. These unstructured virtual networks are difficult to study because they lack defined boundaries and a formal structure governing leadership roles and the transfer of information. The purpose of the study was to explore the relationship between how a member participates in a virtual blog network and the role of that member in the network. Unlike previous studies that use behavioral or structural characteristics of an individual’s network to infer social roles, this study utilized cluster analysis to combine behavior and structural information in role detection. Quantitative methods from social network analysis were used to compare the network structure of individual bloggers both across and within groups. The results indicate that how an individual participates in the network has an influence not only on their current role in the network, but also in how and how quickly their role in the community changes.     

Finding the top influential bloggers based on productivity and popularity features

A blog acts as a platform of virtual communication to share comments or views about products, events and social issues. Like other social web activities, blogging actions spread to a large number of people. Users influence others in many ways, such as buying a product, having a particular political or social opinion or initiating new activity. Finding the top influential bloggers is an active research domain as it helps us in various fields, such as online marketing, e-commerce, product search and e-advertisements. There exist various models to find the influential bloggers, but they consider limited features using non-modular approach. This paper proposes a new model, Popularity and Productivity Model (PPM), based on a modular approach to find the top influential bloggers. It consists of popularity and productivity modules which exploit various features. We discuss the role of each proposed and existing features and evaluate the proposed model against the standard baseline models using datasets from the real-world blogs. The analysis using standard performance evaluation measures verifies that both productivity and popularity modules play a vital role to find influential bloggers in blogging community in an effective manner.     

基于ESU图的活动社交网络用户参加活动推荐

活动社交网络(EBSNs)为用户提供了方便的组织、参加和分享社交活动的平台。该文面向EBSNs活动推荐问题,提出了包含活动(Event)、主办方(Sponsor)和用户(User)的ESU图模型,深入揭示了EBSNs的实体及其社交关系。因为用户参加活动受多个因素影响,我们提出了基于ESU图的活动推荐多因素决策模型,包括社交影响力、活动内容、活动地点及活动时间。根据ESU图特点,提出了基于双向重启随机游走算法BD-RWR的实体重要度计算方法。选取真实的EBSNs平台—豆瓣同城验证所提方法的有效性。实验结果表明,该文提出的ESU图模型及融合了多因素的活动推荐模型,与已有最新方法相比,有效地提升了用户参加活动的推荐效果。     

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.     

Multi-relational reinforcement for computing credibility of nodes

Nowadays, growing number of social networks are available on the internet, with which users can conveniently make friends, share information, and exchange ideas with each other. As the result, large amount of data are generated from activities of those users. Such data are regarded as valuable resources to support different mining tasks, such as predicting friends for a user, ranking users in terms of their influence on the social network, or identifying communities with common interests. Traditional algorithms for those tasks are often designed under the assumption that a user selects another user as his friend based on their common interests. As a matter of fact, users on a social network may not always develop their friends with common interest. For example, a user may randomly select other users as his friends just in order to attract more links reversely from them. Therefore, such links may not indicate his influence. In this paper, we study the user rank problem in terms of their ‘real’ influences. For this sake, common interest relationships among users are established besides their friend relationships. Then, the credible trust link from one node to another is on account of their similarities, which means the more similar the two users, the more credible their trust relation. So the credibility of a node is high if its trust inlinks are credible enough. In this work, we propose a framework that computes the credibility of nodes on a multi-relational network using reinforcement techniques. To the best of our knowledge, this is the first work to assess credibility exploited knowledge on multi-relational social networks. The experimental results on real data sets show that our framework is effective.     

Blogging: self presentation and privacy

Blogs are permeating most niches of social life, and addressing a wide range of topics from scholarly and political issues1 to family and children's daily lives. By their very nature, blogs raise a number of privacy issues as they are easy to produce and disseminate, resulting in large amounts of sometimes personal information being broadcast across the Internet in a persistent and cumulative manner. This article reports the preliminary findings of an online survey of bloggers from around the world. The survey explored bloggers' subjective sense of privacy by examining their blogging practices and their expectations of privacy when publishing online. The findings suggest that blogging offers individuals a unique opportunity to work on their self-identity via the degree of self-expression and social interaction that is available in this medium. This finding helps to explain why bloggers consciously bring the 'private' to the public realm, despite the inherent privacy risks they face in doing so.     

Factors influencing the popularity of customer-generated content in a company-hosted online co-creation community: A social capital perspective

With the current trend of co-creation, companies and consumers increasingly take advantage of online co-creation communities to share and exchange product-related information. In response to the knowledge exchange and collaborative nature of online co-creation, we advance the theoretical understanding of consumer co-creation by considering it as mutual effects and interactions with other consumers. Based on social capital theory, we developed a research model to examine the factors influencing the popularity of customer-generated content using objective data collected from the MIUI online co-creation community in China. A key result of this study is that social networks ties among consumers enable the norm of reciprocity and shared language, which, in turn, influence the popularity of customer-generated content. Theoretical and practical implications are discussed.     

The provision of online public goods: Examining social structure in an electronic network of practice

Electronic networks of practice are computer-mediated social spaces where individuals working on similar problems self-organize to help each other and share knowledge, advice, and perspectives about their occupational practice or common interests. These interactions occur through message postings to produce an on-line public good of knowledge, where all participants in the network can then access this knowledge, regardless of their active participation in the network. Using theories and concepts of collective action and public goods, five hypotheses are developed regarding the structural and social characteristics that support the online provision and maintenance of knowledge in an electronic network of practice. Using social network analysis, we examine the structure of message contributions that produce and sustain the public good. We then combine the results from network analysis with survey results to examine the underlying pattern of exchange, the role of the critical mass, the quality of the ties sustaining participation, the heterogeneity of resources and interests of participants, and changes in membership that impact the structural characteristics of the network. Our results suggest that the electronic network of practice chosen for this study is sustained through generalized exchange, is supported by a critical mass of active members, and that members develop strong ties with the community as a whole rather than develop interpersonal relationships. Knowledge contribution is significantly related to an individual's tenure in the occupation, expertise, availability of local resources and a desire to enhance one's reputation, and those in the critical mass are primarily responsible for creating and sustaining the public good of knowledge. Finally, we find that this structure of generalized exchange is stable over time although there is a high proportion of member churn in the network.     

Spatial-aware interest group queries in location-based social networks

With the recent advances in positioning and smartphone technologies, a number of social networks such as Twitter, Foursquare and Facebook are acquiring the dimension of location, thus bridging the gap between the physical world and online social networking services. Most of the location-based social networks released check-in services that allow users to share their visiting locations with their friends. In this paper, users' interests are modeled by check-in actions. We propose a new type of Spatial-aware Interest Group (SIG) query that retrieves a user group of size k where each user is interested in the query keywords and they are close to each other in the Euclidean space. We prove that the SIG query problem is NP-complete. A family of efficient algorithms based on the IR-tree is thus proposed for the processing of SIG queries. Experiments on two real datasets show that our proposed algorithms achieve orders of magnitude improvement over the baseline algorithm.     

Inferring preference correlations from social networks

Identifying consumer preferences is a key challenge in customizing electronic commerce sites to individual users. The increasing availability of online social networks provides one approach to this problem: people linked in these networks often share preferences, allowing inference of interest in products based on knowledge of a consumer’s network neighbors and their interests. This paper evaluates the benefits of inference from online social networks in two contexts: a random graph model and a web site allowing people to both express preferences and form distinct social and preference links. We determine conditions on network topology and preference correlations leading to extended clusters of people with similar interests. Knowledge of when such clusters occur improves the usefulness of social network-based inference for identifying products likely to interest consumers based on information from a few people in the network. Such estimates could help sellers design customized bundles of products and improve combinatorial auctions for complementary products.     

Bloggers and Readers Blogging Together: Collaborative Co-creation of Political Blogs

A significant amount of research has focused on blogs, bloggers, and blogging. However, relatively little work has examined blog readers, their interactions with bloggers, or their impact on blogging. This paper presents a qualitative study focusing specifically on readers of political blogs to develop a better understanding of readers?? interactions with blogs and bloggers. This is the first such study to examine the same blogging activity from both readers?? and bloggers?? perspectives. Readers?? significance and contributions to blogs are examined through a number of themes, including: community membership and participation; the relationship between political ideology, reading habits, and political participation; and differences and similarities between mainstream media (MSM) and blogs. Based on these analyses, this paper argues that blogging is not only a social activity, but is a collaborative process of co-creation in which both bloggers and readers engage. Implications of this finding contribute to the study and understanding of reader participation, to the design of technologies for bloggers and blog readers, and to the development of theoretical understandings of social media.     

Post-adoption switching behavior for online service substitutes: A perspective of the push–pull–mooring framework

The post-adoption behaviors of online service users are critical performance factors for online service providers. To fill an academic gap that persists regarding bloggers’ switching behavior across online service substitutes, this empirical study investigates which factors affect bloggers who switch social network sites, in an attempt to understand specifically how push, pull, and mooring factors shape their switching intentions. The data to test the hypotheses come from an online survey of 319 bloggers, analyzed using partial least squares techniques. The results confirm positive influences of push and pull effects, a negative influence of mooring effects, and an interactive effect of push and mooring on switching intentions. The push–pull–mooring framework thus is a useful tool for comprehending the competing forces that influence the use of online service substitutes. In particular, perceptions of weak connections and writing anxiety push bloggers away, whereas relative enjoyment and usefulness pull bloggers to social network sites; switching cost and past experience also inhibit a change. These findings offer key insights and implications for the competitive strategy choices of online service providers.     

CSD: A multi-user similarity metric for community recommendation in online social networks

Communities are basic components in networks. As a promising social application, community recommendation selects a few items (e.g., movies and books) to recommend to a group of users. It usually achieves higher recommendation precision if the users share more interests; whereas, in plenty of communities (e.g., families, work groups), the users often share few. With billions of communities in online social networks, quickly selecting the communities where the members are similar in interests is a prerequisite for community recommendation. To this end, we propose an easy-to-compute metric, Community Similarity Degree (CSD), to estimate the degree of interest similarity among multiple users in a community. Based on 3460 emulated Facebook communities, we conduct extensive empirical studies to reveal the characteristics of CSD and validate the effectiveness of CSD. In particular, we demonstrate that selecting communities with larger CSD can achieve higher recommendation precision. In addition, we verify the computation efficiency of CSD: it costs less than 1 hour to calculate CSD for over 1 million of communities. Finally, we draw insights about feasible extensions to the definition of CSD, and point out the practical uses of CSD in a variety of applications other than community recommendation.     

Verbal irony use in personal blogs

Blogs are a widely growing form of computer-mediated communication used to achieve various personal and professional communicative goals. In the present study, we examined previously posted entries from 71 regular bloggers. We examined the blogs for the use of five forms of verbal irony: hyperbole, understatement, rhetorical question, sarcasm and jocularity. In addition, topic and emotional valence of the ironic utterances were examined. Results showed that hyperbole and understatement were more frequently used than the other forms of ironic language. Discussion of hobbies and social outings was the most commonly occurring topic of ironic language, and bloggers used verbal irony to convey both positive and negative intent. The results of this study demonstrated that adult bloggers do use a variety of forms of verbal irony in their personal blogs, despite the potential risk of being misunderstood.     

A synthetical approach for blog recommendation: Combining trust,social relation,and semantic analysis

Weblog is a good paradigm of online social network which constitutes web-based regularly updated journals with reverse chronological sequences of dated entries, usually with blogrolls on the sidebars, allowing bloggers link to favorite site which they are frequently visited. In this study we propose a blog recommendation mechanism that combines trust model, social relation and semantic analysis and illustrates how it can be applied to a prestigious online blogging system – wretch in Taiwan. By the results of experimental study, we found a number of implications from the Weblog network and several important theories in domain of social networking were empirically justified. The experimental evaluation reveals that the proposed recommendation mechanism is quite feasible and promising.     

Blogger-Centric Contextual Advertising

Web advertising (online advertising), a form of advertising that uses the World Wide Web to attract customers, has become one of the most commonly-used marketing channels. This paper addresses the concept of Blogger-Centric Contextual Advertising, which refers to the assignment of personal ads to any blog page, chosen in according to bloggers’ interests. As blogs become a platform for expressing personal opinions, they naturally contain various kinds of statements, including facts, comments and statements about personal interests, of both a positive and negative nature. To extend the concept behind the Long Tail theory in contextual advertising, we argue that web bloggers, as the constant visitors of their own blog-sites, could be potential consumers who will respond to ads on their own blogs. Hence, in this paper, we propose using text mining techniques to discover bloggers’ immediate personal interests in order to improve online contextual advertising. The proposed Blogger-Centric Contextual Advertising (BCCA) framework aims to combine contextual advertising matching with text mining in order to select ads that are related to personal interests as revealed in a blog and rank them according to their relevance. We validate our approach experimentally using a set of data that includes both real ads and actual blog pages. The results indicate that our proposed method could effectively identify those ads that are positively-correlated with a blogger’s personal interests.     

Online social networks: A survey of a global phenomenon

Online social networks became a global phenomenon with enormous social as well as economic impact within a few years. Alone, the most popular online social network, Facebook, counts currently more than 850 million users worldwide. Consequently, online social networks attract a great deal of attention among practitioners as well as researchers. The goal of this article is to provide an overview of online social networks in order to contribute to a better understanding of this worldwide phenomenon. In this context, we address for example the following questions: What are the major functionalities and characteristics of online social networks? What are the users’ motives for using them and how did online social networks emerge and develop over time? What is the impact and value of online social networks from a business perspective and what are corresponding challenges and risks?     

Individual characteristics influencing the sharing of knowledge on social networking services: online identity,self-efficacy,and knowledge sharing intentions

ABSTRACT

Social media have become an important place for individuals with similar interests to exchange opinions and share resources. Ubiquitous collaborative learning environments in SNS have great educational and organisational potential and offer a platform for sharing knowledge. Rising interest in personal factors in knowledge management research calls for a better understanding of how knowledge is shared in SNS-based collaborative learning. Grounded on the theories of planned behaviour and social identity, this study investigates individual characteristics that affect the sharing of knowledge in SNS learning communities. The study also examines whether intent to share knowledge mediates those personal characteristics and sharing of knowledge. Eighty individuals from five active Facebook groups participated in this study. The findings indicate that individual characteristics, such as online personal identity, web-specific self-efficacy, and knowledge-creation self-efficacy significantly predict sharing of knowledge. In addition, intent to share knowledge mediates between the sharing of knowledge and online identity, web-specific self-efficacy, or knowledge-creation self-efficacy. The results reveal a need for customised support and environmental design focusing on online personal identity, web-specific self-efficacy, knowledge-creation self-efficacy, and intent to share knowledge in a knowledge-sharing community on SNS. Implications for the SNS communities and suggestions for further research are discussed.     

微博网络测量研究

随着移动通信和Web技术的不断突破,以微博为代表的在线社会网络在中国广泛发展起来,越来越多的人开始使用微博进行信息分发和舆论传播.为了了解中国微博网络中的拓扑结构特征和用户行为特征等内在信息,对国内最大的微博系统——新浪微博——开展了主动测量,并结合已有的在线社会网络测量结果,对新浪微博的网络拓扑和用户行为特征进行了分析和比较.主要发现包括:1)新浪微博网络具有小世界特性;2)新浪微博网络的入度分布属于幂次分布,而出度分布表现为某种分段幂率函数;3)与类似社会网络相比,新浪微博网络的出入度不具有相关性;4)新浪微博网络属于同配网络;5)新浪微博用户发博时间具有明显的日分布和周分布模式;6)新浪微博用户博文数目分布表现为威布尔分布;7)新浪微博用户博文的转发和评价行为具有很强的相关性,且博文转发概率要高于评价概率.这些测量研究和发现不仅有助于设计出符合中国微博网络结构特征的数学模型和计算模型,也是实现对微博舆论的监测、引导、控制等方面的重要依据和基础.