Social media,design and creative citizenship: an introduction

ABSTRACT

This article serves as an introduction to a special issue of the journal Digital Creativity, which addresses the challenges, potentials and meaning of different creative forms of citizenship enacted through social media. The articles in this special issue paint a complex picture of the expressions and meaning and citizenship. This introduction aims to provide a wider context for the contributions to this special issue by reviewing some of the claims made for the creative civic potential of social media and identifying a number of issues and questions that might inform analyses of the subject.     

基于兴趣的社交网络用户聚类及可视化

随着社交网络的流行,从各种各样的社交网络数据中提取出有效信息并进行清晰直观的可视化分析,从而为用户提供有价值的潜在知识,显得尤为重要。聚类分析是数据挖掘中的重要分析手段,传统的面向社交网络数据的用户聚类分析大都仅考虑网络的拓扑链接结构,未考虑用户的兴趣相似度。文中基于贝叶斯概率模型来计算用户兴趣相似度并进行聚类,进一步设计交互可视化方式来展示上述聚类结果。具体地,针对社交网络中的用户评分数据 建立潜在语义模型来提取表示每个用户兴趣特点的特征向量;基于用户的特征向量对用户进行聚类,得到具有不同特征的人群,并通过实验和热度图选择合适的人群聚类数;最后提出了基于层次气泡图的可视化展现和分析方案,将用户、电影类型、电影等多维信息在图形中交互展示,支持用户从全局概览到局部细节的推进式探索,从多角度可视化人群特征。对豆瓣网用户和电影评分数据进行了实验和分析,结果验证了所提方法的有效性。     

社交网络服务系统核心功能的设计与实现

现如今,社交网站早已经走进了大家的生活,使用社交网络服务的人数与日俱增,不论是在信息传播还是在社会影响力方面,社交网络服务都对传统方式产生的很大的冲击。因此,本文介绍了社交网络发展的历史、现状,并着重分析了社交网络的核心功能更和实现方法。同时,介绍了当今社交网络使用的主要技术,以及它们的使用方法。另外,作为一个已经产生近10多年的网络事物,伴随着各种新技术的出现,对社交网络进行变革的要求变得越来越迫切。基于位置的服务、即时网络服务是其中的两大趋势。通过结合定位引擎技术,本文中介绍的系统实现了基于位置的社交网络服务;通过与手机客户端的整合,用户通过手机登陆社交网络的同时也实现了即时网络服务,在本文的最后对下一代社交网络的发展做出了展望。文章是对此次毕业设计的总结,其中详细介绍了社交网络服务系统核心功能的调研、设计与实现,还有定位服务端与Android手机客户端的通讯和整合。并通过展示服务器功能模块的核心代码的方式,展现了整个系统良好的结构和具体实现操作方法。     

基于PageRank的社交网络影响最大化传播模型与算法研究

社交网络中影响最大化问题是指找出最具有影响力的k个节点,使得最终社交网络中被影响的节点最多,信息传播范围最大。针对影响最大化问题,目前已存在一些基本传播模型,但是这些模型没有考虑网络中节点的相关性和重要性,而网络中节点的相关性和重要性是衡量其影响力的一个重要指标,因此,提出了一种基于网页排名算法的信息传播模型(PageRank-based Propagation Model,PRP),然后利用贪心算法来近似求解影响最大化问题。实验结果表明,基于PageRank的传播模型解决影响最大化问题的效果比传统的线性阈值模型、加权级联模型和独立级联模型的效果更好,影响力范围更大。     

A note on maximizing the spread of influence in social networks

We consider the spread maximization problem that was defined by Domingos and Richardson (2001, 2002) [7] and [22]. In this problem, we are given a social network represented as a graph and are required to find the set of the most “influential” individuals that by introducing them with a new technology, we maximize the expected number of individuals in the network, later in time, that adopt the new technology. This problem has applications in viral marketing, where a company may wish to spread the rumor of a new product via the most influential individuals in popular social networks such as Myspace and Blogsphere.The spread maximization problem was recently studied in several models of social networks (Kempe et al. (2003, 2005) [14] and [15], Mossel and Roch (2007) [20]). In this short paper we study this problem in the context of the well studied probabilistic voter model. We provide very simple and efficient algorithms for solving this problem. An interesting special case of our result is that the most natural heuristic solution, which picks the nodes in the network with the highest degree, is indeed the optimal solution.     

Adoption of Open Source Software: The role of social identification

While the benefits of incorporating Open Source Software (OSS) into personal and organizational systems have been widely touted, OSS must be adopted and used by end users before these benefits can be realized. Drawing on research in information systems and sociology, this study develops and evaluates an integrated model for the acceptance of OSS. In addition to the traditional technology adoption variables the findings stress the importance of social identification as a key driver of OSS adoption. The proposed model provides a useful decision support tool for assessing and proactively designing interventions targeted at successful OSS adoption and diffusion.     

Impact of network effects and diffusion channels on home computer adoption

Researchers have identified network effects as one of the major drivers for the adoption and diffusion of household technologies. However, the mechanisms and social contexts through which network effects induce technology adoption are unclear. In this paper, we investigate the adoption of household computers using the dataset from the 1989-2003 Computer and Internet Supplement to the Current Population Surveys (CPS). We argue that social influence and learning play a dominant role in inducing the adoption of home computers, and workplaces and schools are important channels through which network effects take place. We find that recent adopters have a stronger impact than distant adopters on future computer adoptions. When the adoption rate is low, channels play a more important role, but their effects diminish as the adoption rate grows. We also find that diffusion channels are more effective for first-time adoption than for repeat purchases. Overall, our study provides important theoretical, policy, and managerial implications.     

A new clustering method and its application in social networks

In a graph theory model, clustering is the process of division of vertices into groups, with a higher density of edges within groups than between them. In this paper, we introduce a new clustering method for detecting such groups and use it to analyse some classic social networks. The new method has two distinguished features: non-binary hierarchical tree and the feature of overlapping clustering. A non-binary hierarchical tree is much smaller than the binary-trees constructed by most traditional methods and, therefore, it clearly highlights meaningful clusters which significantly reduces further manual efforts for cluster selections. The present method is tested by several bench mark data sets for which the community structure was known beforehand and the results indicate that it is a sensitive and accurate method for extracting community structure from social networks.     

On positive influence dominating sets in social networks

In this paper, we investigate the positive influence dominating set (PIDS) which has applications in social networks. We prove that PIDS is APX-hard and propose a greedy algorithm with an approximation ratio of H(δ) where H is the harmonic function and δ is the maximum vertex degree of the graph representing a social network.