Adaptive neighborhood selection in peer-to-peer networks based on content similarity and reputation

To address the two most critical issues in P2P file-sharing systems: efficient information discovery and authentic data acquisition, we propose a Gnutella-like file-sharing protocol termed Adaptive Gnutella Protocol (AGP) that not only improves the querying efficiency in a P2P network but also enhances the quality of search results at the same time. The reputation scheme in the proposed AGP evaluates the credibility of peers based on their contributions to P2P services and subsequently clusters nodes together according to their reputation and shared content, essentially transforming the P2P overlay network into a topology with collaborative and reputed nodes as its core. By detecting malicious peers as well as free-riders and eventually pushing them to the edge of the overlay network, our AGP propagates search queries mainly within the core of the topology, accelerating the information discovery process. Furthermore, the clustering of nodes based on authentic and similar content in our AGP also improves the quality of search results. We have implemented the AGP with the PeerSim simulation engine and conducted thorough experiments on diverse network topologies and various mixtures of honest/dishonest nodes to demonstrate improvements in topology transformation, query efficiency, and search quality by our AGP.

Querying dynamic communities in online social networks简

Online social networks （OSNs） offer people the opportunity to join communities where they share a common interest or objective. This kind of community is useful for studying the human behavior, diffusion of information, and dynamics of groups. As the members of a community are always changing, an efficient solution is needed to query information in real time. This paper introduces the Follow Model to present the basic relationship between users in OSNs, and combines it with the MapReduce solution to develop new algorithms with parallel paradigms for querying. Two models for reverse relation and high-order relation of the users were implemented in the Hadoop system. Based on 75 GB message data and 26 GB relation network data from Twitter, a case study was realized using two dynamic discussion communities：#musicmonday and #beatcancer. The querying performance demonstrates that the new solution with the implementation in Hadoop significantly improves the ability to find useful information from OSNs.     

Discovering public sentiment in social media for predicting stock movement of publicly listed companies

The popularity of many social media sites has prompted both academic and practical research on the possibility of mining social media data for the analysis of public sentiment. Studies have suggested that public emotions shown through Twitter could be well correlated with the Dow Jones Industrial Average. However, it remains unclear how public sentiment, as reflected on social media, can be used to predict stock price movement of a particular publicly-listed company. In this study, we attempt to fill this research void by proposing a technique, called SMeDA-SA, to mine Twitter data for sentiment analysis and then predict the stock movement of specific listed companies. For the purpose of experimentation, we collected 200 million tweets that mentioned one or more of 30 companies that were listed in NASDAQ or the New York Stock Exchange. SMeDA-SA performs its task by first extracting ambiguous textual messages from these tweets to create a list of words that reflects public sentiment. SMeDA-SA then made use of a data mining algorithm to expand the word list by adding emotional phrases so as to better classify sentiments in the tweets. With SMeDA-SA, we discover that the stock movement of many companies can be predicted rather accurately with an average accuracy over 70%. This paper describes how SMeDA-SA can be used to mine social media date for sentiments. It also presents the key implications of our study.     

From blurry numbers to clear preferences: A mechanism to extract reputation in social networks

Complex social networks are typically used in order to represent and structure social relationships that do not follow a predictable pattern of behaviour. Due to their openness and dynamics, these networks make participants continuously deal with uncertainty before any type of interaction. Reputation appears as a key concept helping users to mitigate such uncertainty. Most of the reputation mechanisms proposed in the literature are based on numerical opinions (ratings), and consequently, they are exposed to potential problems such as the subjectivity in the opinions and their consequent inaccurate aggregation. With these problems in mind, this paper presents a reputation mechanism based on the concepts of pairwise elicitation processes and knock-out tournaments. The main objective of this mechanism is to build reputation rankings from qualitative opinions, thereby removing the subjectivity problems associated with the aggregation of quantitative opinions. The proposed approach is evaluated with different data sets from the MovieLens and Flixster web sites.     

The impact of reputation on supply chains. An analysis of permanent and discounted reputation

In long-term recurring contractual relationships, which are common in the B2B-arena, reputation and trust play a crucial role. This analysis investigates the joint impact of reputation and price-based ranking of suppliers on the material flow in the supply chain. Positive reputation proves to be a key factor in reaching dominating market positions, which illustrates the importance of building brand awareness in all stages of a supply chain. Through our simulation, it will be observed that the ranking of suppliers by reputation-based choice has a stabilizing effect on the material flow in the supply chain. A strong reputation component in the individual choice stimulates the formation of monopolies, while the discount of reputation imposes a countertendency on this effect. The Bullwhip Effect, another phenomenon that carries a countertendency to the reputation-based monopoly effect, is observed to be even stronger for members of tiers with a high fluctuation of order rates.     

A novel approach to stance detection in social media tweets by fusing ranked lists and sentiments

Stance detection is a relatively new concept in data mining that aims to assign a stance label (favor, against, or none) to a social media post towards a specific pre-determined target. These targets may not be referred to in the post, and may not be the target of opinion in the post. In this paper, we propose a novel enhanced method for identifying the writer’s stance of a given tweet. This comprises a three-phase process for stance detection: (a) tweets preprocessing; here we clean and normalize tweets (e.g., remove stop-words) to generate words and stems lists, (b) features generation; in this step, we create and fuse two dictionaries for generating features vector, and lastly (c) classification; all the instances of the features are classified based on the list of targets. Our innovative feature selection proposes fusion of two ranked lists (top-$k$) of term frequency-inverse document frequency (tf-idf) scores and the sentiment information. We evaluate our method using six different classifiers: $K$ nearest neighbor (K-NN), discernibility-based K-NN, weighted K-NN, class-based K-NN, exemplar-based K-NN, and Support Vector Machines. Furthermore, we investigate the use of Principal Component Analysis and study its effect on performance. The model is evaluated on the benchmark dataset (SemEval-2016 task 6), and the results significance is determined using t-test. We achieve our best performance of macro $F$-score (averaged across all topics) of 76.45% using the weighted K-NN classifier. This tops the current state-of-the-art score of 74.44% on the same dataset.     

复杂网络中的在线社交网络演化模型

在线社交网络是一种广泛存在的社会网络,其节点度遵循幂率分布规律,但对于其结构演化模型方面的相关研究还不多。基于复杂网络理论研究在线社交网络内部结构特征,提出一种结合内增长、外增长及内部边更替的演化模型,借助平均场理论分析该模型的拓扑特性,实验和理论分析表明由该模型生成的网络,其度分布服从幂率分布,且通过调整参数,幂率指数在1~3,能较好地反映不同类型的真实在线社交网络的度分布特征,因此具有广泛适用性。     

基于拍卖机制的网格在线信誉系统模型

在现有的网格经济模型和在线信誉系统的基础上,提出了基于拍卖机制的网格在线信誉系统模型.该模型侧重于保护资源提供者,为资源提供者提供了贡献与共享资源的动机,吸引更多更好的资源加入网格,实现资源优化分配.保证交易双方均获取最大利益,有利于网格资源的市场管理及供需均衡.并以市场为平台,构建一种新的网格信任模型,由交易事件和衰减函数共同驱动信任度的在线更新,并引入激励机制,尽可能增强信任模型的合理性和可操作性.     

Ambient awareness: From random noise to digital closeness in online social networks

Ambient awareness refers to the awareness social media users develop of their online network in result of being constantly exposed to social information, such as microblogging updates. Although each individual bit of information can seem like random noise, their incessant reception can amass to a coherent representation of social others. Despite its growing popularity and important implications for social media research, ambient awareness on public social media has not been studied empirically. We provide evidence for the occurrence of ambient awareness and examine key questions related to its content and functions. A diverse sample of participants reported experiencing awareness, both as a general feeling towards their network as a whole, and as knowledge of individual members of the network, whom they had not met in real life. Our results indicate that ambient awareness can develop peripherally, from fragmented information and in the relative absence of extensive one-to-one communication. We report the effects of demographics, media use, and network variables and discuss the implications of ambient awareness for relational and informational processes online.     

Beyond positive or negative: Qualitative sentiment analysis of social media reactions to unexpected stressful events

Sentiment analysis techniques are increasingly used to grasp reactions from social media users to unexpected and potentially stressful social events. This paper argues that, alongside assessments of the affective valence of social media content as negative or positive, there is a need for a deeper understanding of the context in which reactions are expressed and the specific functions that users' emotional states may reflect. To demonstrate this, we present a qualitative analysis of affective expressions on Twitter collected in Germany during the 2011 EHEC food contamination incident based on a coding scheme developed from Skinner et al.'s (2003) coping classification framework. Affective expressions of coping were found to be diverse not only in terms of valence but also in the adaptive functions they served: beyond the positive or negative tone, some people perceived the outbreak as a threat while others as a challenge to cope with. We discuss how this qualitative sentiment analysis can allow a better understanding of the way the overall situation is perceived – threat or challenge – and the resources that individuals experience having to cope with emerging demands.     

在线社交网络下基于信任度的消息传播模型

社交网络作为一种新兴的媒体具有广泛的社会影响力,且基于社交网络的营销方式逐渐成为一种新的发展趋势,因此研究社交网络中消息的传播具有重大的现实和经济意义。通过借鉴日常生活中人与人之间的信任原理,提出了一种基于信任度的消息传播模型。该模型首先利用个体的公开信息,使用数据挖掘的算法对个体进行分类;然后,根据同类和不同类个体之间的关系计算个体之间的信任度;最后,使用消息与个体的属性相似性以及信任度来计算消息可能传播范围。给出了相应的计算方法,并与两种基准方法对比,结果表明,该模型在准确度上提升15%左右,而所用时间降低50%以上。与数据集统计结果对比,该实验的结果与统计结果相差5%左右,充分表明该模型在实际应用中有比较好的效果。     

基于内容分割的在线作业管理系统

对目前在线作业管理系统的现状与不足进行了分析,通过对作业试题、学生答卷、教师批改等内容的分割、控制与统一保存,设计实现了一个作业的发布、应答、批改、成绩登记等全部在线完成的自动化作业管理系统.系统在实现过程,通过设计一种自定义的作业标记符,解决了作业数据在数据库中的表示与存储问题,减少了作业数据的冗余度.该系统可提高作业管理的自动化程度,方便学生在线答题,有效地减轻了教师批改作业的工作强度.     

Enterprise reputation threats on social media: A case of data breach framing

Reputation threats on social media in the aftermath of a data breach is a critical concern to enterprises. We argue that any effort to minimize reputation threats will require an orderly assessment of how reputation threat manifests on social media. Drawing on crisis communication and social media literature, we analyze Twitter postings related to the 2014 Home Depot data breach. We identify a taxonomy of data breach frames and sub-frames and the related reputation threats as manifested by data breach responsibility-attributions and negative emotional responses. Results indicate that reputation threats vary for intentional, accidental, and victim data breach frames. Based on crisis stage theory, we also analyze the dynamics of evolving reputation threats as data breach situation unfolds on social media. Results suggest that the data breach frames and associated reputation threats vary across the crisis stages. Further, intentional and accidental frames increase subsequent responsibility-attributions and negative emotions. Tweets with responsibility-attributions further increase the subsequent generation of reputation-threatening tweets. Negative emotions, particularly anger and disgust, also increase subsequent reputation threats. Our study has implications for enterprise reputation management and word-of-mouth literature. The results yield valuable insights that can guide enterprise strategy for social media reputation management and post data breach intervention.     

Flocking based evolutionary computation strategy for measuring centrality of online social networks

Centrality in social network is one of the major research topics in social network analysis. Even though there are more than half a dozen methods to find centrality of a node, each of these methods has some drawbacks in one aspect or the other. This paper analyses different centrality calculation methods and proposes a new swarm based method named Flocking Based Centrality for Social network (FBCS). This new computation technique makes use of parameters that are more realistic and practical in online social networks. The interactions between nodes play a significant role in determining the centrality of node. The new method has been calculated both empirically as well as experimentally. The new method is tested, verified and validated for different sets of random networks and benchmark datasets. The method has been correlated with other state of the art centrality measures. The new centrality measure is found to be realistic and suits well with online social networks. The proposed method can be used in applications such as finding the most prestigious node and for discovering the node which can influence maximum number of users in an online social network. FBCS centrality has higher Kendall’s tau correlation when compared with other state of the art centrality methods. The robustness of the FBCS centrality is found to be better than other centrality measures.     

Microblogs recommendations based on implicit similarity in content social networks

With the development of online social networking applications, microblogs have become a necessary online communication network in daily life. Users are interested in obtaining personalized recommendations related to their tastes and needs. In some microblog systems, tags are not available, or the use of tags is rare. In addition, user-specified social relations are extremely rare. Hence, sparsity is a problem in microblog systems. To address this problem, we propose a new framework called Pblog to alleviate sparsity. Pblog identifies users’ interests via their microblogs and social relations and computes implicit similarity among users using a new algorithm. The experimental results indicated that the use of this algorithm can improve the results. In online social networks, such as Twitter, the number of microblogs in the system is high, and it is constantly increasing. Therefore, providing personalized recommendations to target users requires considerable time. To address this problem, the Pblog framework groups similar users using the analytic hierarchy process (AHP) method. Then, Pblog prunes microblogs of the target user group and recommends microblogs with higher ratings to the target user. In the experimental results section, the Pblog framework was compared with several other frameworks. All of these frameworks were run on two datasets: Twitter and Tumblr. Based on the results of these comparisons, the Pblog framework provides more appropriate recommendations to the target user than previous frameworks.

     

Effect of online social networking on student academic performance

Online social networks (OSNs) have permeated all generations of Internet users, becoming a prominent communications tool, particularly in the student community. Thus, academic institutions and faculty are increasingly using social networking sites, such as Facebook and LinkedIn, to connect with current and potential students and to deliver instructional content. This has led to a rise in questions about the impact of OSN on academic performance and the possibility of using it as an effective teaching tool. To learn more about the impact on academic performance, we conducted a survey of business students at a large state university. Survey results were analyzed using structural equation modeling (SEM). The results revealed a statistically significant negative relationship between time spent by students on OSN and their academic performance. The time spent on OSN was found to be heavily influenced by the attention span of the students. Specifically, we determined that the higher the attention span, the lower is the time spent on OSN. Further, attention span was found to be highly correlated with characteristics that predict or influence student behavior, such as their perceptions about society’s view of social networking, their likes and dislikes of OSN, ease of use of OSN, etc.     

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.     

在线社会网络的k-shell结构研究

分析研究了Twitter与You Tube两个在线社会网络的结构。用k-shell(k-壳)分解法对网络分解,并对比分析了它们的入(出)度、入(出)k-shell、以及度与k-shell之间的关系,发现它们之间有较大的差异。You Tube的入(出)度、入(出)k-shell分布均服从幂律分布,而Twitter的分布服从漂移幂律分布、指数截断的幂律分布,但它们的度与k-shell关系基本相同,都未表现出较强的相关性。此外,根据度相关系数的定义还提出k-shell相关性的定义及其计算方法,并用来刻画网络k-shell之间的同(异)配性。