One size does not fit all: Rethinking recognition system design for behaviorally heterogeneous online communities

Online knowledge-sharing communities typically acknowledge their top contributors by implementing recognition systems. Extant recognition systems suffer from several limitations such as treatment of the entire community as homogeneous and inflexibility of customization. We propose a framework based on socio-technical design principles for designing a multi-criterion- and multi-segment-based recognition system that exploits multiple user characteristics for differential recognition according to community goals. We apply this framework on data gathered from Yelp.com and show how it can be used to recognize top members of different identified behavioral segments (amateurs, adepts, and enthusiasts) based on their performance on various relevant factors.     

Political YouTube Channel Reputation (PYCR): Development and validation of a multidimensional scale

Although YouTube is driving change in the news and information environment and earning a reputation as an alternative media, there is a lack of research examining what determines people’s reputational judgments on YouTube’s political channels. Based on in-depth interviews with 32 experts and empirical findings from 380 adults, this research identified factors that impact YouTube users’ reputation perception of political channels and developed a scale. The results showed that the Political YouTube Channel Reputation (PYCR) scale comprises four distinctive dimensions: news credibility, political ideology, user-oriented communication, and quality management. The reliability and validity of the scale were assessed, using exploratory and confirmatory analyses and construct validity. The PYCR scale has a variety of potential applications for users, political influencers, and YouTube, and it can serve as a theoretical framework for future empirical research in this novel and emerging area.     

Determinants of repurchase intention in online group-buying: The perspectives of DeLone & McLean IS success model and trust

The purpose of this study is to propose a theoretical model to examine the antecedents of repurchase intention in online group-buying by integrating the perspective of DeLone & McLean IS success model and the literature of trust. The model was tested using the data collected from 253 customers of a group-buying website in Taiwan. The results show that satisfaction with website, satisfaction with sellers, and perceived quality of website have positive influences on repurchase intention, while perceived quality of website and perceived quality of sellers have significant impacts on satisfaction with website and satisfaction with sellers, respectively. The results also show that trust in website has positive influences on perceived quality of website and satisfaction with website, whereas trust in sellers influence perceived quality of sellers and satisfaction with sellers significantly. Finally, the results show that perceived size of website has positive influence on trust in website, while reputation of website and reputation of sellers significantly affect trust in website and trust in sellers, respectively. The implications for theory and practice and suggestions for future research are also discussed.     

The Story of Collective Action: The Emergence of Ideological Leaders,Collective Action Network Leaders,and Cross-Sector Network Partners in Civil Society

Collective action and community ecology theories frame this study of longitudinal interorganizational networks in Croatia during the country's political transition. As time progresses toward political stability, grass-roots organizing activities shift through participation in new networks. Although engaged cross-sector communication was important in early stages of the transformation, homophilous partnering emerged as the system stabilized. System stability left room for organizations to exit the collective action network but with costs associated with centralized organizing. Over time, organizations embodied roles as ideological leaders, collective action network leaders, and within-sector network partners. We offer a unique contribution to community ecology and collective action theories with a communication-centered framework that emphasizes the nature of communication in interorganizational networks over a 4-year period.     

Towards a trust evaluation middleware for cloud service selection

With the advent of cloud computing, employing various cloud services to build highly reliable cloud applications has become increasingly popular. The trustworthiness of cloud services is a critical issue that hinders the development of cloud applications, and thus is an urgently-required research problem. Previous studies evaluate trustworthiness of services via either QoS monitoring mechanisms or user feedback ratings, while seldom they combine both of them for enhancing service trust evaluation. This paper proposes a trustworthy selection framework for cloud service selection, named TRUSS. Aiming at developing an effective trust evaluation middleware for TRUSS, we propose an integrated trust evaluation method via combining objective trust assessment and subjective trust assessment. The objective trust assessment is based on QoS monitoring, while the subjective trust assessment is based on user feedback ratings. Experiments conducted using a synthesized dataset show that our proposed method significantly outperforms the other trust and reputation methods.     

Online reputation measurement of companies based on user-generated content in online social networks

Social media websites such as Facebook, Twitter, etc. has changed the way peoples communicate and make decision. In this regard, various companies are willing to use these media to raise their reputation. In this paper, a reputation management system is proposed which measures the reputation of a given company by using the social media data, particularly tweets of Twitter. Taking into account the name of the company and its' related tweets, it is determined that a given tweet has either negative or positive impact on the company's reputation or product. The proposed method is based on N-gram learning approach, which consists of two steps: train step and test step. In the training step, we consider four profiles i.e. positive, negative, neutral, and irrelevant profiles for each company. Then 80% of the available tweets are used to build the companies' profiles. Each profile contains the terms that have been appeared in the tweets of each company together with the terms' frequencies. Then in the test step, which is performed on the 20% remaining tweets of the dataset, each tweet is compared with all of the built profiles, based on distance criterion to examine how the given tweet affects a company's reputation. Evaluation of the proposed method indicates that this method has a better efficiency and performance in terms of recall and precision compared to the previous methods such as Neural Network and Bayesian method.     

Trust and reputation management for opportunistic dissemination

Nodes in opportunistic networks need to cooperate to disseminate data. However, employing intermediate nodes for dissemination leads to several security issues. Here, we propose an opportunistic trust and reputation mechanism entitled SAROS, which detects and avoids malicious nodes, i.e. nodes which, upon receiving messages for other interested peers, modify their content in order to spread false information. This can negatively affect the network, by polluting it with spam messages, or dropping messages of interest to the nodes in the network. By detecting and avoiding malicious nodes, SAROS is able to increase the percentage of correct messages that reach their destinations.     

Collective intelligence in law enforcement - The WikiCrimes system

Collaboration is on the rise, primordially leveraged by the Web 2.0 in which the difference between information producer and consumer decreases significantly. In this paper we describe WikiCrimes, an example of a Web 2.0 application that offers a collaborative environment based on the use and direct manipulation of maps, in order to register and research criminal events. WikiCrimes is driven by three goals: (i) to give more transparency and publicity to criminal information, (ii) to provide means for citizen prevention, and (iii) to reduce the phenomenon of under-reporting (crimes that are not notified to authorities). In the paper we pay particular attention to the fact that in this context, finding the equilibrium between people’s participation and information credibility is crucial. Anonymous mass collaboration is the easiest way to receive information; however, the credibility of the received information is depreciated, because the source of information is unknown. We particularly concentrate on the ways we have created to keep the “participation vs. reliability of information” trade-off at good level. The reputation model is the main component created with this purpose. The experiences and the obstacles we are facing in the implementation of the project are the subjects of discussion and analyses.     

Evolutionary cyber-psychology: Applying an evolutionary framework to Internet behavior

An evolutionary psychological perspective has much to offer the study of Internet behavior. However, cyber-psychologists have hitherto neglected this rich theoretical tradition and evolutionary psychologists have been slow to apply their perspective to computer-mediated behavior. This paper applies an evolutionary perspective to the study of Internet behavior in four relevant domains: (1) mating and sexual competition, (2) parenting and kinship, (3) trust and social exchange, and (4) personal information management. Both general and specific evolutionary theories are explored in relation to online behavior in each domain, with an emphasis on generating testable hypotheses for future research.     

基于FIRE模型的Agent联盟研究

FIRE模型改进了SPORAS模型，更适用于开放性的问题求解要求。联盟是多Agent之间一种重要的合作方法。文章将联盟机制引入FIRE模型，通过Agent之间形成联盟对问题求解，能够降低计算复杂度，提高问题求解效率。文章是对多Agent系统联盟的进一步研究，同时改进了Zacharia和Jennings等人关于多Agent系统的信任和名誉模型的研究工作。