Twitter, a popular microblogging service, has received much attention recently. An important characteristic of Twitter is its real-time nature. For example, when an earthquake occurs, people make many Twitter posts (tweets) related to the earthquake, which enables detection of earthquake occurrence promptly, simply by observing the tweets. As described in this paper, we investigate the real-time interaction of events such as earthquakes, in Twitter, and propose an algorithm to monitor tweets and to detect a target event. To detect a target event, we devise a classifier of tweets based on features such as the keywords in a tweet, the number of words, and their context. Subsequently, we produce a probabilistic spatiotemporal model for the target event that can find the center and the trajectory of the event location. We consider each Twitter user as a sensor and apply Kalman filtering and particle filtering, which are widely used for location estimation in ubiquitous/pervasive computing. The particle filter works better than other compared methods in estimating the centers of earthquakes and the trajectories of typhoons. As an application, we construct an earthquake reporting system in Japan. Because of the numerous earthquakes and the large number of Twitter users throughout the country, we can detect an earthquake by monitoring tweets with high probability (96 % of earthquakes of Japan Meteorological Agency (JMA) seismic intensity scale 3 or more are detected). Our system detects earthquakes promptly and sends e-mails to registered users. Notification is delivered much faster than the announcements that are broadcast by the JMA. 1.

Abstract. Web-based environments typically span interactions between humans and software services. The management and automatic calculation of trust are among the key challenges of the future service-oriented Web. Trust management systems in large-scale systems, for example, social networks or service-oriented environments determine trust between actors by either collecting manual feedback ratings or by mining their interactions. However, most systems do not support bootstrapping of trust. In this paper we propose techniques and algorithms enabling the prediction of trust even when only few or no ratings have been collected or interactions captured. We introduce the concepts of mirroring and teleportation of trust facilitating the evolution of cooperation between various actors. We assume a user-centric environment, where actors express their opinions, interests and expertises by selecting and tagging resources. We take this information to construct tagging profiles, whose similarities are utilized to predict potential trust relations. Most existing similarity approaches split the three-dimensional relations between users, resources, and tags, to create and compare general tagging profiles directly. Instead, our algorithms consider (i) the understandings and interests of actors in tailored subsets of resources and (ii) the similarity of resources from a certain actor-group’s point of view. 1

Abstract—Mixed service-oriented systems composed of human actors and software services build up complex interaction networks. Without any coordination, such system may exhibit undesirable properties due to unexpected behavior. Also, communications and interactions in such networks are not preplanned by top-down composition models. Consequently, the management of service-oriented applications is difficult due to changing interaction and behavior patterns that possibly contradict and result in faults from varying conditions and misbehavior in the network. In this paper we present a self-adaptation approach that regulates local interactions to maintain desired system functionality. To prevent degraded or stalled systems, adaptations operate by link modification or substitution of actors based on similarity and trust metrics. Unlike a security perspective on trust, we focus on the notion of socially inspired trust. We design an architecture based on two separate independent frameworks. One providing a real Web service testbed extensible for dynamic adaptation actions. The other is our self-adaptation framework including all modules required by systems with self- * properties. In our experiments we study a trust and similarity based adaptation approach by simulating dynamic interactions in the real Web services testbed. Index Terms—Service-oriented collaboration, monitoring, selfadaptation, web service testbed, dynamic trust

Abstract—The global scale and distribution of companies have changed the economy and dynamics of businesses. Web-based collaborations and cross-organizational processes typically require dynamic and context-based interactions between people and services. However, finding the right partner to work on joint tasks or to solve emerging problems in such scenarios is challenging due to scale (number of involved people and services) and the temporary nature of collaborations. Furthermore, actor skills and competencies evolve over time requiring dynamic approaches for the management of actor properties. Web services and SOA are the ideal technical framework to automate interactions spanning people and services. In this paper, we present a novel discovery mechanism based on social trust to support formation and dynamic interactions in serviceoriented collaboration networks. We argue that trust between members is essential for successful collaborations. Here we discuss profile similarity-based link establishment to connect disparate network segments. Keywords-interaction monitoring, trust inference, group formation, privacy issues, service-centric collaborations I.

Trust reciprocity, a special form of link reciprocity, exists in many networks of trust among users. In this paper, we seek to determine the extent to which reciprocity exists in a trust network and develop quantitative models for measuring reciprocity and reciprocity related behaviors. We identify several reciprocity behaviors and their respective measures. These behavior measures can be employed for predicting if a trustee will return trust to her trustor given that the latter initiates a trust link earlier. We develop for this reciprocal trust prediction task a number of ranking method and classification methods, and evaluated them on an Epinions trust network data. Our results show that reciprocity related behaviors provide good features for both ranking and classification based methods under different parameter settings. 1

Traditionally, research about trust assumes a single type of trust between users. However, trust, as a social concept, inherently has many facets indicating multiple and heterogeneous trust relationships between users. Due to the presence of a large trust network for an online user, it is necessary to discern multi-faceted trust as there are naturally experts of different types. Our study in product review sites reveals that people place trust differently to different people. Since the widely used adjacency matrix cannot capture multi-faceted trust relationships between users, we propose a novel approach by incorporating these relationships into traditional rating prediction algorithms to reliably estimate their strengths. Our work results in interesting findings such as heterogeneous pairs of reciprocal links. Experimental results on real-world data from Epinions and Ciao show that our work of discerning multi-faceted trust can be applied to improve the performance of tasks such as rating prediction, facet-sensitive ranking, and status theory.

Cross-enterprise collaboration has emerged as a key survival factor in today’s global markets. Semantic Web technologies are the basis to establish enterprise interoperability including data mediation support and automatic composition of services. Capabilities of services are semantically described andreasoningtechniquessupportthediscoveryandselection of services at run-time. These technologies are commonly based on precisely defined enterprise ontologies. In contrast to Semantic Web technologies that cover interactions between (technical) services, human collaborations emerge based on social preferences. Social networks have become a mass phenomenon. The fundamental aspects of these networks are to manage personal contacts and to share profile information with friends. These principles are increasingly harnessed in businesses and professional environments. In a manner similar to service-oriented systems, they enable flexible discovery and dynamic collaborations between participants. In this paper, we discuss the concept of social overlays for Web service based collaboration infrastructures. This mechanism enables information flows between actors in order to allow for flexible group formations in highly dynamic large-scale networks.

Trust between users is an important piece of knowledge that can be exploited in search and recommendation. Given that user-supplied trust relationships are usually very sparse, we study the prediction of trust relationships using user interaction features in an online user generated review application context. We show that trust relationship prediction can achieve better accuracy when one adopts personalized and cluster-based classification methods. The former trains one classifier for each user using user-specific training data. The cluster-based method first constructs user clusters before training one classifier for each user cluster. Our proposed methods have been evaluated in a series of experiments using two datasets from Epinions.com. It is shown that the personalized and cluster-based classification methods outperform the global classification method, particularly for the active users.

Abstract—Flexible interactions in complex social and serviceoriented collaboration systems increasingly demand for automated adaptation techniques to optimize partner discovery and selection. Today, applications of complex service-oriented systems can be found in crowdsourcing environments. In such environments, collaborations are typically short-lived and strongly influenced by incentives and actor behavior. As actors prove their reliable and dependable behavior in jointly performed activities, they become increasingly considered as invaluable partners. A social network builds a strong basis to enable successful collaborations between crowd members. In order to keep track of the dynamics in such systems, it is inevitable to apply an autonomous approach to manage social network structures automatically using captured interaction data. Thus, we introduce an adaptation concept that accounts for emerging social relations based on varying interaction behavior of collaboration partners. We describe the foundational concepts for dynamic social link management in Web-based collaborations. We highlight major concerns of computational models in highly dynamic networks and deal with temporal aspects such as supporting the emergence of relations, efficient update mechanisms, and aging of relations. Keywords-computational social network management; emergence of social relations; service-oriented crowdsourcing I.

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