Roberto Interdonato

Time-aware analysis and ranking of lurkers in social networks

Mining the silent members of an online community, also called lurkers, has been recognized as an important problem that accompanies the extensive use of online social networks (OSNs). Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in an OSN. However, they are limited to use only structural properties of the static network graph, thus ignoring any relevant information concerning the time dimension. Our goal in this work is to push forward research in lurker mining in a twofold manner: (1) to provide an in-depth analysis of temporal aspects that aims to unveil the behavior of lurkers and their relations with other users, and (2) to enhance existing methods for ranking lurkers by integrating different time-aware properties concerning information production and information consumption actions. Network analysis and ranking evaluation performed on Flickr, FriendFeed and Instagram networks allowed us to draw interesting remarks on both the understanding of lurking dynamics and on transient and cumulative scenarios of time-aware ranking.

Lurking in social networks: topology-based analysis and ranking methods

The massive presence of silent members in online communities, the so-called lurkers, has long attracted the attention of researchers in social science, cognitive psychology, and computer–human interaction. However, the study of lurking phenomena represents an unexplored opportunity of research in data mining, information retrieval and related fields. In this paper, we take a first step towards the formal specification and analysis of lurking in social networks. We address the new problem of lurker ranking and propose the first centrality methods specifically conceived for ranking lurkers in social networks. Our approach utilizes only the network topology without probing into text contents or user relationships related to media. Using Twitter, Flickr, FriendFeed and GooglePlus as cases in point, our methods’ performance was evaluated against data-driven rankings as well as existing centrality methods, including the classic PageRank and alpha-centrality. Empirical evidence has shown the significance of our lurker ranking approach, and its uniqueness in effectively identifying and ranking lurkers in an online social network.

A Versatile Graph-Based Approach to Package Recommendation

An emerging trend in research on recommender systems is the design of methods capable of recommending packages instead of single items. The problem is challenging due to a variety of critical aspects, including context-based and user-provided constraints for the items constituting a package, but also the high sparsity and limited accessibility of the primary data used to solve the problem. Most existing works on the topic have focused on a specific application domain (e.g., travel package recommendation), thus often providing ad-hoc solutions that cannot be adapted to other domains. By contrast, in this paper we propose a versatile package recommendation approach that is substantially independent of the peculiarities of a particular application domain. A key aspect in our framework is the exploitation of prior knowledge on the content type models of the packages being generated that express what the users expect from the recommendation task. Packages are learned for each package model, while the recommendation stage is accomplished by performing a PageRank-style method personalized w.r.t. the target users preferences, possibly including a limited budget. Our developed method has been tested on a TripAdvisor dataset and compared with a recently proposed method for learning composite recommendations.

To Trust or Not to Trust Lurkers?: Evaluation of Lurking and Trustworthiness in Ranking Problems

Research on social trust analysis has traditionally focused on the trustworthy/untrustworthy behaviors that are exhibited by active users. By contrast, due to their inherent reticence to regularly contribute to the online community life, the silent users in a social network, a.k.a. lurkers, have been taken out of consideration so far. Nevertheless, analysis and mining of lurkers in social networks has been recently recognized as an important problem. Determining trust/distrust relationships that involve lurkers can provide a unique opportunity to understand whether and to what extent such users can be trusted or distrusted from the other users. This is important from both the perspective of protecting the active users from untrustworthy or undesired interactions, and the perspective of encouraging lurkers to more actively participate in the community life through the guidance of active users. In this paper we aim at understanding and quantifying relations between lurkers and trustworthy/untrustworthy users in ranking problems. We evaluate lurker ranking methods against classic approaches to trust/distrust ranking, in scenarios of who-trusts-whom networks and followship networks. Results obtained on Advogato, Epinions, Flickr and FriendFeed networks indicate that lurkers should not be a-priori flagged as untrustworthy users, and that trustworthy users can indeed be found among lurkers.

Modeling Evolutionary Dynamics of Lurking in Social Networks

Lurking is a complex user-behavioral phenomenon that occurs in all large-scale online communities and social networks. It generally refers to the behavior characterizing users that benefit from the information produced by others in the community without actively contributing back to the production of social content. The amount and evolution of lurkers may strongly affect an online social environment, therefore understanding the lurking dynamics and identifying strategies to curb this trend are relevant problems. In this regard, we introduce the Lurking Game, i.e., a model for analyzing the transitions from a lurking to a non-lurking (i.e., active) user role, and vice versa, in terms of evolutionary game theory. We evaluate the proposed Lurking Game by arranging agents on complex networks and analyzing the system evolution, seeking relations between the network topology and the final equilibrium of the game. Results suggest that the Lurking Game is suitable to model the lurking dynamics, showing how the adoption of rewarding mechanisms combined with the modeling of hypothetical heterogeneity of users’ interests may lead users in an online community towards a cooperative behavior.

Ranking Vicarious Learners in Research Collaboration Networks

Despite being a topic of growing interest in social learning theory, vicarious learning has not been well-studied so far in digital library related tasks. In this paper, we address a novel ranking problem in research collaboration networks, which focuses on the role of vicarious learner. We introduce a topology-driven vicarious learning definition and propose the first centrality method for ranking vicarious learners. Results obtained on DBLP networks support the significance and uniqueness of the proposed approach.

Community-based delurking in social networks

The participation inequality phenomenon in online social networks between the niche of super contributors and the crowd of silent users, a.k.a. lurkers, has been witnessed in many domains. Within this view, understanding the role that lurkers take in the network is essential to develop innovative strategies to delurk them, i.e., to engage such users into a more active participation in the social network life. In this work, we leverage the boundary spanning theory to enhance our understanding of lurking behaviors, with the goal of improving the task of delurking in social networks. Assuming the availability of a global community structure, we first analyze how lurkers are related to users that take the role of bridges between different communities, unveiling insights into the bridging nature of lurkers and their tendency to acquire information from outside their own community. Moreover, based on a targeted influence maximization method designed for delurking, we also analyze how the learning of users that can best engage lurkers is related to the community structure. We found that the best users to engage lurkers belonging to any particular community, are more often found outside that community, and more specifically they are located in the adjacent communities.

Understanding lurking behaviors in social networks across time

Mining the silent members, also called lurkers, of an online community has been recognized as an important problem that accompanies the extensive use of social networks. Existing solutions to the ranking of lurkers can aid understanding the lurking behaviors in social networks, however they ignore any information concerning the time dimension. In this work we push forward research in lurker mining by providing an analysis of temporal aspects that aims to unveil the behavior of lurkers and their interrelations with other users. Our analysis builds upon four research questions, which encompass relations between lurkers and inactive users, relations between lurkers and active users, the responsiveness behavior of lurkers, and the evolution of lurking trends across time. Evaluation has been conducted on Flickr, FriendFeed and Instagram networks.

Controversy in Trust Networks

Given the increasing volume and impact of online social interactions in various aspects of life, inferring how a user should be trusted becomes a matter of crucial importance, which can strongly bias any decision process. Existing trust inference algorithms are based on the propagation and aggregation of trust values. However, trust opinions are subjective and can be very different from one user to another. Consequently, inferred trust values can lose significance or even be unavailable if there is a strong disagreement among the original values. In this work, we discuss the trust controversy problem. We analyze to what extent existing trust inference algorithms are robust with respect to controversial situations, and propose a novel trust controversy measure to support trust inference in controversial cases. Experimental results on real world datasets demonstrate that controversial cases should be explicitly taken into account and that the controversy level of inferred trust values is highly related to the prediction error. Our trust controversy measure can serve as an integrated and unsupervised estimator for trust inference accuracy.

Local community detection in multilayer networks

The problem of local community detection refers to the identification of a community starting from a query node and using limited information about the network structure. Existing methods for solving this problem however are not designed to deal with multilayer network models, which are becoming pervasive in many fields of science. In this work, we present the first method for local community detection in multilayer networks. Our method exploits both internal and external connectivity of the nodes in the community being constructed for a given seed, while accounting for different layer-specific topological information. Evaluation of the proposed method has been conducted on real-world multilayer networks.