Profiling user interactions on online social networks.

Over the last couple of years, there has been signi_cant research e_ort in mining user behavior on online social networks for applications ranging from sentiment analysis to marketing. In most of those applications, usually a snapshot of user attributes or user relationships are analyzed to build the data mining models, without considering how user attributes and user relationships can be utilized together. In this thesis, we will describe how user relationships within a social network can be further augmented by information gathered from user generated texts to analyze large scale dynamics of social networks. Speci_cally, we aim at explaining social network interactions by using information gleaned from friendships, pro_les, and status posts of users. Our approach pro_les user interactions in terms of shared similarities among users, and applies the gained knowledge to help users in understanding the inherent reasons, consequences and bene_ts of interacting with other social network users

Is Web Content a Good Proxy for Real-Life Interaction?

International audience—Today, many people spend a lot of time online. Their social interactions captured in online social networks are an important part of the overall personal social profile, in addition to interactions taking place offline. This paper investigates whether relations captured by online social networks can be used as a proxy for the relations in offline social networks, such as networks of human face-to-face (F2F) proximity and coauthorship networks. Particularly, the paper focuses on interactions of computer scientists in online settings (homepages, social networks profiles and connections) and offline settings (scientific collaboration, face-to-face communications during the conferences). We focus on quantitative studies and investigate the structural similarities and correlations of the induced networks; in addition, we analyze implications between networks. Finally, we provide a qualitative user analysis to find characteristics of good and bad proxies

Profiling user interactions on online social networks.

Over the last couple of years, there has been signi_cant research e_ort in mining user behavior on online social networks for applications ranging from sentiment analysis to marketing. In most of those applications, usually a snapshot of user attributes or user relationships are analyzed to build the data mining models, without considering how user attributes and user relationships can be utilized together. In this thesis, we will describe how user relationships within a social network can be further augmented by information gathered from user generated texts to analyze large scale dynamics of social networks. Speci_cally, we aim at explaining social network interactions by using information gleaned from friendships, pro_les, and status posts of users. Our approach pro_les user interactions in terms of shared similarities among users, and applies the gained knowledge to help users in understanding the inherent reasons, consequences and bene_ts of interacting with other social network users

IMPROVING COLLABORATIVE FILTERING RECOMMENDER BY USING MULTI-CRITERIA RATING AND IMPLICIT SOCIAL NETWORKS TO RECOMMEND RESEARCH PAPERS

Research paper recommender systems (RSs) aim to alleviate the information overload of researchers by suggesting relevant and useful papers. The collaborative filtering in the area of recommending research papers can benefit by using richer user feedback data through multi-criteria rating, and by integrating richer social network data into the recommender algorithm. Existing approaches using collaborative filtering or hybrid approaches typically allow only one rating criterion (overall liking) for users to evaluate papers. We conducted a qualitative study using focus group to explore the most important criteria for rating research papers that can be used to control the paper recommendation by enabling users to set the weight for each criterion. We investigated also the effect of using different rating criteria on the user interface design and how the user can control the weight of the criteria. We followed that by a quantitative study using a questionnaire to validate our findings from the focus group and to find if the chosen criteria are domain independent. Combining social network information with collaborative filtering recommendation algorithms has successfully reduced some of the drawbacks of collaborative filtering and increased the accuracy of recommendations. All existing recommendation approaches that combine social network information with collaborative filtering in this domain have used explicit social relations that are initiated by users (e.g. “friendship”, “following”). The results have shown that the recommendations produced using explicit social relations cannot compete with traditional collaborative filtering and suffer from the low user coverage. We argue that the available data in social bookmarking Web sites can be exploited to connect similar users using implicit social connections based on their bookmarking behavior. We explore the implicit social relations between users in social bookmarking Web sites (such as CiteULike and Mendeley), and propose three different implicit social networks to recommend relevant papers to users: readership, co-readership and tag-based implicit social networks. First, for each network, we tested the interest similarities of users who are connected using the proposed implicit social networks and compare them with the interest similarities using two explicit social networks: co-authorship and friendship. We found that the readership implicit social network connects users with more similarities than users who are connected using co-authorship and friendship explicit social networks. Then, we compare the recommendation using three different recommendation approaches and implicit social network alone with the recommendation using implicit and explicit social network. We found that fusing recommendation from implicit and explicit social networks can increase the prediction accuracy, and user coverage. The trade-off between the prediction accuracy and diversity was also studied with different social distances between users. The results showed that the diversity of the recommended list increases with the increase of social distance. To summarize, the main contributions of this dissertation to the area of research paper recommendation are two-fold. It is the first to explore the use of multi-criteria rating for research papers. Secondly, it proposes and evaluates a novel approach to improve collaborative filtering in both prediction accuracy (performance) and user coverage and diversity (nonperformance measures) in social bookmarking systems for sharing research papers, by defining and exploiting several implicit social networks from usage data that is widely available

Infer user interests via link structure regularization

Learning user interests from online social networks helps to better understand user behaviors and provides useful guidance to design user-centric applications. Apart from analyzing users' online content, it is also important to consider users' social connections in the social Web. Graph regularization methods have been widely used in various text mining tasks, which can leverage the graph structure information extracted from data. Previously, graph regularization methods operate under the cluster assumption that nearby nodes are more similar and nodes on the same structure (typically referred to as a cluster or a manifold) are likely to be similar. We argue that learning user interests from complex, sparse, and dynamic social networks should be based on the link structure assumption under which node similarities are evaluated based on the local link structures instead of explicit links between two nodes. We propose a regularization framework based on the relation bipartite graph, which can be constructed from any type of relations. Using Twitter as our case study, we evaluate our proposed framework from social networks built from retweet relations. Both quantitative and qualitative experiments show that our proposed method outperforms a few competitive baselines in learning user interests over a set of predefined topics. It also gives superior results compared to the baselines on retweet prediction and topical authority identification

Animating and sustaining niche social networks

Within the communicative space online Social Network Sites (SNS) afford, Niche Social Networks Sites (NSNS) have emerged around particular geographic, demographic or topic-based communities to provide what broader SNS do not: specified and targeted content for an engaged and interested community. Drawing on a research project developed at the Queensland University of Technology in conjunction with the Australian Smart Services Cooperative Research Centre that produced an NSNS based around Adventure Travel, this paper outlines the main drivers for community creation and sustainability within NSNS. The paper asks what factors motivate users to join and stay with these sites and what, if any, common patterns can be noted in their formation. It also outlines the main barriers to online participation and content creation in NSNS, and the similarities and differences in SNS and NSNS business models. Having built a community of 100 registered members, the staywild.com.au project was a living laboratory, enabling us to document the steps taken in producing a NSNS and cultivating and retaining active contributors. The paper incorporates observational analysis of user-generated content (UGC) and user profile submissions, statistical analysis of site usage, and findings from a survey of our membership pool in noting areas of success and of failure. In drawing on our project in this way we provide a template for future iterations of NSNS initiation and development across various other social settings: not only niche communities, but also the media and advertising with which they engage and interact. Positioned within the context of online user participation and UGC research, our paper concludes with a discussion of the ways in which the tools afforded by NSNS extend earlier understandings of online ‘communities of interest’. It also outlines the relevance of our research to larger questions about the diversity of the social media ecology

Community detection in civil society online networks: Theoretical guide and empirical assessment

Community detection is a fundamental challenge in the analysis of online networks. However, there is a lack of consensus regarding how to accomplish this task in a manner that acknowledges domain-specific, substantive social theory. We develop a typology of what social phenomena communities of hyperlinked actors may signify—topical similarities, ideological associations, strategic alliances, and potential user traffic—and offer recommendations for community detection grounded in these concepts. Testing procedures on a hyperlink network of the food safety movement, we demonstrate that the handling of tie directions and weights as well as algorithm choice influence which communities are ultimately detected in such a network

Content Recommendation by Analyzing User Behavior in Online Health Communities

Online health communities (OHCs) are the platforms for patients and their care-givers to search and share health-related information, and have attracted a vast amount of users in recent years. However, health consumers are easily overwhelmed by the overloaded information in OHCs, which makes it inefficient for users to find contents of their interest. This study proposes a framework for content recommendation by analyzing user activities in OHCs that utilizes social network analysis and text mining technology. We model users’ activities by constructing user behavior networks that capture implicit interactions of users, based on which closely related users are detected and user similarities are calculated. Text analysis are performed using topic model to select the threads for final content recommendation. Based on the data collected from a famous Chinese OHCs, we expect that our model could achieve promising results