Computing word-of-mouth trust relationships in social networks from Semantic Web and Web 2.0 data sources

Social networks can serve as both a rich source of new information and as a filter to identify the information most relevant to our specific needs. In this paper we present a methodology and algorithms that, by exploiting existing Semantic Web and Web2.0 data sources, help individuals identify who in their social network knows what, and who is the most trustworthy source of information on that topic. Our approach improves upon previous work in a number of ways, such as incorporating topic-specific rather than global trust metrics. This is achieved by generating topic experience profiles for each network member, based on data from Revyu and del.icio.us, to indicate who knows what. Identification of the most trustworthy sources is enabled by a rich trust model of information and recommendation seeking in social networks. Reviews and ratings created on Revyu provide source data for algorithms that generate topic expertise and person to person affinity metrics. Combining these metrics, we are implementing a user-oriented application for searching and automated ranking of information sources within social networks

A Theory-Driven Design Framework for Social Recommender Systems

Social recommender systems utilize data regarding users’ social relationships in filtering relevant information to users. To date, results show that incorporating social relationship data – beyond consumption profile similarity – is beneficial only in a very limited set of cases. The main conjecture of this study is that the inconclusive results are, at least to some extent, due to an under-specification of the nature of the social relations. To date, there exist no clear guidelines for using behavioral theory to guide systems design. Our primary objective is to propose a methodology for theory-driven design. We enhance Walls et al.’s (1992) IS Design Theory by introducing the notion of “applied behavioral theory,” as a means of better linking theory and system design. Our second objective is to apply our theory-driven design methodology to social recommender systems, with the aim of improving prediction accuracy. A behavioral study found that some social relationships (e.g., competence, benevolence) are most likely to affect a recipient’s advice-taking decision. We designed, developed, and tested a recommender system based on these principles, and found that the same types of relationships yield the best recommendation accuracy. This striking correspondence highlights the importance of behavioral theory in guiding system design. We discuss implications for design science and for research on recommender systems

A SYSTEMATIC REVIEW OF COMPUTATIONAL METHODS IN AND RESEARCH TAXONOMY OF HOMOPHILY IN INFORMATION SYSTEMS

Homophily is both a principle for social group formation with like-minded people as well as a mechanism for social interactions. Recent years have seen a growing body of management research on homophily particularly on large-scale social media and digital platforms. However, the predominant traditional qualitative and quantitative methods employed face validity issues and/or are not well-suited for big social data. There are scant guidelines for applying computational methods to specific research domains concerning descriptive patterns, explanatory mechanisms, or predictive indicators of homophily. To fill this research gap, this paper offers a structured review of the emerging literature on computational social science approaches to homophily with a particular emphasis on their relevance, appropriateness, and importance to information systems research. We derive a research taxonomy for homophily and offer methodological reflections and recommendations to help inform future research

Recommendations based on social links

The goal of this chapter is to give an overview of recent works on the development of social link-based recommender systems and to offer insights on related issues, as well as future directions for research. Among several kinds of social recommendations, this chapter focuses on recommendations, which are based on users’ self-defined (i.e., explicit) social links and suggest items, rather than people of interest. The chapter starts by reviewing the needs for social link-based recommendations and studies that explain the viability of social networks as useful information sources. Following that, the core part of the chapter dissects and examines modern research on social link-based recommendations along several dimensions. It concludes with a discussion of several important issues and future directions for social link-based recommendation research

Information-seeking on the Web with Trusted Social Networks - from Theory to Systems

This research investigates how synergies between the Web and social networks can enhance the process of obtaining relevant and trustworthy information. A review of literature on personalised search, social search, recommender systems, social networks and trust propagation reveals limitations of existing technology in areas such as relevance, collaboration, task-adaptivity and trust. In response to these limitations I present a Web-based approach to information-seeking using social networks. This approach takes a source-centric perspective on the information-seeking process, aiming to identify trustworthy sources of relevant information from within the user's social network. An empirical study of source-selection decisions in information- and recommendation-seeking identified five factors that influence the choice of source, and its perceived trustworthiness. The priority given to each of these factors was found to vary according to the criticality and subjectivity of the task. A series of algorithms have been developed that operationalise three of these factors (expertise, experience, affinity) and generate from various data sources a number of trust metrics for use in social network-based information seeking. The most significant of these data sources is Revyu.com, a reviewing and rating Web site implemented as part of this research, that takes input from regular users and makes it available on the Semantic Web for easy re-use by the implemented algorithms. Output of the algorithms is used in Hoonoh.com, a Semantic Web-based system that has been developed to support users in identifying relevant and trustworthy information sources within their social networks. Evaluation of this system's ability to predict source selections showed more promising results for the experience factor than for expertise or affinity. This may be attributed to the greater demands these two factors place in terms of input data. Limitations of the work and opportunities for future research are discussed

Measuring Social Influence in Online Social Networks - Focus on Human Behavior Analytics

With the advent of online social networks (OSN) and their ever-expanding reach, researchers seek to determine a social media user’s social influence (SI) proficiency. Despite its exploding application across multiple domains, the research confronts unprecedented practical challenges due to a lack of systematic examination of human behavior characteristics that impart social influence. This work aims to give a methodical overview by conducting a targeted literature analysis to appraise the accuracy and usefulness of past publications. The finding suggests that first, it is necessary to incorporate behavior analytics into statistical measurement models. Second, there is a severe imbalance between the abundance of theoretical research and the scarcity of empirical work to underpin the collective psychological theories to macro-level predictions. Thirdly, it is crucial to incorporate human sentiments and emotions into any measure of SI, particularly as OSN has endowed everyone with the intrinsic ability to influence others. The paper also suggests the merits of three primary research horizons for future considerations

Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering

In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly available datasets, we evaluate approaches from four method families: (i) factorization-based, (ii) random walk-based, (iii) deep learning-based, and (iv) the Large-scale Information Network Embedding (LINE) approach. We find that across the four families, random-walk-based approaches consistently achieve the best accuracy. Besides, they result in highly novel and diverse recommendations. Furthermore, our results show that the use of graph embeddings in trust-based collaborative filtering significantly improves user coverage.Comment: 10 pages, Accepted as a full paper on the 25th International Symposium on Methodologies for Intelligent Systems (ISMIS'20

Towards Time-Aware Context-Aware Deep Trust Prediction in Online Social Networks

Trust can be defined as a measure to determine which source of information is reliable and with whom we should share or from whom we should accept information. There are several applications for trust in Online Social Networks (OSNs), including social spammer detection, fake news detection, retweet behaviour detection and recommender systems. Trust prediction is the process of predicting a new trust relation between two users who are not currently connected. In applications of trust, trust relations among users need to be predicted. This process faces many challenges, such as the sparsity of user-specified trust relations, the context-awareness of trust and changes in trust values over time. In this dissertation, we analyse the state-of-the-art in pair-wise trust prediction models in OSNs. We discuss three main challenges in this domain and present novel trust prediction approaches to address them. We first focus on proposing a low-rank representation of users that incorporates users' personality traits as additional information. Then, we propose a set of context-aware trust prediction models. Finally, by considering the time-dependency of trust relations, we propose a dynamic deep trust prediction approach. We design and implement five pair-wise trust prediction approaches and evaluate them with real-world datasets collected from OSNs. The experimental results demonstrate the effectiveness of our approaches compared to other state-of-the-art pair-wise trust prediction models.Comment: 158 pages, 20 figures, and 19 tables. This is my PhD thesis in Macquarie University, Sydney, Australi