Curation-based network marketing: strategies for network growth and electronic word-of-mouth diffusion

In the last couple of years, a new aspect of online social networking has emerged, in which the strength of social network connections is based not on social ties but mutually shared interests. This dissertation studies these "curation-based" online social networks (CBN) and their suitability for the diffusion of electronic word-of-mouth information (eWOM). Within CBN, users do not rely on profiles full of personal information to identify network ``friends''. Rather, CBN users curate collections of digital content that becomes their digital self-expression within the network. This digital content can then be viewed, commented on, and shared across the pages of other CBN users. As the dissertation will show, this process of digital content curation, a relatively new online practice that centers around the collection and sharing of rich digital media, builds CBN, and presents exciting opportunities for the study of eWOM. The dissertation presents three studies around digital content curation, CBN, and eWOM diffusion. Study 1 examines individual level antecedents of digital content curation behavior. In this study, we use theory from sociology and behavioral psychology to develop a model of user intentions towards digital content curation behavior. We find that digital content curation is comprised of a mixture of social and utilitarian motivations, and that the management and organization of digital content is a major reason that people spend time on CBN. Study 2 examines the way that digital content curation behaviors grow CBN. We study a sample of 1800 CBN users to determine the way that their digital content curation behaviors attract and retain interested CBN followers. We find that the most successful CBN users are those that can generate an eWOM response around their content collections. Additionally, we find that textual eWOM plays a very limited role in attracting followers in the CBN environment. Finally, Study 3 examines eWOM diffusion by analyzing data on the structure and diffusion of digital content through real-world CBN network structures. This descriptive analysis of eWOM in CBN presents details on the way that CBN data is structured, and the methods and techniques that can be used to collect and analyze real-world eWOM collected from a CBN site. The study uses the UCINET network visualization software package to examine the networks of thirty companies operating CBN pages. Using a unique data set specifically compiled for this study, we are able to visualize the diffusion of curated digital content through the networks of these companies, and show how companies can identify their most influential followers as targets for further eWOM and traditional marketing efforts. Together, the three dissertation studies offer a holistic view of content curation behavior and curation-based online social networking and has the potential to fill the gap in the literature on information diffusion and online marketing. We make substantial contributions to the areas of sociology, economics, and marketing, and offer one of the first treatments of the role of digital content curation in online social networks

Finding Influential Users in Social Media Using Association Rule Learning

Influential users play an important role in online social networks since users tend to have an impact on one other. Therefore, the proposed work analyzes users and their behavior in order to identify influential users and predict user participation. Normally, the success of a social media site is dependent on the activity level of the participating users. For both online social networking sites and individual users, it is of interest to find out if a topic will be interesting or not. In this article, we propose association learning to detect relationships between users. In order to verify the findings, several experiments were executed based on social network analysis, in which the most influential users identified from association rule learning were compared to the results from Degree Centrality and Page Rank Centrality. The results clearly indicate that it is possible to identify the most influential users using association rule learning. In addition, the results also indicate a lower execution time compared to state-of-the-art methods

Linguistic Markers of Influence in Informal Interactions

There has been a long standing interest in understanding `Social Influence' both in Social Sciences and in Computational Linguistics. In this paper, we present a novel approach to study and measure interpersonal influence in daily interactions. Motivated by the basic principles of influence, we attempt to identify indicative linguistic features of the posts in an online knitting community. We present the scheme used to operationalize and label the posts with indicator features. Experiments with the identified features show an improvement in the classification accuracy of influence by 3.15%. Our results illustrate the important correlation between the characteristics of the language and its potential to influence others.Comment: 10 pages, Accepted in NLP+CSS workshop for ACL (Association for Computational Linguistics) 201

Searching for superspreaders of information in real-world social media

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.Comment: 12 pages, 7 figure

Human-Centric Cyber Social Computing Model for Hot-Event Detection and Propagation

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Microblogging networks have gained popularity in recent years as a platform enabling expressions of human emotions, through which users can conveniently produce contents on public events, breaking news, and/or products. Subsequently, microblogging networks generate massive amounts of data that carry opinions and mass sentiment on various topics. Herein, microblogging is regarded as a useful platform for detecting and propagating new hot events. It is also a useful channel for identifying high-quality posts, popular topics, key interests, and high-influence users. The existence of noisy data in the traditional social media data streams enforces to focus on human-centric computing. This paper proposes a human-centric social computing (HCSC) model for hot-event detection and propagation in microblogging networks. In the proposed HCSC model, all posts and users are preprocessed through hypertext induced topic search (HITS) for determining high-quality subsets of the users, topics, and posts. Then, a latent Dirichlet allocation (LDA)-based multiprototype user topic detection method is used for identifying users with high influence in the network. Furthermore, an influence maximization is used for final determination of influential users based on the user subsets. Finally, the users mined by influence maximization process are generated as the influential user sets for specific topics. Experimental results prove the superiority of our HCSC model against similar models of hot-event detection and information propagation