The Multidimensional Study of Viral Campaigns as Branching Processes

Viral campaigns on the Internet may follow variety of models, depending on the content, incentives, personal attitudes of sender and recipient to the content and other factors. Due to the fact that the knowledge of the campaign specifics is essential for the campaign managers, researchers are constantly evaluating models and real-world data. The goal of this article is to present the new knowledge obtained from studying two viral campaigns that took place in a virtual world which followed the branching process. The results show that it is possible to reduce the time needed to estimate the model parameters of the campaign and, moreover, some important aspects of time-generations relationship are presented.Comment: In proceedings of the 4th International Conference on Social Informatics, SocInfo 201

A Viral Branching Model for Predicting the Spread of Electronic Word-of-Mouth

In a viral marketing campaign an organization develops a marketing message, and stimulates customers to forward this message to their contacts. Despite its increasing popularity, there are no models yet that help marketers to predict how many customers a viral marketing campaign will reach, and how marketers can influence this process through marketing activities. This paper develops such a model using the theory of branching processes. The proposed Viral Branching Model allows customers to participate in a viral marketing campaign by 1) opening a seeding email from the organization, 2) opening a viral email from a friend, and 3) responding to other marketing activities such as banners and offline advertising. The model parameters are estimated using individual-level data that become available in large quantities already in the early stages of viral marketing campaigns. The Viral Branching Model is app

Spreading processes in Multilayer Networks

Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of an online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.Comment: 21 pages, 3 figures, 4 table

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

The cause, development and outcome of word-of-mouth marketing: with particular reference to WOM volume, valence and the modeling of viral marketing

Viral marketing is a form of online word-of-mouth (WOM) communication in which individuals are encouraged to pass on promotional messages through social websites. With the growing popularity of online social websites, viral marketing has increasingly garnered attention of marketers and marketing researchers alike. The two most important WOM attributes highlighted in the extant literature are volume and valence. This thesis looked into the cause, development and outcome of WOM marketing and provided computational models for forecasting the development of WOM volume and valence of viral marketing in social websites. With the data extracted from large-scale web-crawling activities, through a series of computer simulation experiments comparable to social websites, the author developed models to predict WOM volume and valence in viral marketing. The model for predicting WOM volume in viral marketing used theories of network topologies. The model for predicting WOM valence in viral marketing used an artificial neural network model. The author discussed the insights from the findings and suggested viral marketing strategies to optimize the performance of WOM volume and valence in social websites. A key contribution of this thesis is the new approaches of modeling and data collection for WOM volume and valance forecasting in viral marketing

A picture is worth a thousand words: an empirical study on the influence of content visibility on diffusion processes within a virtual world

Studying information diffusion and the spread of goods in the real world and in many digital services can be extremely difficult since information about the information flows is challenging to accurately track. How information spreads has commonly been analysed from the perspective of homophily, social influence, and initial seed selection. However, in virtual worlds and virtual economies, the movements of information and goods can be precisely tracked. Therefore, these environments create laboratories for the accurate study of information diffusion characteristics that have been difficult to study in prior research. In this paper, we study how content visibility as well as sender and receiver characteristics, the relationship between them, and the types of multilayer social network layers affect content absorption and diffusion in virtual world. The results show that prior visibility of distributed content is the strongest predictor of content adoption and its further spread across networks. Among other analysed factors, the mechanics of diffusion, content quality, and content adoption by users’ neighbours on the social activity layer had very strong influences on the adoption of new content.</p