Modeling Paying Behavior in Game Social Networks

Online gaming is one of the largest industries on the Internet, generating tens of billions of dollars in revenues annually. One core problem in online game is to find and convert free users into paying customers, which is of great importance for the sustainable development of almost all online games. Although much research has been conducted, there are still several challenges that remain largely unsolved: What are the fundamental factors that trigger the users to pay? How does users? paying behavior influence each other in the game social network? How to design a prediction model to recognize those potential users who are likely to pay? In this paper, employing two large online games as the basis, we study how a user becomes a new paying user in the games. In particular, we examine how users' paying behavior influences each other in the game social network. We study this problem from various sociological perspectives including strong/weak ties, social structural diversity and social influence. Based on the discovered patterns, we propose a learning framework to predict potential new payers. The framework can learn a model using features associated with users and then use the social relationships between users to refine the learned model. We test the proposed framework using nearly 50 billion user activities from two real games. Our experiments show that the proposed framework significantly improves the prediction accuracy by up to 3-11% compared to several alternative methods. The study also unveils several intriguing social phenomena from the data. For example, influence indeed exists among users for the paying behavior. The likelihood of a user becoming a new paying user is 5 times higher than chance when he has 5 paying neighbors of strong tie. We have deployed the proposed algorithm into the game, and the Lift_Ratio has been improved up to 196% compared to the prior strategy

The impact of brand communication on brand equity through Facebook

Purpose: The purpose of this study is to fill the gap in the discussion of the ways in which firm-created and user-generated social media brand communication impacts consumer-based brand equity metrics through Facebook. Design/methodology/approach: We evaluated 302 data sets that were generated through a standardized online-survey to investigate the impact of firm-created and user-generated social media brand communication on brand awareness/associations, perceived quality, and brand loyalty across 60 brands within three different industries: non-alcoholic beverages, clothing, and mobile network providers. We applied structural equation modeling techniques (SEM) to investigate the effects of social media brand communication on consumers’ perception of brand equity metrics, as well as in an examination of industry-specific differences. Findings: The results of our empirical studies showed that both firm-created and user-generated social media brand communication influence brand awareness/associations; whereas, user-generated social media brand communication had a positive impact on brand loyalty and perceived brand quality. Additionally, there are significant differences between the industries being investigated. Originality/value: This article is pioneering in that it exposes the effects of two different types of social media brand communication (i.e., firm-created and user-generated social media communication) on consumer-based brand equity metrics, a topic of relevance for both marketers and scholars in the era of social media. Additionally, it differentiates the effects of social media brand communication across industries, which indicate that practitioners should implement social media strategies according to industry specifics to lever consumer-based brand equity metrics

Network Model Selection for Task-Focused Attributed Network Inference

Networks are models representing relationships between entities. Often these relationships are explicitly given, or we must learn a representation which generalizes and predicts observed behavior in underlying individual data (e.g. attributes or labels). Whether given or inferred, choosing the best representation affects subsequent tasks and questions on the network. This work focuses on model selection to evaluate network representations from data, focusing on fundamental predictive tasks on networks. We present a modular methodology using general, interpretable network models, task neighborhood functions found across domains, and several criteria for robust model selection. We demonstrate our methodology on three online user activity datasets and show that network model selection for the appropriate network task vs. an alternate task increases performance by an order of magnitude in our experiments