The Dynamics of Viral Marketing

2013

We present an analysis of how the structure of a social network in- fluences the diffusion of information in a viral marketing context. We per- formed diffusion simulations on a large number of real world and artificially generated network datasets. We analyze how the characteristics of a network and parameter settings like the selection of initial start nodes influences the dif- fusion. The results indicate that the network structure has a significant effect on the diffusion. Extreme cases show a difference in the diffusion of over 65%. Our investigation also proves that a viral marketing diffusion may be predicted without the knowledge of the whole network. We further provide useful rec- ommendations for marketers which could be taken into consideration when marketing campaigns are conducted.

This PhD Thesis presents in detail the methodology and results of research on viral diffusion of marketing information through online social networks carried out by the author on experimental data provided by IBM Corporation. Aside from constituting supplementary information on the methods and materials used to obtain the results published in three peer-reviewed articles on such research, the document includes the following previously unpublished content: - The foundation and rational of the Message Affinity Model, an information diffusion in online social networks model proposed by the author to explain the peculiarities observed in the online information diffusion mechanism. - A review of online diffusion processes leading to information epidemics such as Word of Mouth (WOM), Viral Marketing or Influentials driven contagion together with an in depth analysis of the various models and theories available in the scientific literature to explain them. - A discussion on the applicability of the research findings to the management and improvement of online diffusion processes in terms of speeding the messages spread, increasing the size of the diffusion cascades or improving the reachability of the desired targets. Recommendations of the metrics and parameters to monitor for better results are also offered.

Electronic Commerce Research and Applications, 2014

Viral marketing can be an effective marketing technique in social networks. Initiating from a set of influential seed users, it can activate a ''chain-reaction'' driven by word-of-mouth. The effectiveness of viral marketing lies in the fact that it conveys an implied endorsement from social ties. Existing approaches to selecting influential seeds depend on measures of global centrality within the structure of the social network-they select users that are central in the entire network according to some centrality measure (e.g., Eigenvector centrality). In this paper a new targeted approach to viral marketing is proposed that exploits prior knowledge about the potential market and uses local centrality scores to identify seeds that have high chances of reaching and activating many users in the potential market. The performance gained by the proposed approach is investigated with an experimental evaluation that uses data from real social networks. The results show that targeted approach outperforms existing, global centrality based methods. It is also shown that the relative performance of the targeted approach improves in the case where the majority of users are indifferent (or negative) to the viral marketing campaign.

Applied Network Science, 2018

Modeling influence diffusion in social networks is an important challenge. We investigate influence-diffusion modeling and maximization in the setting of viral marketing, in which a node's influence is measured by the number of nodes it can activate to adopt a new technology or purchase a new product. One of the fundamental problems in viral marketing is to find a small set of initial adopters who can trigger the most further adoptions through word-of-mouth-based influence propagation in the network. We propose a novel multiple-path asynchronous threshold (MAT) model, in which we quantify influence and track its diffusion and aggregation. Our MAT model captures not only direct influence from neighboring influencers but also indirect influence passed along by messengers. Moreover, our MAT framework models influence attenuation along diffusion paths, temporal influence decay, and individual diffusion dynamics. Our work is an important step toward a more realistic diffusion model. Further, we develop an effective and efficient heuristic to tackle the influence-maximization problem. Our experiments on four real-life networks demonstrate its excellent performance in terms of both influence spread and time efficiency. Our work provides preliminary but significant insights and implications for diffusion research and marketing practice.

2010

One method of viral marketing involves seeding certain consumers within a population to encourage faster adoption of the product throughout the entire population. However, determining how many and which consumers within a particular social network should be seeded to maximize adoption is challenging. We define a strategy space for consumer seeding by weighting a combination of network characteristics such as average path length, clustering coefficient, and degree. We measure strategy effectiveness by simulating adoption on a Bass-like agent-based model, with five different social network structures: four classic theoretical models (random, lattice, small-world, and preferential attachment) and one empirical (extracted from Twitter friendship data). To discover good seeding strategies, we have developed a new tool, called BehaviorSearch, which uses genetic algorithms to search through the parameter-space of agent-based models. This evolutionary search also provides insight into the interaction between strategies and network structure. Our results show that one simple strategy (ranking by node degree) is near-optimal for the four theoretical networks, but that a more nuanced strategy performs significantly better on the empirical Twitter-based network. We also find a correlation between the optimal seeding budget for a network, and the inequality of the degree distribution.

Proceedings of the ACM Conference on Electronic Commerce, 2012

Models of networked diffusion that are motivated by analogy with the spread of infectious disease have been applied to a wide range of social and economic adoption processes, including those related to new products, ideas, norms and behaviors. However, it is unknown how accurately these models account for the empirical structure of diffusion over networks. Here we describe the diffusion patterns arising from seven online domains, ranging from communications platforms to networked games to microblogging services, each involving distinct types of content and modes of sharing. We find strikingly similar patterns across all domains. In particular, the vast majority of cascades are small, and are described by a handful of simple tree structures that terminate within one degree of an initial adopting "seed." In addition we find that structures other than these account for only a tiny fraction of total adoptions; that is, adoptions resulting from chains of referrals are extremely rare. Finally, even for the largest cascades that we observe, we find that the bulk of adoptions often takes place within one degree of a few dominant individuals. Together, these observations suggest new directions for modeling of online adoption processes.

2015

Viral marketing can become an effective marketing technique in social networks. Initiating from a set of influential seed users, it can activate a “chain-reaction” driven by word-of-mouth. The effectiveness of viral marketing lies in the fact that it conveys an implied endorsement from social ties. However, not all viral marketing campaign become successful some stop even before the number of activated users of the network reaches critical mass. In this paper we propose a new approach to viral marketing that will allow marketers to increase the performance of the stopped campaign by initiating new “waves” of the campaign. But in order to not overwhelm users that were already exposed to the initial campaign, the activation of seeds is performed in a non-intrusive way by suggesting users to follow recommendations of their friends. The proposed method for seed selection for the next “wave” is based on percolation centrality that takes into account already activated nodes and uses their...

Cornell University Library arXiv.org, 2007

The dynamics of information dissemination in social networks is of paramount importance in processes such as rumors or fads propagation, spread of product innovations or 'word of mouth' communications. Due to the difficulty in tracking a specific information when it is transmitted by people, most understanding of information spreading in social networks comes from models or indirect measurements. Here we present an integrated experimental and theoretical framework to understand and quantitatively predict how and when information spreads over social networks. Using datacollected in Viral Marketing campaigns thatreached over 31,000 individuals in eleven European markets, we show the large degree of variability of the participants’ actions, despite them being confronted with the common task of receiving and forwarding the same piece of information. Specifically we observe large heterogeneity in both the number of recommendations made by individuals and of the time they take to transmit the information. Both have a profound effect on information diffusion: Firstly, most of the transmission takes place due to super spreading events which would be considered extraordinary in population average models. Secondly, due to the different way individuals schedule information transmission we observe a slowing down of the spreading of information in social networks that happens in logarithmic time. Quantitative description of the experiments is possible through an stochastic branching process which corroborates the importance of heterogeneity. The fact that both the intensity and frequency of human responses show also large degrees of heterogeneity in many other activities suggests that our findings are pertinent to many other human driven diffusion processes like rumors, fads, innovations or news which has important consequences for organizations management, communications, marketing or electronic social communities.

2006

Abstract. Information cascades are phenomena in which individuals adopt a new action or idea due to influence by others. As such a process spreads through an underlying social network, it can result in widespread adoption overall. We consider information cascades in the context of recommendations, and in particular study the patterns of cascading recommendations that arise in large social networks.