The dynamics of viral marketing

By Jure Leskovec, Lada A. Adamic, Bernardo A. Huberman. 2005

2,493 citations recorded by the source corpus.

We present an analysis of a person-to-person recommendation network, consisting of 4 million people who made 16 million recommendations on half a million products. We observe the propagation of recommendations and the cascade sizes, which we explain by a simple stochastic model. We then establish how the recommendation network grows over time and how effective it is from the viewpoint of the sender and receiver of the recommendations. While on average recommendations are not very effective at inducing purchases and do not spread very far, we present a model that successfully identifies product and pricing categories for which viral marketing seems to be very effective.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

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Citations in the corpus

Deep Representation Learning for Social Network Analysis (2019, background): “, 2006), viral marketing (Leskovec et al., 2007; Chen et al., 2010), social healthcare (Tang and Yang, 2012), social influence analysis (Peng et al.”
Group formation in large social networks: membership, growth, and evolution (2006, background): “[25], in yet a nother different context — recommendation data for on-line p urchases — although the curves in their case become noisier at s maller values ofk.”
Feedback effects between similarity and social influence in online communities (2008, background): “We extend a methodology explored recently in several papers on social inﬂuence in online datasets [1, 11, 14]; these papers investigate how social ties aﬀect behavior by studying the extent to which various properties of a social network predict future activities or behaviors.”
Word of Mouth: What We Know and What We Have Yet to Learn (2013, neutral): “…channels which has led to renewed interest in commercially incentivized WOM, where the origin of a message is a commercial entity and where some consumers may receive an incentive for spreading a message (Leskovec, Adamic, and Huberman 2007; De Bruyn and Lilien 2008; Brown, Bhadury, and Pope 2010).”
Social Networks that matter: Twitter under the Microscope (2008, background): “Furthermore, commercial enterprises try to exploit them for marketing purposes, as they provide a ready made medium for propagating recommendations through people with similar interests [8].”
The structure of online diffusion networks (2012, background): “…large cascades infrequent in these online domains, but even when present they do not resemble the multi-step epidemics that arise in prevalent theoretical models of diffusion [Watts 2002; Kempe et al. 2003; Leskovec et al. 2007], in which cascades become large by spreading for many generations.”
Defining and evaluating network communities based on ground-truth (2012, background): “The second type of network we consider is the Amazon product co-purchasing network [16].”
Truthy: mapping the spread of astroturf in microblog streams (2010, background): “The study of opinion dynamics and information diffusion in social networks has a long history in the social, physical, and computational sciences [38, 17, 5, 13, 6, 31, 32].”
Searching for superspreaders of information in real-world social media (2014, background): “Information spreading is a ubiquitous process in society which describes a wide variety of phenomena ranging from the adoption of innovations [1], the success of commercial promotions [2, 3], the rise of political movements [4–7], and the spread of news, opinions and brand new products in society [8”