Mining social network data for personalisation and privacy concerns: A case study of Facebook’s Beacon

This is the post-print version of the final published paper that is available from the link below.The popular success of online social networking sites (SNS) such as Facebook is a hugely tempting resource of data mining for businesses engaged in personalised marketing. The use of personal information, willingly shared between online friends' networks intuitively appears to be a natural extension of current advertising strategies such as word-of-mouth and viral marketing. However, the use of SNS data for personalised marketing has provoked outrage amongst SNS users and radically highlighted the issue of privacy concern. This paper inverts the traditional approach to personalisation by conceptualising the limits of data mining in social networks using privacy concern as the guide. A qualitative investigation of 95 blogs containing 568 comments was collected during the failed launch of Beacon, a third party marketing initiative by Facebook. Thematic analysis resulted in the development of taxonomy of privacy concerns which offers a concrete means for online businesses to better understand SNS business landscape - especially with regard to the limits of the use and acceptance of personalised marketing in social networks

Mitigating Overexposure in Viral Marketing

In traditional models for word-of-mouth recommendations and viral marketing, the objective function has generally been based on reaching as many people as possible. However, a number of studies have shown that the indiscriminate spread of a product by word-of-mouth can result in overexposure, reaching people who evaluate it negatively. This can lead to an effect in which the over-promotion of a product can produce negative reputational effects, by reaching a part of the audience that is not receptive to it. How should one make use of social influence when there is a risk of overexposure? In this paper, we develop and analyze a theoretical model for this process; we show how it captures a number of the qualitative phenomena associated with overexposure, and for the main formulation of our model, we provide a polynomial-time algorithm to find the optimal marketing strategy. We also present simulations of the model on real network topologies, quantifying the extent to which our optimal strategies outperform natural baselinesComment: In AAAI-1

Virtual Geodemographics: Repositioning Area Classification for Online and Offline Spaces

Computer mediated communication and the Internet has fundamentally changed how consumers and producers connect and interact across both real space, and has also opened up new opportunities in virtual spaces. This paper describes how technologies capable of locating and sorting networked communities of geographically disparate individuals within virtual communities present a sea change in the conception, representation and analysis of socioeconomic distributions through geodemographic analysis. We argue that through virtual communities, social networks between individuals may subsume the role of neighbourhood areas as the most appropriate units of analysis, and as such, geodemographics needs to be repositioned in order to accommodate social similarities in virtual, as well as geographical, space. We end the paper by proposing a new model for geodemographics which spans both real and virtual geographies

The Dynamics of Viral Marketing

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 analyze how user behavior varies within user communities defined by a recommendation network. Product purchases follow a 'long tail' where a significant share of purchases belongs to rarely sold items. We 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 communities, product and pricing categories for which viral marketing seems to be very effective

Maximum Coverage and Maximum Connected Covering in Social Networks with Partial Topology Information

Viral marketing campaigns seek to recruit the most influential individuals to cover the largest target audience. This can be modeled as the well-studied maximum coverage problem. There is a related problem when the recruited nodes are connected. It is called the maximum connected cover problem. This problem ensures a strong coordination between the influential nodes which are the backbone of the marketing campaign. In this work, we are interested on both of these problems. Most of the related literature assumes knowledge about the topology of the network. Even in that case, the problem is known to be NP-hard. In this work, we propose heuristics to the maximum connected cover problem and the maximum coverage problem with different knowledge levels about the topology of the network. We quantify the difference between these heuristics and the local and global greedy algorithms