Negative Effects of Incentivised Viral Campaigns for Activity in Social Networks

Viral campaigns are crucial methods for word-of-mouth marketing in social communities. The goal of these campaigns is to encourage people for activity. The problem of incentivised and non-incentivised campaigns is studied in the paper. Based on the data collected within the real social networking site both approaches were compared. The experimental results revealed that a highly motivated campaign not necessarily provides better results due to overlapping effect. Additional studies have shown that the behaviour of individual community members in the campaign based on their service profile can be predicted but the classification accuracy may be limited.Comment: In proceedings of the 2nd International Conference on Social Computing and its Applications, SCA 201

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

Pioneers of Influence Propagation in Social Networks

With the growing importance of corporate viral marketing campaigns on online social networks, the interest in studies of influence propagation through networks is higher than ever. In a viral marketing campaign, a firm initially targets a small set of pioneers and hopes that they would influence a sizeable fraction of the population by diffusion of influence through the network. In general, any marketing campaign might fail to go viral in the first try. As such, it would be useful to have some guide to evaluate the effectiveness of the campaign and judge whether it is worthy of further resources, and in case the campaign has potential, how to hit upon a good pioneer who can make the campaign go viral. In this paper, we present a diffusion model developed by enriching the generalized random graph (a.k.a. configuration model) to provide insight into these questions. We offer the intuition behind the results on this model, rigorously proved in Blaszczyszyn & Gaurav(2013), and illustrate them here by taking examples of random networks having prototypical degree distributions - Poisson degree distribution, which is commonly used as a kind of benchmark, and Power Law degree distribution, which is normally used to approximate the real-world networks. On these networks, the members are assumed to have varying attitudes towards propagating the information. We analyze three cases, in particular - (1) Bernoulli transmissions, when a member influences each of its friend with probability p; (2) Node percolation, when a member influences all its friends with probability p and none with probability 1-p; (3) Coupon-collector transmissions, when a member randomly selects one of his friends K times with replacement. We assume that the configuration model is the closest approximation of a large online social network, when the information available about the network is very limited. The key insight offered by this study from a firm's perspective is regarding how to evaluate the effectiveness of a marketing campaign and do cost-benefit analysis by collecting relevant statistical data from the pioneers it selects. The campaign evaluation criterion is informed by the observation that if the parameters of the underlying network and the campaign effectiveness are such that the campaign can indeed reach a significant fraction of the population, then the set of good pioneers also forms a significant fraction of the population. Therefore, in such a case, the firms can even adopt the naive strategy of repeatedly picking and targeting some number of pioneers at random from the population. With this strategy, the probability of them picking a good pioneer will increase geometrically fast with the number of tries

Containment of RumorsunderLimitCost Budget in Social Network

With widely usingof computer and mobile devices available, people share information more frequently on the online social network (OSN) than before, so information spread faster and wider, especially misinformation andrumors. Rumors on the OSN often make E-commerce companiessuffermuch financial losses.Once there is a rumor, the companies always try to control the rumor propagating so that they suffer the least loss by using a certain budget. In this paper,an effective method on information blocking maximization with cost budget (CIBM) is proposed to solve rumor containment problem with cost budget in e-commerce environment. First, CIBM is proved as NP-hardproblem withthe characteristic of sub-modular and monotone.Then a community dividing algorithm based community structure is presented to optimize containment of the rumors. To verify our proposed method, a lot of experiments are conducted on real dataset and random generated datasets. And the experiment results show that our algorithm has advantage over traditional methods

PREDICTING VIRAL MARKETING PROPAGATING EFFICIENCY WITHIN GIVEN DEADLINE

As a new developed marketing strategy in recent years, viral marketing attracts great attention from scholars and enterprises. Many enterprises try to adopt it for marking new product in order to greatly improve large sales and to quickly recoup the cost. But how marketing efficiency is actually? How fast marketing propagating speed is on earth? Especially for a given deadline, can the enterprise predict the sales when viral marketing is used? In this paper, a predicting method based on deadline graph is proposed to evaluate the viral marketing efficiency within given deadline. Specifically, two methods are first proposed to generate deadline graph, respectively Shortest-Distance methods and Time-Iteration method, based on which, a Reverse Tree method is exploited to predict the activated (buying) probability of the users. A lot of experiments are made to test our proposed method by using three datasets, respectively Twitter, Friendster and Random. The experiment results clearly show that deadline graph is a very key and necessary technique for evaluating viral marketing propagating efficiency within given deadline since overwhelming advantages over traditional method are gained by the method based on deadline graph in our experiment

Research on Viral Marketing Propagating Oriented to Marketing Context

Viral marketing exploits social networks to market new product, where users are encouraged to recommend products to their friends. Propagating model is base of other researches on viral marketing, which describes how marketing information is propagated from seed users to other users. In this paper, it is found that currently widely used Independent Cascade (IC) model is not adapted to marketing context where a user will accept recommendation only when the recommendation come from a lot of his friends. Based on the finding, k-order propagating model oriented to marketing context is proposed. Two specific k-order propagating are studied, respectively General_KP and Binary_KP. Using Twitter, Friendster and Random dataset, there 384 experiments are made to show propagating results based on proposed models. The results shows that influence order k is has important influence on propagating process, which illustrate that k-order propagating model is important for viral marketing

How to Influence People with Partial Incentives

We study the power of fractional allocations of resources to maximize influence in a network. This work extends in a natural way the well-studied model by Kempe, Kleinberg, and Tardos (2003), where a designer selects a (small) seed set of nodes in a social network to influence directly, this influence cascades when other nodes reach certain thresholds of neighbor influence, and the goal is to maximize the final number of influenced nodes. Despite extensive study from both practical and theoretical viewpoints, this model limits the designer to a binary choice for each node, with no way to apply intermediate levels of influence. This model captures some settings precisely, e.g. exposure to an idea or pathogen, but it fails to capture very relevant concerns in others, for example, a manufacturer promoting a new product by distributing five "20% off" coupons instead of giving away one free product. While fractional versions of problems tend to be easier to solve than integral versions, for influence maximization, we show that the two versions have essentially the same computational complexity. On the other hand, the two versions can have vastly different solutions: the added flexibility of fractional allocation can lead to significantly improved influence. Our main theoretical contribution is to show how to adapt the major positive results from the integral case to the fractional case. Specifically, Mossel and Roch (2006) used the submodularity of influence to obtain their integral results; we introduce a new notion of continuous submodularity, and use this to obtain matching fractional results. We conclude that we can achieve the same greedy $(1-1/e-\epsilon)$-approximation for the fractional case as the integral case. In practice, we find that the fractional model performs substantially better than the integral model, according to simulations on real-world social network data