Query: viral marketing

title: Evolutionary multiobjective optimization to target social network influentials in viral marketing
abstract: Marketers have an important asset if they effectively target social networks' influentials. They can advertise products or services with free items or discounts to spread positive opinions to other consumers (i.e. word of mouth) However, main research on choosing the best influentials to target is single objective and mainly focused on maximizing sales revenue. In this paper we propose a multiobjective approach to the influence maximization problem with the aim of increasing the revenue of viral marketing campaigns while reducing the costs. By using local social network metrics to locate influentials, we apply two evolutionary multiobjective optimization algorithms, NSGA II and MOEA/D, a multiobjective adaptation of a single objective genetic algorithm, and a greedy algorithm. Our proposal uses a realistic agent based market framework to evaluate the fitness of the chromosomes by simulating the viral campaigns. The framework also generates, in a single run, a set of non dominated solutions that allows marketers to consider multiple targeting options The algorithms are evaluated on five network topologies and a real data generated social network, showing that both MOEA/D and NSGA II outperform the single objective and the greedy approaches. More interestingly, we show a clear correlation between the algorithms' performance and the diffusion features of the social networks.

title: Viral Marketing and How to Make a Viral Bomb in the Digital Space to Invest the Pandemic COVID 19
abstract: Viral marketing is one of the contemporary strategies and it is one of the most important strategies that organizations must rely on in marketing their products and services. Viral marketing is important in times of crisis for the organization to maintain its continuity in providing its products and services, and viral marketing depends on the strength of social relations between the organization and its customers. The higher the levels of trust by the public towards the organization, the more successful it will be in viral marketing. Progress in the field of information technology and the disappearance of electronic trade restrictions have contributed to the expansion of viral marketing.

title: Viral Marketing and Social Networks

title: Scalable influence maximization for prevalent viral marketing in large scale social networks
abstract: Influence maximization, defined by Kempe, Kleinberg, and Tardos (2003) is the problem of finding a small set of seed nodes in a social network that maximizes the spread of influence under certain influence cascade models. The scalability of influence maximization is a key factor for enabling prevalent viral marketing in large scale online social networks. Prior solutions, such as the greedy algorithm of Kempe et al. (2003) and its improvements are slow and not scalable, while other heuristic algorithms do not provide consistently good performance on influence spreads. In this paper, we design a new heuristic algorithm that is easily scalable to millions of nodes and edges in our experiments. Our algorithm has a simple tunable parameter for users to control the balance between the running time and the influence spread of the algorithm. Our results from extensive simulations on several real world and synthetic networks demonstrate that our algorithm is currently the best scalable solution to the influence maximization problem: (a) our algorithm scales beyond million sized graphs where the greedy algorithm becomes infeasible, and (b) in all size ranges, our algorithm performs consistently well in influence spread it is always among the best algorithms, and in most cases it significantly outperforms all other scalable heuristics to as much as 100% 260% increase in influence spread.

title: A Novel Scene of Viral Marketing for Complementary Products
abstract: Viral marketing, the method of using a small set of users in social networks to propagate products through cascades, is a well known and extreme research problem in recent years. Then, influence maximization (IM) is formulated, which aims to select the most influential seeds to maximize the expected total adoption eventually. IM expresses viral marketing perfectly. However, almost all prior work focused on cardinality constraint or considers only simple comparative products model. They neglected that composite complementary products (CCP) are widespread. In other words, when a customer adopts products <inline formula> <tex math notation="LaTeX"$A$ /tex math>/inline formula> and <inline formula> <tex math notation="LaTeX"$B$ /tex math>/inline formula> at the same time, it is possible for him to adopt product <inline formula> <tex math notation="LaTeX"$C$ /tex math>/inline formula> Therefore, we design a multi layer network model under independent cascade (IC) model to adapt to multiple complementary products and define the seed selection problem for complementary products model [IM for complementary products (IMCP) and CCP model (IMCCP) under knapsack constraint. Here, the seed for different products has a different cost. In this paper, we propose two efficient techniques to solve IMCP problem, called Greedy and general TIM. The Greedy uses simple Greedy Hill Climbing algorithm under knapsack constraint and obtain <inline formula> <tex math notation="LaTeX"{1}{2}(1 {1}{e} /tex math>/inline formula> approximation, but the time complexity is hard to accept. The second algorithm, general TIM, forms a weighted set cover problem by means of randomized sampling (close to Greedy in practice) which reduces the time consuming significantly. For IMCCP problem, it is difficult to handle because no ready made algorithms exist to optimize a function that is nonsubmodular and nonsupermodular. Then, we need to get help from sandwich method by finding an upper and lower bound. Finally, our algorithms are evaluated on several real data sets, which prove the correctness of our algorithms.

title: A Combination of Self Reported Data and Social Related Neural Measures Forecasts Viral Marketing Success on Social Media
abstract: Consumers often share product related content (e.g. advertising) and highly shared advertising has a huge impact on consumer behavior. Despite its apparent effectiveness, prediction of whether such advertising will be highly shared is a poorly understood area of marketing. Advances in brain imaging techniques may allow researchers to forecast aggregate consumer behavior beyond subjective reports. Using neuroimaging techniques, previous research has established models showing that expectations of self related outcomes (potential for self enhancement) and the social impact of sharing (potential for social approval) contribute to the likelihood of users sharing non commercial static content (i.e. text based health news) However, whether this finding can be applied to forecasting the virality of dynamic commercial stimuli, which is more relevant to interactive marketing (i.e. video ads) remains unknown. Combining brain imaging techniques, cross validation methods, and real world data regarding sharing on social media, the present study investigated whether brain data can be used to forecast the viral marketing success of video ads. We used neuroimaging (functional magnetic resonance imaging: fMRI) to measure neural activity during three sets of theory driven neural measures implicated in value, self, and social (mentalizing) processes while 40 participants viewed video ads that brands had posted on Facebook. Contrary to previous findings regarding value related virality in non commercial static content, our results indicate that social related neural activity contributes significantly to forecasting the virality of dynamic marketing related content. The model that included both social related neural measures and subjective intentions to share forecasted viral marketing success better than the model that included only social related neural measures. The model that included only subjective intention to share did not forecast viral marketing success. Overall, these findings provide a novel connection between neurophysiological measures and real world dynamic commercial content. Contrary to previous neuroforecasting findings, social related but not value related neural measures can significantly improve our ability to predict market level sharing of video ads.

title: Celebrity fashion brand endorsement in Facebook viral marketing and social commerce
abstract: PurposeIn light of Facebook based viral marketing and social commerce, the purpose of this paper is to test the moderating role of social identification with the Facebook profile owner (celebrity as aspirational reference group vs ingroup member (college student, same school) vs outgroup member (college student, different school) in Facebook based fashion brand marketing and management. The sample was composed of college students recruited from a subject pool in a US university.FindingsResults of multiple regression analyses indicate that social identification with the Facebook profile owner and ingroup vs outgroup perception moderate the influence of consumers' materialism, fashion involvement and opinion leadership on interpersonal attraction to the celebrity, wishful identification with the celebrity, emotional quotient, involvement with the ads, advertising believability and willingness to buy the advertised fashion products. Furthermore, the results of structural equation modeling analyses show that source credibility perception (both the celebrity who endorses her own brand and the Facebook profile owner) mediates the relationship between experimental conditions (celebrity as aspirational outgroup vs same school student as an ingroup member vs different school student as an outgroup member) and the outcome variables (interpersonal attraction to the celebrity, involvement with ads, and advertising believability) in viral marketing leveraging a social media platform.valueThis study makes several theoretical contributions to consumer psychology and provides managerial implications for Facebook based fashion marketing and fashion brand management.

title: Stop and Stare: Optimal Sampling Algorithms for Viral Marketing in Billion scale Networks
abstract: Influence Maximization (IM) that seeks a small set of key users who spread the influence widely into the network, is a core problem in multiple domains. It finds applications in viral marketing, epidemic control, and assessing cascading failures within complex systems. Despite the huge amount of effort, IM in billion scale networks such as Facebook, Twitter, and World Wide Web has not been satisfactorily solved. Even the state of the art methods such as TIM+ and IMM may take days on those networks. In this paper, we propose SSA and D SSA, two novel sampling frameworks for IM based viral marketing problems. SSA and D SSA are up to 1200 times faster than the SIGMOD'15 best method, IMM, while providing the same (1 1/e e) approximation guarantee. Underlying our frameworks is an innovative Stop and Stare strategy in which they stop at exponential check points to verify (stare) if there is adequate statistical evidence on the solution quality. Theoretically, we prove that SSA and D SSA are the first approximation algorithms that use (asymptotically) minimum numbers of samples, meeting strict theoretical thresholds characterized for IM. The absolute superiority of SSA and D SSA are confirmed through extensive experiments on real network data for IM and another topic aware viral marketing problem, named TVM.

title: A Heuristic Mixed Model for Viral Marketing Cost Minimization in Social Networks
abstract: The Influence maximization (IM) aims at estimating a small number of influential users that maximize the viral marketing profit whereas, the Reverse Influence Maximization (RIM) deals with the minimization of Viral Marketing (VM) cost in social networks. Here, the VM cost is measured by the minimum number of nodes that are required to activate seed nodes and the profit is defined by the maximum number of nodes influenced by seed users when they are initially activated. However, most of the existing works focus on the profit maximization without considering the VM cost. Thus, in this research, we introduce a Viral Marketing Cost (VMC) Minimization problem and propose a Heuristic Mixed (HM) approach under mixed Reverse Independent Cascade (RIC) and Reverse Linear Threshold (RLT) diffusion models. The proposed HM model employs the greedy technique along with a heuristic approach to optimize the VM cost. Moreover, our model resolves the challenging issues of the RIM problem more efficiently. Finally, we simulate our model using data sets of two real social networks, and the result shows that our model outperforms the baseline and existing models.

title: Interrelationships between Viral Marketing and Purchase Intention via Customer Based Brand Equity
abstract: The advent of the Internet has presented several challenges as well as opportunities for marketers. Marketers have found more innovative methods of communicating effectively with their target market, and viral marketing is one of these methods. This study tries to explore interrelationships between viral marketing and purchase intention via customer based brand equity. The empirical results reveal that viral marketing is made up of interesting, useful and informative subjects; message interaction; message reception; and offensive content, in which each had differing effects on customer based brand equity and purchase intention. Additionally, we also find that the interaction with viral marketing messages is extremely important in order to positively influence either brand equity or purchase intentions, because mere message reception have detrimental effects on brand equity and purchase intention. Furthermore, by including brand equity as a mediator, the negative effect of message reception on consumer purchase intentions can be transformed into a positive effect.