A frequently studied problem in the context of digital marketing for online social networks is the influence maximization problem that seeks for an initial seed set of influencers that trigger an information propagation cascade (in terms of message-forwarding) of expected maximum impact. The studied problems typically neglect that the probability that individuals only view content without forwarding it is much higher than the probability that they forward content. We argue that more natural objectives include maximizing: (a) the expected organic reach, (b) the expected number of total impressions, or, (c) the expected patronage of the influence spreading entity. We propose mathematical models to maximize these objectives whereby the model for variant (c) includes individual's resistances and uses a multinomial logit model to model customer behavior. These models are also geared to a competitive setting in which the seed set of a competitor is known and contain the variants for a single influence spreading entity as special cases. In a computational study based on network graphs from Twitter (and from the literature) we show that one can increase the expected organic reach and number of total impressions by 25% on average (and up to 500% in particular cases) compared to seed sets obtained from the classical maximization of message-forwards. Further insights such as the impact of individual user preferences, and performance analysis of our algorithms are given.
Kahr, Michael, Leitner, Markus, and Ljubić, Ivana (2022). The impact of passive social media users in (competitive) influence maximization. Technical report. University of Vienna, Austria.