In a series of our prior works, we studyinfluenceandsubscriptionmaximization problems in social networks that are based on posts having influentialcontent; as content we consider a set offeatureswhere each feature corresponds to a specificsocial network page, whereas influence and subscription relate to gaining thepostlikeandsubscription-to-brand pageof targeted users, respectively; subscription is conceptually achieved asrepetitive influenceon users. So, both influence and subscription depend on content that gains the likes of users; however, to be realistic, modeling and estimating such likes is acomplex problemthat has not been adequately studied via a computational way. In this article, we propose anovel perspectiveon the mentioned content-aware research that has the potential to really understand theuser likesto social network posts. By the term$\mathsf{feelit}$system, we refer to the combined result of this proposal with our previous content-aware research, so as to estimate theuser likesin a much more analytical way than before. We providerealistic examplesto clarify the operation of$\mathsf{feelit}$, and we discuss a number oftechnical challengesassociated with it. Our contributions are beneficial to several formulations ofinfluence maximizationin the literature since$\mathsf{feelit}$provides an accurate and advanced way for brands to estimate theuser likesto their posts.

How can we adapt the composition of a post over a series of rounds to make it more appealing in a social network? Techniques that progressively learn how to make a fixed post more influential over rounds have been studied in the context of the Influence Maximization (IM) problem, which seeks a set of seed users that maximize a post’s influence. However, there is no work on progressively learning how a post’s features affect its influence. In this article, we propose and study the problem of Adaptive Content-Aware Influence Maximization (ACAIM), which calls to find k features to form a post in each round so as to maximize the cumulative influence of those posts over all rounds. We solve ACAIM by applying, for the first time, an Online Learning to Rank (OLR) framework for IM purposes. We introduce the CATRID propagation model , which expresses how posts disseminate in a social network using click probabilities and post visibility criteria and develop a simulator that runs CATRID via a training-testing scheme based on real posts of the VK social network, so as to realistically represent the learning environment. We deploy three learners that solve ACAIM in an online (real-time) manner. We experimentally prove the practical suitability of our solutions via exhaustive experiments on multiple brands (operating as different case studies ) and several VK datasets; the best learner is evaluated on 45 separate case studies yielding convincing results.

How can we recommend content for a brand agent to use over a series of rounds so as to gain new subscribers to its social network page? The Influence Maximization (IM) problem seeks a set of~k users, and its content-aware variants seek a set of~k post features, that achieve, in both cases, an objective of expected influence in a social network. However, apart from raw influence, it is also relevant to study gain in subscribers, as long-term success rests on the subscribers of a brand page; classic IM may select~k users from the subscriber set, and content-aware IM starts the post's propagation from that subscriber set. In this paper, we propose a novel content recommendation policy to a brand agent for Gaining Subscribers by Messaging (GSM) over many rounds. In each round, the brand agent messages a fixed number of social network users and invites them to visit the brand page aiming to gain their subscription, while its most recently published content consists of features that intensely attract the preferences of the invited users. To solve GSM, we find, in each round, which content features to publish and which users to notify aiming to maximize the cumulative subscription gain over all rounds. We deploy three GSM solvers, named \sR, \sSC, and \sSU, and we experimentally evaluate their performance based on VKontakte (VK) posts by considering different user sets and feature sets. Our experimental results show that \sSU provides the best solution, as it is significantly more efficient than \sSC with a minor loss of efficacy and clearly more efficacious than \sR with competitive efficiency.

How do we create content that will become viral in a whole network after we share it with friends or followers' Significant research activity has been dedicated to the problem of strategically selecting a seed set of initial adopters so as to maximize a meme's spread in a network. This line of work assumes that the success of such a campaign depends solely on the choice of a tunable seed set of adopters, while the way users perceive the propagated meme is fixed. Yet, in many real-world settings, the opposite holds: a meme's propagation depends on users' perceptions of its tunable characteristics, while the set of initiators is fixed.