# InfluetNet: Trust-Based Influencer Marketing Campaigns in Online Social Networks
## 1 Introduction
### Contributions  
Though significant research efforts have been devoted to the problem of recruiting influencers to perform online advertisement in OSNs, the problem of enabling the influencers to autonomously choose products from different OSNs to advertise to the corresponding customers (i.e., current and potential future followers) has received little attention in the existing literature. Furthermore, this problem becomes even more complicated when considering the trust levels of the companies and the OSN platforms that request the online advertisement of different products.  
In this paper, aiming at addressing those research challenges, we introduce the INHURUST framework to support trust-based influencer marketing campaigns in OSNs. Specifically, the INHURUST framework enables the influencers to autonomously choose a product from different OSN platforms to advertise to their customers based on a reinforcement learning algorithm that considers the reward provided by the corresponding OSN platform, their achieved profit from advertising the product, and the influencers' trust to the corresponding OSN platform. The influencers' trust consists of their direct and indirect trust, where the first one is built based on the influencers' past interactions with the OSN platforms, while the latter one is formed based on the influencers' interactions among each other and the sharing of their experiences by selecting different OSN platforms. Furthermore, the influencers receive rewards by the OSN platforms based on a multilateral bargaining model, where the influencers compete among each other for the corresponding monetary rewards to advertise the same product. The main contributions of this research work are summarized as follows.  
1. Initially, the INHURUST framework is presented considering multiple influencers and multiple OSN platforms that request the advertisement of different products. The concept of product quality, as it is evaluated by the influencers, is presented, and the influencers' profit by contributing to the online advertisement product campaign is quantified.
2. A novel trust model for the influencers is introduced quantifying the influencers' trust in the OSN platforms and the corresponding products that they advertise. The influencers' trust model consists of direct and indirect trust. Direct trust is quantified based on the influencers' direct interactions with the OSN platforms, while indirect trust is shaped based on the influencers' interactions with each other, their social ties, and their past experience from different OSN platforms.
3. A Stochastic Learning Automata reinforcement learning algorithm is introduced to enable the influencers to autonomously select an OSN platform and a corresponding product to advertise, considering the monetary reward received by the OSN platform, their achieved profit by advertising the product, and the OSN platforms' trust levels.
4. Focusing on the OSN platforms, the monetary rewards are allocated to the influencers, who choose to advertise a product through the corresponding OSN platform, by following a multilateral bargaining model. Specifically, the influencers compete with each other for the OSN platforms' monetary rewards while advertising the same product through the same OSN platform.
5. A detailed set of simulation-based results are provided based on real datasets in order to demonstrate the operation and efficiency of the INHURUST framework under different realistic scenarios. Also, a scalability analysis is performed to highlight the efficiency and robustness of the INHURUST framework under large-scale setups. A thorough comparative evaluation of the INHURUST framework, compared to models that ignore the OSN platforms' trust levels during the product selection for advertisement by the influencers and/or allocate the OSN platforms' rewards to the influencers following the principles of proportional fairness, is presented to quantify the benefits of the INFLUTRUST framework both from the influencers' and the OSN platforms' perspectives.