Social networks are a platform for individuals and organizations to connect with each other and inform, advertise, spread ideas, and ultimately influence opinions. These platforms have been known to propel misinformation. We argue that this could be compounded by the recommender algorithms that these platforms use to suggest items potentially of interest to their users, given the known biases and filter bubbles issues affecting recommender systems. While much has been studied about misinformation on social networks, the potential exacerbation that could result from recommender algorithms in this environment is in its infancy. In this manuscript, we present the result of an in-depth analysis conducted on two datasets (Politifact FakeNewsNet dataset and HealthStory FakeHealth dataset) in order to deepen our understanding of the interconnection between recommender algorithms and misinformation spread on Twitter. In particular, we explore the degree to which well-known recommendation algorithms are prone to be impacted by misinformation. Via simulation, we also study misinformation diffusion on social networks, as triggered by suggestions produced by these recommendation algorithms. Outcomes from this work evidence that misinformation does not equally affect all recommendation algorithms. Popularity-based and network-based recommender algorithms contribute the most to misinformation diffusion. Users who are known to be superspreaders are known to directly impact algorithmic performance and misinformation spread in specific scenarios. Findings emerging from our exploration result in a number of implications for researchers and practitioners to consider when designing and deploying recommender algorithms in social networks.

Recommender systems play an essential role in our digital society as they suggest products to purchase, restaurants to visit, and even resources to support education. Recommender systems based on collaborative filtering are the most popular among the ones used in e-commerce platforms to improve user experience. Given the collaborative environment, these recommenders are more vulnerable to shilling attacks, i.e., malicious users creating fake profiles to provide fraudulent reviews, which are deliberately written to sound authentic and aim to manipulate the recommender system to promote or demote target products or simply to sabotage the system. Therefore, understanding the effects of shilling attacks and the robustness of recommender systems have gained massive attention. However, empirical analysis thus far has assessed the robustness of recommender systems via simulated attacks, and there is a lack of evidence on what is the impact of fraudulent reviews in a real-world setting. In this paper, we present the results of an extensive analysis conducted on multiple real-world datasets from different domains to quantify the effect of shilling attacks on recommender systems. We focus on the performance of various well-known collaborative filtering-based algorithms and their robustness to different types of users. Trends emerging from our analysis unveil that, in the presence of spammers, recommender systems are not uniformly robust for all types of benign users.