Understanding the Contribution of Recommendation Algorithms on Misinformation Recommendation and Misinformation Dissemination on Social Networks

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