Workshop on Online Misinformation- and Harm-Aware Recommender Systems

Social media platforms have become an integral part of everyday life and activities of most people, providing new forms of communication and interaction. One of the most valuable features of social platforms is the potential for the dissemination of information on a large scale. Recommender systems play an important role in this process as they leverage on the massive user-generated content to assist users in finding relevant information as well as establishing new social relationships. As mediators of online information consumption, recommender systems are affected by the proliferation of low-quality content in social media, and, at the same time, become unintended means for the amplification and massive distribution of online harm. Some of these issues stem from the core concepts and assumptions recommender systems are based on. In their attempt to deliver relevant and engaging suggestions about content/users, recommendation algorithms are prone to introduce biases. Harnessing recommender systems with misinformation- and harm-awareness mechanisms become essential not only to mitigate the negative effects of the diffusion of unwanted content, but also to increase the user-perceived quality of recommender systems. Novel strategies like the diversification of recommendations, bias mitigation, model-level disruption, explainability and interpretation, among others, can help users in performing informed decision making in the context of online misinformation, hate speech and other forms of online harm.

  • Antonela Tommasel, ISISTAN, CONICET-UNICEN,
  • Arkaitz Zubiaga, Queen Mary University of London


Friday afternoon, Sept 25, 2020