OHARS: Workshop on Online Misinformation- and Harm-Aware Recommender Systems

Social media platforms have become an integral part of most people’s everyday life and activities, providing new forms of communication and interaction. One of the most valuable features of social platforms is the potential for disseminating information on a large scale. Recommender systems play an essential role in this process as they leverage the massive user-generated content to assist users in finding relevant information and establishing new social relationships. As mediators of online information consumption, recommender systems are affected by the proliferation of low-quality content and, at the same time, become unintended means for the amplification and massive distribution of online harm. While these phenomena are widely observed in social media, they affect users’ experience on multiple online platforms, such as collaborative filtering systems in e-commerce sites, news media, video platforms, or opinion mining applications. Some of these issues stem from the core concepts and assumptions on which recommender systems are based. In their attempt to deliver relevant and engaging suggestions about content/users, recommendation algorithms can introduce biases. Harnessing recommender systems with misinformation- and harm-awareness mechanisms become essential to mitigate the negative effects of the diffusion of unwanted content and increase the user-perceived quality of recommender systems. Novel strategies like the diversification of recommendations, bias mitigation, model-level disruption, explainability, and interpretation can help users perform informed decision-making in the context of online misinformation, hate speech, and other forms of online harm.

  • Antonela Tommasel, ISISTAN, CONICET-UNICEN, Argentina
  • Daniela Godoy, ISISTAN, CONICET-UNICEN, Argentina
  • Arkaitz Zubiaga, Queen Mary University of London, United Kingdom



Half day.

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