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Implementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)

Diversity in news recommendation is important for democratic debate. Current recommendation strategies, as well as evaluation metrics for recommender systems, do not explicitly focus on this aspect of news recommendation. In the 2021 Embeddia Hackathon, we implemented one novel, normative theory-based evaluation metric, “activation”, and use it to compare two recommendation strategies of New York Times comments, one based on user likes and another on editor picks. We found that both comment recommendation strategies lead to recommendations consistently less activating than the available comments in the pool of data, but the editor’s picks more so. This might indicate that New York Times editors’ support a deliberative democratic model, in which less activation is deemed ideal for democratic debate.

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