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Register a new userImplementing Evaluation Metrics Based on Theories of Democracy in News Comment Recommendation (Hackathon Report)
تنفيذ مقاييس التقييم بناء على نظريات الديمقراطية في توصية تعليق الأخبار (تقرير هاوتاثون)
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التنوع في توصية الأخبار مهم للنقاش الديمقراطي.لا تركز استراتيجيات التوصية الحالية، وكذلك مقاييس التقييم لأنظمة التوصية، بشكل صريح على هذا الجانب من توصية الأخبار.في مجموعة 2021، قامنا بتنفيذ رواية واحدة، وتنشيط التقييم المعياري على الرواية، والتنشيط "، واستخدامه"، واستخدامه لمقارنة استراتيجيات توصية لتعليقات نيويورك تايمز، واحدة تستند إلى إعجاب المستخدم وآخر على المحرر اللقطات.وجدنا أن استراتيجيات توصية التعليق تؤدي إلى توصيات أقل باستمرار تفعيل التعليقات المتاحة في مجموعة البيانات، ولكن يختار المحرر أكثر من ذلك.قد يشير هذا إلى أن محرري نيويورك تايمز يدعمون نموذج ديمقراطي تداول، حيث يعتبر تنشيط أقل مثالية للنقاش الديمقراطي.
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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We conduct automatic sentiment and viewpoint analysis of the newly created Slovenian news corpus containing articles related to the topic of LGBTIQ+ by employing the state-of-the-art news sentiment classifier and a system for semantic change detectio
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