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Communication Dans Un Congrès Année : 2022

Don't burst blindly: for a better use of natural language processing to fight opinion bubbles in news recommendations

Résumé

Online news consumption plays an important role in shaping the political opinions of citizens. The news is often served by recommendation algorithms, which adapt content to users’ preferences. Such algorithms can lead to political polarization as the societal effects of the recommended content and recommendation design are disregarded. We posit that biases appear, at least in part, due to a weak entanglement between natural language processing and recommender systems, both processes yet at work in the diffusion and personalization of online information. We assume that both diversity and acceptability of recommended content would benefit from such a synergy. We discuss the limitations of current approaches as well as promising leads of opinion-mining integration for the political news recommendation process..
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Dates et versions

cea-04562587 , version 1 (29-04-2024)

Licence

Paternité - Pas d'utilisation commerciale

Identifiants

  • HAL Id : cea-04562587 , version 1

Citer

Evan Dufraisse, Célina Treuillier, Armelle Brun, Julien Tourille, Sylvain Castagnos, et al.. Don't burst blindly: for a better use of natural language processing to fight opinion bubbles in news recommendations. First Workshop on Natural Language Processing for Political Science, Jun 2022, Marseille, France. pp.79-85. ⟨cea-04562587⟩
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