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

Evan Dufraisse (1) , Célina Treuillier (2) , Armelle Brun (2) , Julien Tourille (1) , Sylvain Castagnos (2) , Adrian Popescu (1)

1 LASTI - Laboratoire Analyse Sémantique Textes et Images
2 BIRD - Building artificial Intelligence between trust, Responsibility and Decision

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..

Keywords: Political polarization; News processing; Recommender systems; Opinion bubbles

Domains: Computation and Language [cs.CL]

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⟩