Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations
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.
Code (0)
등록된 구현이 없습니다.
Tasks
DiversityNews RecommendationOpinion MiningRecommendation SystemsSimilar Papers 제목 키워드 기반
An Analysis and Implementation of the HDR+ Burst Denoising Method
HDR+ is an image processing pipeline presented by Google in 2016. At its core lies a denoising algorithm that uses a burst of raw images to produce a single higher quality image. Since it is designed as a versatile solut…
DenoisingBurst Image Super-Resolution with Mamba
Burst image super-resolution (BISR) aims to enhance the resolution of a keyframe by leveraging information from multiple low-resolution images captured in quick succession. In the deep learning era, BISR methods have evo…
Burst Image Super-ResolutionComputational EfficiencyImage Super-ResolutionMamba+2Identifying trace alternant activity in neonatal EEG using an inter-burst detection approach
Electroencephalography (EEG) is an important clinical tool for reviewing sleep-wake cycling in neonates in intensive care. Trace alternant (TA)-a characteristic pattern of EEG activity during quiet sleep in term neonates…
EEGElectroencephalogram (EEG)NAN: Noise-Aware NeRFs for Burst-Denoising
Burst denoising is now more relevant than ever, as computational photography helps overcome sensitivity issues inherent in mobile phones and small cameras. A major challenge in burst-denoising is in coping with pixel mis…
DenoisingGated Multi-Resolution Transfer Network for Burst Restoration and Enhancement
Burst image processing is becoming increasingly popular in recent years. However, it is a challenging task since individual burst images undergo multiple degradations and often have mutual misalignments resulting in ghos…
DenoisingSuper-Resolution