Ranking Social Media News Feeds: A Comparative Study of Personalized and Non-personalized Prediction Models

Belkacem, Sami; Boukhalfa, Kamel; Boussaïd, Omar

doi:10.1007/978-3-030-96311-8_19

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…They developed a novel approach using a hesitant fuzzy set (HFS) and a sentiment work framework to calculate overall performance. Belkacem et al [80] rated social media news feeds using the Random Forest (RF) algorithm, providing personalized and non-personalized prediction models. They tested the model on Twitter data to forecast news feed relevance.…”

Section: Machine Learning Applications In Cultural Analyticsmentioning

confidence: 99%

Aesthetic Experience and Popularity Ratings for Controversial and Non-Controversial Artworks Using Machine Learning Ranking

Vlachou,

Panagopoulos

2023

Applied Sciences

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Currently, a substantial portion of images snapped at exhibitions and galleries on social media demonstrates that aesthetic experience is not restricted to the confines of cultural institutions. The primary objective of this paper is to examine whether the content or aspect of an artwork influences the aesthetic experience of the viewer and to measure the artwork’s social media popularity. To compare controversial works of art with those whose design, qualities, or intended message are non-controversial, we first sought out controversial works. A variety of artworks were revealed on Instagram; thus, the objective was to identify a non-controversial artwork published in the same year as each controversial artwork. We adhered to the complete procedure for cleansing, standardizing, and transforming the data to ensure comparability. Popularity was measured using a ranking algorithm and quantitative approaches for the recognition and statistical measurement of emotions. In addition, the exhaustive literature survey on models of aesthetic experience revealed no link between the experience of art and its social media popularity. Considering this, we have proposed, among other things, a new framework for interacting with art that integrates these parameters. According to the findings, controversial artworks elicited stronger emotions than non-controversial artworks. Furthermore, investigations have determined the three most popular works of art in each category. Under the scrutiny of social media, these results may inspire future research on the popularity of museum artworks and the design of aesthetic experiences.

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Section: Machine Learning Applications In Cultural Analyticsmentioning

confidence: 99%