Year: 2023
Type: preprint
Abstract: Combining crowd-sourced data donation and a largescale server-side data collection, we provide quantitative experimental evidence of Twitter recommender distortion of users' environment reality.Twitter's algorithmically curated home feed amplifies toxic and sentimentally valenced tweets, distorts the political landscape perceived by the users, and favors small and/or usually quiet accounts.We argue the need of independent audits of social media platforms with access to large-scale data.
Source: HAL
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4360803336