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.