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Policy makers and researchers have thus called for increased transparency on how algorithms influence exposure to political content on the platform. Based on a massive-scale experiment involving millions of Twitter users, a fine-grained analysis of political parties in seven countries, and 6.2 million news articles shared in the United States, this study carries out the most comprehensive audit of an algorithmic recommender system and its effects on political content. Results unveil that the political right enjoys higher amplification compared to the political left.<\/jats:p>","DOI":"10.1073\/pnas.2025334119","type":"journal-article","created":{"date-parts":[[2021,12,21]],"date-time":"2021-12-21T17:50:14Z","timestamp":1640109014000},"update-policy":"https:\/\/doi.org\/10.1073\/pnas.cm10313","source":"Crossref","is-referenced-by-count":249,"title":["Algorithmic amplification of politics on Twitter"],"prefix":"10.1073","volume":"119","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4988-1430","authenticated-orcid":false,"given":"Ferenc","family":"Husz\u00e1r","sequence":"first","affiliation":[{"name":"Machine Learning Ethics, Transparency, and Accountability Team, Twitter, San Francisco, CA 94103;"},{"name":"Department of Computer Science and Technology, University of Cambridge, Cambridge CB3 0FD, United Kingdom;"},{"name":"Gatsby Computational Neuroscience Unit, University College London, London, W1T 4JG, United Kingdom;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sofia Ira","family":"Ktena","sequence":"additional","affiliation":[{"name":"Machine Learning Ethics, Transparency, and Accountability Team, Twitter, San Francisco, CA 94103;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5290-7788","authenticated-orcid":false,"given":"Conor","family":"O\u2019Brien","sequence":"additional","affiliation":[{"name":"Machine Learning Ethics, Transparency, and Accountability Team, Twitter, San Francisco, CA 94103;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2749-0586","authenticated-orcid":false,"given":"Luca","family":"Belli","sequence":"additional","affiliation":[{"name":"Machine Learning Ethics, Transparency, and Accountability Team, Twitter, San Francisco, CA 94103;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5493-8892","authenticated-orcid":false,"given":"Andrew","family":"Schlaikjer","sequence":"additional","affiliation":[{"name":"Machine Learning Ethics, Transparency, and Accountability Team, Twitter, San Francisco, CA 94103;"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moritz","family":"Hardt","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, CA 94720"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"341","published-online":{"date-parts":[[2021,12,21]]},"reference":[{"key":"e_1_3_4_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.giq.2016.04.007"},{"key":"e_1_3_4_2_2","unstructured":"The Economist Twitter\u2019s algorithm does not seem to silence conservatives. 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