Facebook ‘Trending’ List Skewed by Individual Judgment, Not Institutional Bias
Facebook ‘Trending’ List Skewed by Individual Judgment, Not Institutional Bias
5/29/2016
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summary
This New York Times article explores the controversy surrounding Facebook's Trending Topics feature and the accusations of bias in its content selection process. It discusses how Facebook uses a team of human editors to curate the trending news stories, and how this introduces potential bias into the algorithmic system. The article highlights a specific incident where conservative news topics were allegedly suppressed by the editors. Facebook faced criticism for lack of transparency and accountability in their content curation process. The article also discusses the challenges of addressing bias in algorithms and the implications for the role of social media in shaping public opinion.
tags
facebook ꞏ social media ꞏ news ꞏ trending ꞏ algorithmic bias ꞏ institutional bias ꞏ confirmation bias ꞏ filter bubble ꞏ information ecosystem ꞏ online platforms ꞏ media manipulation ꞏ news curation ꞏ technology ꞏ media ethics ꞏ media bias ꞏ online news ꞏ journalism ꞏ media influence ꞏ media literacy ꞏ media consumption ꞏ media credibility ꞏ online content ꞏ internet culture ꞏ social influence ꞏ digital manipulation ꞏ information dissemination ꞏ online discourse ꞏ news consumption ꞏ online communities ꞏ media transparency ꞏ information integrity ꞏ media objectivity ꞏ social networking ꞏ media accountability ꞏ media regulation ꞏ online algorithms ꞏ content curation ꞏ online influence ꞏ informational bias ꞏ media trustworthiness ꞏ data-driven decisions ꞏ information bias ꞏ technological influence